Final Book

Kuwait University College of Engineering and Petroleum Department of Industrial Engineering IE 496: Industrial Engineering Design Fall 2008

Leader: Hamid Al-Yousufi Vice Leader: Alaa’ Aboelfotoh

Abrar Hajiya Aisha Al-Roomi Amal Al-Fouzan Basel Nijem Elaf Ashkanani Farah Al-Doussery Maryam Al-Qatami Moneera Al-Fayyad Moudi Al-Abassi Nouf Al-Fraih Shaikha Al-Dabbous Shaima'a Dehrab Sherifa Al-Fulaij Zahra'a Amir

Supervised by: Prof. Mehmet Savsar . Eng. Bedour Al-Saleh Page | 1

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Acknowledgements

As I look at this book, finally printed and looking professional, I cannot help but remember all the pain and misery that went into putting it all together. I would like to thank and congratulate all my team members for their excellent contributions to this project and a special thank you to Basel Nijem, who put up with my obsession of having everything as perfect as possible. His tireless work in formatting this report made it a reality. However, before writing this book became a remote reality, we had to navigate the many presentations and deadlines set by our supervisor. None of this would have been possible without the amazing dedication of our priceless vice leader, Ala’a Aboelfotoh. For all your hard work, and having to put up with my insanity throughout the semester, thank you. Gratitude is also due to the IMSE faculty at KU, who guided us when we were lost, and kept making ever harder demands for the quality of our work. The staff at the National Canned Food Production and Trading Company deserves the utmost appreciation. They provided us with the data we needed whenever they could, and were friendly and courteous to us during our visits. To the families and friends of each and every member, a heartfelt thank you. Their love and support (and teasing) kept us going when we were down. Finally, I will not use the cliché that we hope you get as much pleasure from reading this book as we got from writing it, because it was a nightmare to write. Hamid Al-Yousufi On behalf of myself and my group, I would like to thank our dear staff for their assistance in making this design project a successful and pleasant one. We are particularly grateful to the great management and staff at the National Canned Food Production and Trading Company for all their assistance in providing us with the material required, and taking time off their work to help us. The coordination of all teams and the preparation of this report would not have been successful without the endless efforts of our leader Hamid Al Yousufi. A special thanks goes to Basel Nijem for his assistance in the editing and formatting of this report. Finally, I would like to thank all members for their hard work and congratulate them on their success. None of this would have been possible without the support of our families and friends to whom we owe much. Alaa Aboelfotoh

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Table of Contents Introduction 1.1 Company Background ..................................................................................... 12 Products ............................................................................................................................14

1.2 General Problem Description .......................................................................... 15 Quality Control 2.1 Introduction ...................................................................................................... 18 Problem Description............................................................................................................19 Objectives ..........................................................................................................................20 Solution Approach ..............................................................................................................20

2.2 Analysis of the As-Is System .......................................................................... 21 The Can Making Line ...........................................................................................................21 The Can Filling Line .............................................................................................................27 Local Lab ............................................................................................................................33 Central Lab .........................................................................................................................35 The As-Is Raw Material Sampling Plans ..............................................................................39 Quality Control Documentation ........................................................................................57

2.4 New Quality Control Documentation .............................................................. 73 2.5 New Sampling Plans ........................................................................................ 79 2.6 Proposed Double Sampling Plans For Beans................................................ 88 2.7 Proposed New Single Sampling Plan for Tin Sheets .................................. 116 2.8 Proposed Double Sampling Plans For Tin Sheets ...................................... 123 2. 9 Conclusion ..................................................................................................... 151 Cost Analysis 3.1 Introduction .................................................................................................... 154 3.1.1 Problem Description.................................................................................................. 155 3.1.2 Objectives ................................................................................................................ 156

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3.1.3 Solution Approach .................................................................................................... 156

3.2 Analysis of As-Is System: ............................................................................. 157 3.2.1 System .................................................................................................................... 157 1. Suppliers .................................................................................................................. 158 2. Customers ................................................................................................................ 161 3. Missions and Goals of The National Canned Food Company ............................................ 161 4. Resources ................................................................................................................. 161 5. Output ...................................................................................................................... 163 6. Outcome................................................................................................................... 163 7. Performance Measures .............................................................................................. 163 8. Decisions The National Canned Food Company Should Consider .................................... 163 3.2.2 Productivity Indices ................................................................................................... 164 1. Direct Cost ................................................................................................................... 164 Direct Labor Costs ......................................................................................................... 165 Direct Material Cost ....................................................................................................... 166 Equipment Direct Cost ................................................................................................... 177 2. Indirect Costs ................................................................................................................ 183 3. Overheads .................................................................................................................... 185 Technical Overheads ...................................................................................................... 185 Company Overheads...................................................................................................... 186 Marketing Overheads .................................................................................................... 186 5. Variable Cost................................................................................................................. 188 6. Fixed Costs ................................................................................................................... 190 7. Total Cost ..................................................................................................................... 190 8. Total Revenue: .............................................................................................................. 191 9. Total Profit ................................................................................................................... 192 10. Productivity Analysis Results ......................................................................................... 195 11. Break Even Point ......................................................................................................... 195

4. New System ...................................................................................................... 198 A. Overfilling: ................................................................................................................... 198 B. Transportation Costs ..................................................................................................... 200

5. Conclusion ........................................................................................................ 212

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Production Line Analysis and System Maintenance 4.1 Introduction .................................................................................................... 216 Problem Statement ........................................................................................................... 218 Objectives ........................................................................................................................ 218 Solution Approach ............................................................................................................ 218

4.2 Part List ........................................................................................................... 219 4.3 Bill of Materials (BOM) ................................................................................... 220 4.4 Component Part Drawing .............................................................................. 221 4.5 Process Description....................................................................................... 223 4.6 Process Flow on the Factory Layout ............................................................ 226 4.7 Operation Process Chart ............................................................................... 227 4.8 Route sheets ................................................................................................... 229 4.9 Data Collection and Fitting ............................................................................ 232 4.10 Maintenance Types ...................................................................................... 234 Corrective Maintenance (CM)............................................................................................. 234 Preventive Maintenance (PM) ............................................................................................ 235

4.11 Maintenance Plan ......................................................................................... 236 Current Maintenance Plan ................................................................................................. 237 Proposed Maintenance Plans ............................................................................................. 239 Alternative 1 ................................................................................................................. 239 Alternative 2 ................................................................................................................. 241 Alternative 3: ................................................................................................................ 244

4.12 The Reliability of the Lines .......................................................................... 247 4.13 Results .......................................................................................................... 251 Can Making Line ............................................................................................................... 251

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Can Filling Line.................................................................................................................. 252

4.14 Availability of the Machines ........................................................................ 254 Inherent Availability (Ai): ................................................................................................ 254 Achieved Availability (Aa) ................................................................................................ 255 Operational Availability (Ao) ............................................................................................ 255

4.15 Spare Parts ................................................................................................... 257 4.16 System Simulation ....................................................................................... 260 Problem Formulation ........................................................................................................ 262 System entities.............................................................................................................. 262 Material handling system ............................................................................................... 263 Current Problems in the Layout....................................................................................... 264 Work Schedule .............................................................................................................. 264 Scrap Estimate .............................................................................................................. 265 Policies ......................................................................................................................... 265 Simplification Assumptions ............................................................................................. 266 Coding the Arena Model of the As-Is System ........................................................................ 267 Explanation of the As-is Model of the Can Making Line ...................................................... 268 Can Filling line .................................................................................................................. 269 Explanation of the As-is Model of the Can Filling Line ........................................................ 271 Verification and Validation ................................................................................................. 273 Can Making Line ............................................................................................................ 273

4.17 Analysis of Daily Production Runs and Improvement .............................. 281 Can Making Line ............................................................................................................ 281 Can Filling Line .............................................................................................................. 287

4.18 Summary of the Proposed Alternatives ..................................................... 296 4.18 Conclusion .................................................................................................... 297 Inventory Management and Production Planning 5.1 Introduction .................................................................................................... 300 Problem description ......................................................................................................... 301 Solution approach ............................................................................................................. 302

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Methodology.................................................................................................................... 302

5.2 Analysis .......................................................................................................... 302 1- Demand forecasting ...................................................................................................... 303 2- Holt’s method ........................................................................................................... 304 Five Year Forecasts............................................................................................................ 385 Economic Order Quantity (EOQ) for Production Planning ...................................................... 399 Economic Production Quantity (EPQ) for Production Planning ............................................... 405 Service Level .................................................................................................................... 412

5.3 Conclusion ...................................................................................................... 418 Supply Chain Management 6.1 Introduction .................................................................................................... 420 Warehouses' Locations .................................................................................................. 424 Distribution Network ....................................................................................................... 425 Current Average Demand and Costs .................................................................................... 428 Problem Statement ........................................................................................................... 429 Solution Approach ............................................................................................................ 430

6.2 Analysis and Studies ..................................................................................... 430 Study 1: Establishing a New Factory .............................................................................. 432 Study 2: Using New Trucks ............................................................................................ 437 Justifications for Study 1 and Study 2 ............................................................................. 442 Study 3: Increasing Capacity of Existing Factory ............................................................ 445 Study 4: Demand Increase ............................................................................................. 450

6.3 Conclusion ...................................................................................................... 455 Safety and Human Factors 7.1 Introduction .................................................................................................... 458 Problem Description.......................................................................................................... 459 Objectives ........................................................................................................................ 460 Solution Approach ............................................................................................................ 460

7.2 Safety and Human Factors ............................................................................ 461

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7.3 Hazard Categories .......................................................................................... 463 7.4 Worker interaction with machine and material ............................................ 465 7.5 Data Collection and Findings ........................................................................ 466 7.6 Quick-win Improvements ............................................................................... 473 7.7 Long-term Improvement ................................................................................ 474 7.6 Management Control ...................................................................................... 487 7.7 Conclusion ...................................................................................................... 491 Facilities Planning 8.1 Introduction .................................................................................................... 494 Problem Statment ............................................................................................................. 495 Objectives ........................................................................................................................ 496 Solution Approach ............................................................................................................ 496

8.2 Current Layout................................................................................................ 497 Departments ................................................................................................................... 497 Blue Print of Factory ....................................................................................................... 505 As-Is Layout ................................................................................................................... 506

8.3 Material Handling ........................................................................................... 512 8.4 Method 1: Relationship Diagramming (RDM) Method ................................. 520 8.5 Method 2: CRAFT ........................................................................................... 535 8.6 Comparison of Method 1 and Method 2: Massaged Layouts ..................... 538 8.7 Proposed Layout ............................................................................................ 545 8.8 Savings in Cost .............................................................................................. 546 8.9 Conclusion ...................................................................................................... 548 General Conclusion ............................................................................................. 550

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1. Introduction

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1.1 Company Background

The National Canned Food Production and Trading Co. was founded in 1985 as Kuwait’s only producer of canned and processed food, under the DANIAH brand name and other local private labels, with a capital of 2,000,000 KD. Today it employs over 100 people. It is a subsidiary of Mezzan Holding Co.

Figure 1.1: Mother company and subsidaries.

The company’s objectives are to produce high quality canned food with a minimum number of defects on time to achieve customer and employee satisfaction.

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Products The company produces three main products: 1. Aqua Gulf water. 2. Vinegar. 3. Canned Food (220g, 400g, 450g) 

The Foul Medammes: Foul Medammes American Variety, Foul Medammes with chili, Broad Beans, and Peeled Foul with chili.



Chickpeas: Chickpeas, Giant Garbanzo, with and without chili sauce.



Hommus Tahineh: Hommus Tahineh and Hommus Tahineh with garlic.



Peas: Green Peas, Mixed Vegetables, and Peas & Carrots.



Mushroom: Whole Mushrooms, Mushroom Pieces and Stems.



Olives: Black and Green Olives.



Corn: Whole Kernel Sweet Corn as well as new products such as Baby Corn, and Corn Cream.



Sausages: Frankfurter Sausages, Cocktail Sausages and Beef Sausages.



Beans: Baked Beans in tomato sauce, Black Eye Beans, White Beans, Red Kidney Bean, Red Kidney Beans with chili sauce, Butter Beans.

Figure 1.2: Products offered.

In this study, the production of the 400g cans was focused on since it comprises the bulk of production. In addition, the company produces its own cans. The factory has a separate line for Vinegar with which it produces White, Brown and Apple Vinegar. The factory also trades in Premium Sauces, including Tomato Ketchup, Chili Sauce, Hot Sauce, Extra Hot sauce, and Tomato Paste. Page | 14

1.2 General Problem Description

After thoroughly examining the factory and its operations, numerous, diverse problems were identified. Table 1.1 provides a summary of the problems found. The problems were categorized into an area of study within the Industrial Engineering discipline and teams were formed to study and eradicate each of these problems. Table1 .1: Summary of the problems identified.

General Problem Description

•

Inadequate raw material sampling plans. • Poor quality documentation. • Overfilling of cans during production. •

Unsafe working conditions.

Area of Study

Names Hamid Al-Yousufi

Quality Control

Human Factors and Safety

Shaima’a Dehrab Abrar Hajiya Nouf AL Fraih

•

High overfilling and transportation costs.

Cost Analysis

Amal AL Fouzan Shaikha Al Dabbous Aisha Al-Roumi

• •

Elaf Ashkanani Frequent machine failure. Poor maintenance plans.

Simulation and Maintenance Moudi Al-Abassi Zahra’a Amir Farah Al-Douseri

•

Company cannot meet the demand on time. • No specialized inventory plans in place. • Lead time is relatively long for final product.

Production Planning and Inventory Control

Maryam Al-Qatami Moneera Al-Fayyad Sherifa Al-Fulaij

•

Company at risk of being unable to satisfy demand even with overtime production hours.

Alaa Aboelfotoh Supply Chain Basel Nijem Alaa Aboelfotoh

•

Machines are too crammed, pathways are obstructed, inventory spread throughout the factory and a lot of wasted space.

Facilities Planning

Basel Nijem Nouf Al Fraih

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2. Quality Control

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2.1 Introduction When dealing with the food industry, there are many quality targets that need to be met. These include such things as bacteria count, weight accuracy, etc. The adequacy of the quality control system in place to achieve these targets was considered, by studying each test separately and determining whether action is needed to ensure the targets are being met. If a test returned a lot of negative values, attention was focused on it, to try and eliminate its cause by conducting a root cause analysis. The products being produced were also assessed to determine whether they meet all these targets. Furthermore, the raw material sampling plans in place were evaluated by using such measures as the probability of acceptance, the average outgoing quality, and the average total inspection. If any of the plans were found to be inadequate, new, superior plans were developed.

Problem Description After studying the current system in depth, three distinct problems were identified. First of all, the cans are being consistently over filled. This is a source of waste that will cause the company to lose money unnecessarily. Secondly, the quality management system in place is inadequate as the documentation is very poor and thus requires an immediate overhaul. In addition, there seems to be lack of vigilance in applying quality control, and a disregard for its importance. This could be due to the fact that the company is not aware of the costs involved in poor quality. Last but not least, some of the raw materials sampling plans in place require some modifications in order for them to adequately discriminate between lots of suitable and those of unsuitable quality.

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Objectives 

The quality control documentation system shall be studied and optimized.



It shall be ensured that all the products adhere to all the specifications required. If not, the problems causing a failure to meet these specifications shall be identified and corrected.



New Sampling plans shall be developed that strike a balance between their different properties, such as the probability of acceptance and the costs involved.

Solution Approach In order to rectify the problems discussed previously, and to achieve the objectives set out, a root cause analysis was carried out to eliminate the over filling problem, as well as to bring the cost of overfilling to the attention of the company to educate the company as to the importance of proper quality control,. Furthermore, new quality documentation was developed to maintain a high level of quality control in the future. Finally, statistically reliable raw material sampling plans were proposed.

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2.2 Analysis of the As-Is System The Can Making Line The company produces its own cans. In this section, the can making line processes, as well as the quality control procedures implemented for each, is discussed. 1. Move a sheet metal box from storage to working area. Note: Sheet metal boxes are stored nearby. 2. Open box manually. 3. Transport sheets to cutting machine manually. 4. Sheet is cut according to required size. 5. Ready sheets are transported to electrical welding machine manually. Note: Feed rate: 160 sheets per minute. 6. Can is electrically welded. Note: Copper used to strengthen current. 7. Welded can is transported to lacquering machine by conveyor belt. 8. Varnish is applied to welded section of the can. 9. Can is transported to the oven by belt. 10. Oven heats up glue to allow it to set properly. 11. Random inspection carried out on cans exiting the oven. 12. Can is transported to flanging machine. 13. Can is flanged at both ends. 14. Can is transported towards separator. 15. Distance between consecutive cans is set to a specific amount to complement the speed of the seaming machine. Page | 21

16. Can is transported to seaming machine. 17. Lids fed into seaming machine to coincide with the arrival of the can. Note: Lids are stored and fed manually into the seaming machine. Note: 123,760 lids per box. 18. Lid is attached to the can using double seaming process. Note: Cans arrive upside down to get sealed from below. 19. Can is transported to storage area. Note: Due to the design of the conveyor belt, cans are turned upright during the transportation to the storage area. Note: Since the speed throughout the line is constant, the throughput is 160 cans per minute

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Detailed description of the Can Making Line Cutting Process: Tin sheets are taken from a box similar to the one shown in figure 2.1 which contains 1200-1500 sheets, depending on the supplier, and manually moved to the cutting machine shown in figure 2.2. Each sheet is cut into 32 blanks as seen in figure 2.3, before they are manually arranged into piles on a table next to the welding machine shown in figure 2.4.

Quality: At the start of the production run, the cutting machine blades are checked by producing thirty two blanks (that are used to manufacture the 400g cans) and examining the edges to determine if they are smooth enough. If not, the blades are sharpened. This is a qualitative test.

Figure 2.3: Sheets cut into 32 blanks.

Figure 2.1: A box of tin sheets.

Figure 2.2: The cutting machine.

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Welding Process: As depicted in figure 2.4, the tin blanks are fed manually into the welding machine, where they are bent into a cylindrical shape. Electric currents are induced, and are then strengthened by the presence of thin wires of copper, to weld the two edges of the metal blank. The copper wires only help generate electricity and are not part of the can itself.

Quality: At the start of production, the first four cans are inspected by applying the Pull Test, in which tension is applied to both sides before the can is checked for any tearing. During full production, two cans are taken every two hours and are subjected to the same test.

Figure 2.4: Blanks being fed into welding machine.

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Lacquering Process: The welded cans are moved using a conveyor belt from the welding machine to the lacquering area shown in figure 2.5, where a varnish is applied to both the outside and inside of the can’s welded area.

Quality: The varnish is checked by applying sixty strokes of MEK (a solution similar to paint thinner) to it. No rusting should occur.

Figure 2.5: The Lacquering area.

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Seaming Process: After cans are flanged on both sides, the cans are separated an even distance and enter the seaming machine, where the bottom of the can is sealed using double seaming. As can be seen in figures 2.6 and 2.7, the lids are stored adjacent to the line. Double seaming is used to ensure that no microscopic bacteria can invade its contents.

Quality: After the seaming process, 8 cans are taken every hour, and the following tests are carried out: 

4 cans are manually inspected. If more than 35% of the cover hook consists of wrinkles, the can is scrapped.



4 cans undergo the leak test, which is shown in figure 2.8, where the cans are submerged in water and pressurized at 1.5-2 bar. The tank is then inspected for the presence of bubbles, which would suggest that leakages are occurring.

Figure 2.6: Lids stored next to the seaming machine.

Figure 2.7: Lids coincide with the

Figure 2.8: The leak test.

arrival of the cans.

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The Can Filling Line During the can filling process, the following variables/attributes are checked: 

Dry weight



Net weight



Brine temperature



Application of labels

Soaking Process: The first step in the can filling line is soaking the beans in water in one to five of the three ton tanks, depending on the demand, shown in figure 2.9. The beans are usually left to soak for eight to fourteen hours, depending on the variety. This is usually done during the night.

Quality: A 100g sample is taken to check that soaking is correctly carried out. The weight should double after soaking.

Figure 2.9: Soaking tank.

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Filling Process: Solid food goes through the reel washer, a hollow cylindrical pipe with showers to wash the food. Next, the food is dropped into a bucket elevator which takes it to the blancher. In the blancher, the food is boiled for about ten minutes to remove any gases or enzymes, and then goes through a de-stoning process in which foreign objects are removed. After de-stoning, the food is carried to a hopper, a funnel-like tank, through bucket elevators. This helps regulate the flow of the food to the next step. To guarantee good quality, a final manual inspection is done after the de-stoning process. One layer of the food passes through workers on a conveyor. The workers check for any defects, such as darkly colored or mashed pieces, or tiny pieces of wood. After this, the food is again taken to another hopper using bucket elevators. At this point, the can making line and the can filling lines meet. The empty cans are washed, filled with food in the solid filling machine shown in figure 2.12, and then filled with brine (salted water solution) by the liquid filling machine.

Quality: As shown in figures 2.10 and 2.12, cans are checked at the start of production and the filling machine is calibrated accordingly until the nominal value is met. Once the line is operating properly, 10 cans are checked every 30 minutes. If any errors occur, the machine is calibrated again.

Figure 2.10: Dry weight being checked.

Figure 2.12: The solid filling Figure 2.11: The dry weight meets the nominal value.

machine.

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Seaming Process: Figure 2.13 shows cans going through the seaming machine where the top is seamed using double seaming.

Quality: Before the seaming process, a built-in thermostat checks the temperature of the brine. The temperature should not fall below 75 ˚C. After the seaming process, 8 cans are taken every hour, and the following tests are carried out: 

4 cans are manually inspected. If more than 35% of the cover hook consists of wrinkles, the can is scrapped.



4 cans undergo the leak test, where the cans are submerged in water and pressurized at 1.5-2 bar. The tank is then inspected for the presence of bubbles, which would suggest that leakages are occurring.

Figure 2.13: Cans going through the seaming machine.

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Coding Process: The production date and time are stamped onto the cans. Figure 2.5 shows some cans that have been stamped. The ink used cannot be erased.

Quality: Since faulty coding would be extremely expensive; before production, one can of each product to be produced during the day is coded to make sure that the codes are correctly applied.

Figure 2.14: Coded cans.

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Cooking Process: The cans are cooked for between 10 and 70 minutes depending on the type of product.

Quality: Following the cooking in the retort area shown in figures 2.15 and 2.16, 2 cans from each cycle are taken and are qualitatively checked for the following attributes: 

Color



Taste



Texture



Appearance

Figure 2.15: The ovens in the retort area.

Figure 2.16: Monitors to control the cooking process.

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Labeling Process: Labels are applied to the cans depending on the product and the brand as shown in figures 2.17 and 2.18

Quality: All cans going through the labeling machine are inspected to ensure that the labels are correctly applied. If labels are incorrectly applied, they are cut off and the can is re-labeled.

Figure 2.17: Labels being inspected.

Figure 2.18: A stack of labels.

Finally, after labeling, eight cans are sent to the municipality for health related checks. A further four cans are retained as a sample to check against future complaints.

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Local Lab All the tests carried out in the local lab shall now be discussed. They are split into chemical and physical tests. Chemical Tests Acidity Test 10 ml of brine is measured using a measuring cylinder and is diluted by using 100 ml of distilled water. Then the mixture is deposited in a conical flask before three drops of Phenolphthalein is added. Finally, NOH soda drops are added until the mixture changes color to purple as shown in figure 3.0, indicating that it has become neutral.

Figure 2.19: The mixture turns purple when neutral.

PH Test The PH meter shown in figure 2.20 is inserted into a bottle containing the brine and its PH is indicated on the display. A PH of 7 indicates its neutral, below 7 is acidic and above 7 is basic.

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Brix Test A few drops of brine are deposited on the brix meter shown in figure 2.21, and is then examined visually as in figure 2.21 and 2.22, to determine how much solid precipitation of minerals is present.

Figure 2.21: The brix meter.

Figure 2.22: Using the brix

Figure 2.23: The display of the brix.

meeting to test the brix content.

Physical Tests Weight Checks The net and drained weights are measured as can be seen in figures 2.24, 2.25 and 2.26. The net weight should not be below 400g but should not exceed 430g.

Figure 2.25: Measuring the drained weight. Figure 2.24: Equipment for measuring the net and drained weights.

Figure 2.26: Measuring the net weight.

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Central Lab Receiving the Samples Samples are received in the central lab and stored in the area shown in figure 2.27. They are transported in the coolers shown in figure 2.28 to avoid defrosting during the tri. When the sample is to be tested, it is divided into parts and some of it is stored in a refrigerator for retesting in case there is a problem with the findings of the initial test. The refrigerator shown in figure 2.29 is used to store media to be used in the microbiology tests.

Figure 2.28: The coolers

Figure 2.27: The entrance

carrying the samples.

to the sample sotrage area.

Figure 2.29: Refrigerator storing the test media.

‎0

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Media Preparation As can be seen in figure 2.30, the media are bought in powder form and are stored until needed. Figure 2.31 shows the instructions on the container to help prepare the medium using some certain solutions, some of which are shown in figure 2.32. The medium is then heated before it is inserted in the machine in figure 3.33, called the autoclave. Finally, the medium is placed in a Petri-dish and stored until it is needed as shown in figure 2.34.

Figure 2.30: The powder

Figure 2.31: Instructions for

media stored.

preparing the media.

Figure 2.32: Liquid solutions used in

Figure 2.34: The petri dishes Figure 2.33: The autoclave

preparing the media.

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Microbiology Tests A 10g sample is diluted using 100ml of the buffer solution shown in figure 4.35, and it is then placed in the incubator shown in figure 4.36 for 2 hours at 37°C, after which it is poured in a sterilizing cup and placed in a sterilizer for between 15 and 20 minutes at 80°C, as shown in figure 2.37.

The tests in figure 2.38 count for: 

Total Bacteria



Anaerobic



Salmonella



Yeast and Mold

All of them should be nil.

Figure 2.35: The buffer solution.

Figure 2.36: The incubator.

Figure 2.38: Tests counting for Figure 2.37: The sterilizer.

bacteria presence.

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Canned Food Once the sample is received (note: the number of cans in the sample varies according to the production scheduled for that day), one of the cans is taken as a fresh sample and immediately undergoes weight, PH, and brix tests. The rest of the sample is split into 2 groups of equal size. One is stored at 55°C, whilst the other is stored at 37°C as shown in figure 2.39, and kept for 5 days before they undergo the same tests as the fresh sample.

Figure 2.39: Samples kept at 55°C for 5 days.

Note: The central lab carries out all the tests in the local lab, in addition to the microbiology tests discussed.

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The As-Is Raw Material Sampling Plans The current sampling plans used to test the quality of the incoming raw materials are evaluated in this section. The probability of acceptance, the average outgoing quality and the average total inspection were calculated for each plan. Note that most raw materials do not undergo acceptance sampling since the municipality already checks all food materials coming into Kuwait and in the case of such materials as glue, the company has an excellent relationship with its suppliers and is therefore confident enough to accept lots without subjecting them to sampling. The raw materials that do undergo sampling are the beans, the standard lids, the easy open lids, and the tin sheets. For all raw materials, one sample is taken before the lot is sentenced. Therefore, they were modeled as single sampling plans using the following equations:

The terminology is as follows:

n: The sample size.

N: Lot size.

C: Number of defective units accepted in a sample.

Pa: The probability of acceptance. d: The number of defective units in the p: Lot percentage defective.

sample.

Lots consisting of 1% defective items are deemed acceptable. Therefore, the sampling plans must have a high Pa value at p = 0.01.

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Beans Sampling Plan N = 400 n = 20 c=0

Table 2.1: Summary of the beans sampling plan.

Beans p

Pa

AOQ

ATI

0.01

0.8179

0.78%

89

0.02

0.6676

1.27%

146

0.03

0.5438

1.55%

193

0.04

0.4420

1.68%

232

0.05

0.3585

1.70%

264

0.06

0.2901

1.65%

290

0.07

0.2342

1.56%

311

0.08

0.1887

1.43%

328

0.09

0.1516

1.30%

342

0.10

0.1216

1.15%

354

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Probablity of Acceptance for the As-Is Beans Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.40: Probability of acceptance for the beans sampling plan.

Table 2.2: Probability of acceptance for different values of p for beans sampling plan.

p

Pa

0.01

0.8179

0.02

0.6676

0.03

0.5438

0.04

0.4420

0.05

0.3585

0.06

0.2901

0.07

0.2342

0.08

0.1887

0.09

0.1516

0.10

0.1216

Page | 41

AOQ

AOQ for the As-Is Beans Sampling Plan 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.41: AOQ for the beans sampling plan.

Table 2.3: AOQ for different values of p for beans sampling plan.

p

AOQ

0.01

0.78%

0.02

1.27%

0.03

1.55%

0.04

1.68%

0.05

1.70%

0.06

1.65%

0.07

1.56%

0.08

1.43%

0.09

1.30%

0.10

1.15%

Page | 42

ATI for the As-Is Beans Sampling Plan 400 350 300

ATI

250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.42: ATI for the beans sampling plan.

Table 2.4: ATI for different values of p for beans sampling plan.

p

ATI

0.01

89

0.02

146

0.03

193

0.04

232

0.05

264

0.06

290

0.07

311

0.08

328

0.09

342

0.10

354

Page | 43

As can be seen in figure 2.42 the probability of acceptance is low even at low values of p. At a p = 0.01, Pa is only 81.79%. A new sampling plan for this raw material is needed. Standard Lids Sampling Plan N = 4,000,000 n = 50 c=2 Table 2.5: Summary of the standard lids sampling plan.

Standard Lids p

Pa

AOQ

ATI

0.01

0.9862

0.99%

55,318

0.02

0.9216

1.84%

313,757

0.03

0.8108

2.43%

756,848

0.04

0.6767

2.71%

1,293,178

0.05

0.5405

2.70%

1,837,895

0.06

0.4162

2.50%

2,335,035

0.07

0.3108

2.18%

2,756,861

0.08

0.2260

1.81%

3,096,114

0.09

0.1605

1.44%

3,357,846

0.10

0.1117

1.12%

3,553,091

Page | 44

Probablity of Acceptance for the As-Is Standard Lids Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.43: Probability of acceptance for the standard lids sampling plan.

Table 2.6: Probability of acceptance for different values of p for standard lids sampling plan.

p

Pa

0.01

0.9862

0.02

0.9216

0.03

0.8108

0.04

0.6767

0.05

0.5405

0.06

0.4162

0.07

0.3108

0.08

0.2260

0.09

0.1605

0.10

0.1117

Page | 45

AOQ for the As-Is Standard Lids Sampling Plan 3.00% 2.50%

AOQ

2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.44 AOQ for the standard lids sampling plan. Table 2.7: AOQ for different values of p for standard lids sampling plan.

p

AOQ

0.01

0.99%

0.02

1.84%

0.03

2.43%

0.04

2.71%

0.05

2.70%

0.06

2.50%

0.07

2.18%

0.08

1.81%

0.09

1.44%

0.10

1.12%

Page | 46

ATI for the Standard Lids Sampling Plan 4,000,000 3,500,000 3,000,000

ATI

2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.45: ATI for the standard lids sampling plan.

Table 2.8: ATI for different values of p for standard lids sampling plan

p

ATI

0.01

55,318

0.02

313,757

0.03

756,848

0.04

1,293,178

0.05

1,837,895

0.06

2,335,035

0.07

2,756,861

0.08

3,096,114

0.09

3,357,846

0.10

3,553,091

Page | 47

Figure 2.45 shows that the probability of acceptance is as high as 98.6% at p = 0.01 and falls quickly as p increases. This is a very effective sampling plan.

Easy Open Lids Sampling Plan N = 1,400,000 n = 50 c=2 Table 2.9: Summary of the easy open lids sampling plan.

Easy Open Lids p

Pa

AOQ

ATI

0.01

0.9862

0.99%

19,946

0.02

0.9216

1.84%

112,982

0.03

0.8108

2.43%

272,491

0.04

0.6767

2.71%

465,566

0.05

0.5405

2.70%

661,659

0.06

0.4162

2.50%

840,626

0.07

0.3108

2.18%

992,480

0.08

0.2260

1.81%

1,114,608

0.09

0.1605

1.44%

1,208,830

0.10

0.1117

1.12%

1,279,116

Page | 48

Probablity of Acceptance for the As-Is Easy Open Lids Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.46: Probability of acceptance for the easy open lids sampling plan.

Table 2.10: Probability of acceptance for different values of p for easy open lids sampling plan.

p

Pa

0.01

0.9862

0.02

0.9216

0.03

0.8108

0.04

0.6767

0.05

0.5405

0.06

0.4162

0.07

0.3108

0.08

0.2260

0.09

0.1605

0.10

0.1117

Page | 49

AOQ for the As-Is Easy Open Lids Sampling Plan 3.00% 2.50%

AOQ

2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.47: AOQ for the easy open lids sampling plan.

Table 2.11: AOQ for different values of p for easy open lids sampling plan.

p

AOQ

0.01

0.99%

0.02

1.84%

0.03

2.43%

0.04

2.71%

0.05

2.70%

0.06

2.50%

0.07

2.18%

0.08

1.81%

0.09

1.44%

0.10

1.12%

Page | 50

ATI for the Easy Open Lids Sampling Plan 1,400,000 1,200,000

ATI

1,000,000 800,000 600,000 400,000 200,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.48: ATI for the easy open lids sampling plan.

Table 2.12: ATI for different values of p for easy open lids sampling plan.

p

ATI

0.01

19,946

0.02

112,982

0.03

272,491

0.04

465,566

0.05

661,659

0.06

840,626

0.07

992,480

0.08

1,114,608

0.09

1,208,830

0.10

1,279,116

Page | 51

Figure 2.48 shows that the probability of acceptance is as high as 98.6% at p = 0.01 and falls quickly as p increases. This is a very effective sampling plan. Tins Sheets Sampling Plan N = 420,000 n = 10 c=0 Tin sheets p

Pa

AOQ

ATI

0.01

0.9044

0.90%

40,169

0.02

0.8171

1.63%

76,838

0.03

0.7374

2.21%

110,289

0.04

0.6648

2.95%

140,777

0.05

0.5987

2.99%

168,536

0.06

0.5386

2.69%

193,787

0.07

0.4850

2.42%

216,732

0.08

0.4344

2.17%

237,561

0.09

0.3894

1.95%

256,449

0.10

0.3487

1.74%

273,559

Table 2.13: Summary of the tin sheets sampling plan.

Page | 52

Probablity of Acceptance for the As-Is Tin Sheets Sampling Plan 1.0000 0.8000

Pa

0.6000 0.4000 0.2000 0.0000 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.49: Probability of acceptance for the tin sheets sampling plan. Table 2.14: Probability of acceptance for different values of p for tin sheets sampling plan.

p

Pa

0.01

0.9044

0.02

0.8171

0.03

0.7374

0.04

0.6648

0.05

0.5987

0.06

0.5386

0.07

0.4850

0.08

0.4344

0.09

0.3894

0.10

0.3487

Page | 53

AOQ for the As-Is Tin Sheets Sampling Plan 3.50% 3.00%

AOQ

2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.50: AOQ for the tin sheets sampling plan.

Table 2.15: AOQ for different values of p for tin sheets sampling plan.

p

AOQ

0.01

0.90%

0.02

1.63%

0.03

2.21%

0.04

2.95%

0.05

2.99%

0.06

2.69%

0.07

2.42%

0.08

2.17%

0.09

1.95%

0.10

1.74%

Page | 54

ATI for the As-Is Tin Sheets Sampling Plan 300,000 250,000

ATI

200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.51: ATI for the tin sheets sampling plan.

Table 2.16: ATI for different values of p for tin sheets sampling plan.

p

ATI

0.01

40,169

0.02

76,838

0.03

110,289

0.04

140,777

0.05

168,536

0.06

193,787

0.07

216,732

0.08

237,561

0.09

256,449

0.10

273,559

Page | 55

As can be seen in figure 2.51, the probability of acceptance is low even at low values of p. At a p = 0.01, Pa is only around 90%. A new sampling plan for this raw material is needed. From studying the three different properties for each sampling plan, it was conclude that the plans for the beans and tin sheets need to be redesigned because the Pa curves are inadequate. The procedure followed in the design of the new plans is shown in sections 9 and 10 of the report.

Page | 56

Quality Control Documentation Having studied the quality control documentation in place, the finished product quality sheet shown in figure 2.52 was found to be particularly inadequate as it does not record individual data and wastes a lot of space on tests that always return a positive result. Thus, it was decided to come up with new designs based on statistical and economical considerations. The new quality sheets as well as the properties taken into consideration while designing them are discussed in detail in section 8. Figure 2.53 shows the brine quality sheet which does not show the standards that need to be met for each product. It was therefore recommended that a sheet with all the standards written be posted in a clearly visible location in the lab. Finally, after discussions about the central lab results with the quality personnel, it was noticed that the specifications are not realistic and need to be changed since many results for the brix and PH fall outside the limits even though they were of acceptable quality. It is therefore imperative that the specifications are reset in conjunction with the input of the quality engineers.

Page | 57

Figure 2.52: The as-is finished product quality sheet.

Page | 58

Figure 2,53: The As-Is brine quality sheet.

Page | 59

2.3 Pareto Analysis of Can Defects After studying quality control data for a whole month’s worth of production, there was a need to pinpoint the most common types of defects that occurred. A Pareto chart was used. The Pareto chart is one of the seven basic tools of quality control, which include the histogram, Pareto chart, check sheet, control chart, cause-and-effect diagram, flowchart, and scatter diagram. The Pareto chart is a special type of bar chart where the values being plotted are arranged in descending order. A Pareto chart was constructed for the different types of defects in the can filling process and determined which defects were to be studied in depth. As shown in figure 7.0, the main problems were the brine temperature and net weight.

Types of Defects 35 30 25 20 15 10 5 0 Brine Net Weight Temprature

Filling weight

Vegetable Oil

Seaming

Lacquering

Coding

Figure 2.54: Pareto chart for types defects.

Page | 60

Brine Temperature Problem Upon further inspection, it was found that there was only one incident where the temperature was below 70°C. After discussing this with the quality engineer, it was discovered that products with 70°C brine are acceptable. The target of a minimum temperature of 75°C is set to keep a safety buffer. Therefore, there was no need to waste resources studying a problem that did not exist.

Net Weight Problem A root cause analysis was conducted to pinpoint the source of the problem. Various quality tools, including the why-why diagram, fishbone diagram, and control charts were used in the analysis. Since production is sporadic, meaning a single product will not be produced continually but will be produced based on demand and thus can sometimes be produced on a monthly basis, for example, there were not enough data points to construct a control chart with a proper sub group size. Therefore, individual and moving range charts were constructed instead, to study the performance of the filling system. The products used for this analysis were the chick peas and green peas since they account for the bulk of production (almost 40%). Note that the nominal value for the 400g cans is set at 415g with a tolerance of ±15g.

Page | 61

Why-Why Diagram

Figure 2.55: Why-why diagram for the cause of overfilling.

Page | 62

Fish Bone Diagram

Figure 2.56: Fish bone diagram for the cause of overfilling.

Page | 63

Control Charts Individual and Moving Range Charts for the Net Weight of Chick Peas

Figure 2.57: Control chart for the net weight of chick peas.

Comments: 

Points are randomly scattered.



The process average is too close to upper specification limit.



Points 12-17 indicate lack of vigilance in meeting the target as the weight keeps increasing.



70% of points within ± 1σ.



96.67% of points within ± 2σ.



The Process is under control.



Overfilling could be due to a problem in the dry filling. Therefore, we decided to study the filling weight as well.

Page | 64

Table 2.17: Net Weight data of Chick Peas for the month of October.

Net Weight of Chick Peas 430

430

424

426

426

430

430

428

432

430

434

428

430

430

432

430

430

430

425

426

430

428

426

424

430

428

432

430

430

Page | 65

Individual and Moving Range Charts for the Filling Weight of Chick Peas

Figure 2.58: Control chart for the filling weight of chick peas.

Comments: 

The nominal value for the chick peas filling weight is 205 with a tolerance of ±5g.



The points are randomly scattered.



The process average is close to the upper specification limit.



The only out of control point corresponds to the nominal target!



Runs of points of equal value indicate ability to consistently produce cans at the same weight.



86.67% within ± 1σ.



Too many points lie outside the 2σ boundaries. The process variation must be lowered by being more proactive in changing the process average when deviations from the nominal target occur.

Page | 66

Table 2.18: Net Weight data of Chick Peas for the month of October.

Filling Weight of Chick Peas 205

210

208

210

208

209

209

209

208

209

209

209

209

207

208

209

209

208

208

209

208

209

208

208

210

208

210

209

210

209

209

209

208

Page | 67

Individual and Moving Range Charts for the Net Weight of Green Peas

Figure 2.58: Control chart for the net weight of green peas.

Comments: 

Points are randomly scattered.



Process average is lower than for the chick peas.



92.3% of points within ± 1σ.



96.2% of points within ± 2σ.



Process is under control.

Page | 68

Table 2.19: Net Weight data for Green Peas for the month of October.

Net Weight of Green Peas 420

424

422

420

420

426

426

418

424

420

421

420

426

422

420

420

424

430

421

426

420

420

422

424

420

424

421

Page | 69

Individual and Moving Range Charts for the Filling Weight of Green Peas

Figure 2.59: Control chart for the filling weight of green peas.

Comments: 

The nominal value for the green peas filling weight is 187.5 with a tolerance of ±2,5g.



Points are randomly scattered.



The process average is almost exactly equal to the nominal target. This is consistent with lower net weight than the chick peas where the filling weight average was close to the upper specification limit.



92.3% of points within ± 1σ.



92.3% of points within ± 2σ.



There is a reasonable amount of variation, with only one out of control point.

Page | 70

Table 2.20: Filling Weight data of Green Peas for the month of October.

Filling Weight of Green Peas 187

187

188

187

188

188

187

188

188

187

188

188

188

188

187

187

187

187

188

185

189

188

187

187

188

187

Page | 71

Conclusion

The runs of equal points dispersed in the control charts, and the center line of the filling weight chart for green peas, which corresponds to its nominal target, suggested that the process is indeed capable of producing cans with little variability in the filling weight. However, there seemed to be a lack of interest in correcting process shifts when they did occur. It was concluded that this was due to ignorance of the cost of consistently overfilling the cans. By studying the filling data for the month of October, the Cost Analysis group estimated that the company wastes around 68,000 KD annually by overfilling their cans.

Page | 72

2.4 New Quality Control Documentation

Considerations for designing the new sheets The sporadic nature of production means that some products are only produced for two hours a month, therefore recording only the averages will not suffice for the construction of proper control charts. It was decided that tests shall be carried out every 15 minutes as the rate of production (140 cans/min) is high, and to collect sufficient data to construct reliable control charts to monitor system performance. This resulted in eight subgroups per production run. The subgroup size had to be set so that a single production run would produce enough data points to construct individual control charts. However this couldn’t be done arbitrarily and therefore, statistical analysis was used in to determine the optimum subgroup size. There are two tolerance widths for the dry weights of the different products, 5 and 10 grams. The tighter width of 5 grams was used to base the subgroup size on, so that the quality sheets can be applied for all products. It was qualitatively determined that a change of one gram can be tolerated before a process mean shift needs to be recognized quickly as it would be close to the specification limit at that point. It was also decided that the product PH should be studied immediately after the cooking operation rather than wait until the production run is finished and the samples are sent to the labs. In this way, defects can be detected earlier and thus cumulative costs of poor quality reduced. With these considerations in mind, two quality control sheets, one for the filling weight and one for the finished products, measuring both the net weight and the PH, were created.

Page | 73

Statistical Analysis The number of standard deviations, k, was taken to be 1.5 because for the filling weight, σ = 0.7 i.e. the shift (kσ) is almost equal to 1g. Using this k value, β was found from the following graph:

Figure 2.60

Different parameters were calculated for subgroup sizes of 5 and 10 suing the following equations:

Average run length, the average number of subgroups before a shift of kσ is detected:

Page | 74

Average time to signal, the average time before the shift is detected:

The number of individual cans inspected before the process shift is detected:

Cost Considerations The combined salary of all quality personnel is 1170 KD per month, which works out to be 45KD per day. It takes 15 seconds to check each can’s weight, 30 seconds to check the temperature, and 30 seconds for transportation. There are 10 hours of production per day, and a sample is taken every fifteen minutes, therefore 40 checks per day. For a subgroup of 10 cans, it takes 6 minutes to carry out the tests. Therefore, the total time the personnel are engaged in quality tests is 240 minutes per day. This will cost: 240/600 * 45 = 18 KD/day. For a subgroup of 5 cans it takes 3.5 minutes to carry out the tests. Therefore, the total time the personnel are engaged in quality test is 140 minutes per day. This will cost: 140/600 * 45 = 10.5 KD/day.

Page | 75

Decision Table 2.21



n = 10

n=5

β

0.1

0.3

ARL

1.11

1.43

I

11.1

7.15

ATS

16.65

21.45

Cost (KD/day)

18

10.5

The Average Run Length for both subgroup sizes of 5 and 10 is smaller than 2.



I is smaller for n = 5.



Cost is almost half for n = 5.

Therefore the trade off of a slightly higher average run length is worth it and we shall consider n = 5 as our sample size.

Page | 76

Date:__ /__ /__ Production Run 1 Time #1

Time #2

Variant: Time #3

Time #4

Can Size(g): Time #5

Time #6

Time #7

Time #8

Can # 1 2 3 4 5 Avg

Production Run 2 Time #1

Time #2

Variant: Time #3

Time #4

Can Size(g): Time #5

Time #6

Time #7

Time #8

Can # 1 2 3 4 5 Avg

Production Run 3 Time #1

Time #2

Variant: Time #3

Time #4

Can Size(g): Time #5

Time #6

Time #7

Time #8

Can # 1 2 3 4 5 Avg

Figure 2.62: The new finished product quality sheet.

Page | 77

National Canned Food Company - Daniah

Q.C Department

Quality Sheet of (

)gm can

Date:

Can Production Date:

Variant:

Can Type:

Can #

Time #1: ……………….

Time #2: ……………….

Time #3: ……………….

Brine Temp:

Brine Temp:

Brine Temp:

Net Wt

PH

Net Wt

PH

Net Wt

Time #4: ………………. Brine Temp:

PH

Net Wt

PH

1 2 3 4 5 Average

Can #

Time #5: ……………….

Time #6: ……………….

Time #7: ……………….

Brine Temp:

Brine Temp:

Brine Temp:

Net Wt

PH

Net Wt

PH

Net Wt

Time #8: ………………. Brine Temp:

PH

Net Wt

1 2 3 4 5 Average

Other Defects Time #

Can #

Type:

Time #

Can #

Type:

Time #

Can #

Type:

Time #

Can #

Type:

Time #

Can #

Type:

Time #

Can #

Type:

Time #

Can #

Type:

Time #

Can #

Type:

Key:

SS: Seaming Steam

CW: Can Wash

C: Code

Page | 78

PH

2.5 New Sampling Plans

Proposed New Single Sampling Plan for Beans A new single sampling plan for beans was constructed using α and β values. The probability of acceptance had to be at least 95% for a lot with percent defective of 1 or less (i.e p is no larger 0.01). An attempt was made to achieved a Pa of 98% at p = 0.01, whilst making the Pa curve is sensitive enough to get a Pa no more than 5% at p = 0.10. α was set at 5%, whilst β was kept at 10%, in the following equations:

Using the relevant nomograph, the plan that came closest to meeting these constraints was the one with n = 70, and c = 2. Probablity of Acceptance for the New Beans Single Sampling Plan 1.00

Pa

0.80 0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.63: Probability of acceptance for the new beans single sampling plan.

Page | 79

Table 2.22: Probability of acceptance for the new beans single sampling plan at different values of p.

p

Pa

0.01

0.9667

0.02

0.8350

0.03

0.6492

0.04

0.4656

0.05

0.3137

0.06

0.2013

0.07

0.1241

0.08

0.0740

0.09

0.0428

0.10

0.0242

Page | 80

AOQ

AOQ for the New Beans Single Sampling Plan 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.64: AOQ for the new beans single sampling plan.

Table 2.23: AOQ for the new beans single sampling plan at different values of p.

p

AOQ

0.01

0.80%

0.02

1.38%

0.03

1.61%

0.04

1.54%

0.05

1.29%

0.06

1.00%

0.07

0.72%

0.08

0.49%

0.09

0.32%

0.10

0.20%

Page | 81

ATI

ATI for the New Beans Single Sampling Plan 450 400 350 300 250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.65: ATI for the new beans single sampling plan.

Table 2.24: ATI for the new beans single sampling plan at different values of p.

p

ATI

0.01

81

0.02

124

0.03

186

0.04

246

0.05

296

0.06

334

0.07

359

0.08

376

0.09

386

0.10

392

Figure 2.65 shows that the Probability of acceptance became much more acceptable with a value of 96.67% at p =0.01 and decreasing very quickly, thereafter.

Page | 82

Comparison between the New Single Sampling and As-Is Plans For Beans To judge whether the new sampling plan is superior to the exising one, the P a curve did not siffice to make our decision. It also had to be verified that the cost of the new plan was lower at most values of p, by using the following equations:

Cost of poor quality = AOQ * cost of producing one unit * total annual production

Cost of inspection = ATI * hourly wage of quality personnel * average time to inspect one unit of raw material (in hours)

As mentioned in section 8, the hourly wages of the quality personnel is 4.5 KD.

The average time to inspect one bag of beans is 30 minutes.

The average time to inspect one tin sheet is 2 minutes.

Page | 83

Comparison between the Probability of Acceptance for the Beans As-Is and New Single Sampling Plans 1.00 0.80

Pa

0.60 0.40

New Single Sampling Plan

0.20

As is Plan

0.00 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.66: Comparison between the probability of acceptance for the beans as-is and the new single sampling plan. Table 2.25: Comparison between the probability of acceptance for the beans as-is and new single sampling plans at different values of p.

New Single p

As-Is

Sampling

0.01

0.8179

0.9667

0.02

0.6676

0.8350

0.03

0.5438

0.6492

0.04

0.4420

0.4656

0.05

0.3585

0.3137

0.06

0.2901

0.2013

0.07

0.2342

0.1241

0.08

0.1887

0.0740

0.09

0.1516

0.0428

0.10

0.1216

0.0242

Page | 84

AOQ

Comparison between the AOQ for the Beans As-Is and New Single Sampling Plans 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00%

New Single Sampling Plan As is Plan

0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.67: Comparison between the AOQ for the beans as-is and the new single sampling plan.

Table 2.26: Comparison between the AOQ for the beans as-is and new single sampling plans at different values of p.

p

As-Is

New Single Sampling

0.01

0.78%

0.80%

0.02

1.27%

1.38%

0.03

1.55%

1.61%

0.04

1.68%

1.54%

0.05

1.70%

1.29%

0.06

1.65%

1.00%

0.07

1.56%

0.72%

0.08

1.43%

0.49%

0.09

1.30%

0.32%

0.10

1.15%

0.20%

Page | 85

Comparison between the ATI for the Beans As-Is and New Single Sampling Plans 500

ATI

400 300 200

New Single Sampling Plan

100

As is Plan

0 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.88: Comparison between the ATI for the beans as-is and the new single sampling plan.

Table 2.27: Comparison between the ATI for the beans as-is and new single sampling plan at different values of p.

p

As-Is

New Single Sampling

0.01

89

81

0.02

146

124

0.03

193

186

0.04

232

246

0.05

264

296

0.06

290

334

0.07

311

359

0.08

328

376

0.09

342

386

0.10

354

392

Page | 86

Cost

Comparison between Costs of the Beans As-Is and New Single Sampling Plans 60000 50000 40000 30000 20000 10000 0

New Single sampling Plan As-is plan 0

0.02 0.04 0.06 0.08 0.1

Lot fraction defective, p Figure 2.89: Comparison between the costs of the beans as-is and the new single sampling plan.

Table 9.28: Comparison between the costs of the beans as-is and new single sampling plan at different values of p.

p

As-Is

New Single Sampling

0.01

23595

24194

0.02

38521

41761

0.03

47098

48798

0.04

51093

46811

0.05

51863

39632

0.06

50440

30752

0.07

47600

22386

0.08

43916

15545

0.09

39808

10445

0.1

35571

6889

As is clear from figure 2.89, the cost of the new single sampling plan is lower for most values of p. Page | 87

2.6 Proposed Double Sampling Plans For Beans After construtcing the new sampling plan, the possibility of constructing a superior double sampling plan that will reduce the cost of sampling but still meet the target of having a Pa no less than 95% when p = 0.01, was also considered. Six different double sampling plans were tested and the best was chosen based on the total cost of the plan. Calculations of the paramters for the double sampling plans were made usin the following equations:

The parameters for the six plans are summarized as follows:. Table 9.29: Summary of the parameters of the six proposed beans double sampling plans

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

n1

10

15

20

25

30

35

n2

30

45

60

75

90

60

c1

0

0

0

0

0

0

c2

2

2

2

2

2

2

Where: n1: Size of the first sample. n2: Size of the second sample. c1: The number of defects tolerated in the first sample without a need for the second sample. c2: The number of defects tolerated in both samples, combined, before the lot is rejected. Page | 88

Probablity of Acceptance for the First Proposed Beans Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.90: Probability of acceptance for the first proposed beans sampling plan.

Table 2.30: Probability of acceptance for the first proposed beans sampling plan at different values of.

p

Pa

0.01

0.9955

0.02

0.9721

0.03

0.9265

0.04

0.8633

0.05

0.7892

0.06

0.7106

0.07

0.6325

0.08

0.5582

0.09

0.4898

0.10

0.4281

Page | 89

AOQ

AOQ for the First Proposed Beans Sampling Plan 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.91: AOQ for the first proposed beans sampling plan.

Table 2.31: AOQ for the first proposed beans sampling plan at different values of p.

p

AOQ

0.01

0.96%

0.02

1.87%

0.03

2.67%

0.04

3.31%

0.05

3.78%

0.06

4.08%

0.07

4.24%

0.08

4.28%

0.09

4.23%

0.10

4.11%

Page | 90

ATI for the First Proposed Beans Sampling Plan 250 200

ATI

150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.92: ATI for the first proposed beans sampling plan. Table 2.32: ATI for the first proposed beans sampling plan at different values of p.

p

ATI

0.01

14

0.02

26

0.03

44

0.04

69

0.05

98

0.06

128

0.07

158

0.08

186

0.09

212

0.10

235

Page | 91

Probability of Acceptance for the Second Proposed Beans Sampling Plan 1.00

Pa

0.80 0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.93: Probability of acceptance for the second proposed beans sampling plan.

Table 2.33: Probability of acceptance for the 2nd proposed beans sampling plan at different values of p.

p

Pa

0.01

0.9865

0.02

0.9263

0.03

0.8278

0.04

0.7130

0.05

0.5992

0.06

0.4963

0.07

0.4080

0.08

0.3347

0.09

0.2748

0.10

0.2262

Page | 92

AOQ for the Second Proposed Beans Sampling Plan 3.00% 2.50%

AOQ

2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.94: AOQ for the second proposed beans sampling plan.

Table 2.34: AOQ for the second proposed beans sampling plan at different values of p

p

AOQ

0.01

0.94%

0.02

1.74%

0.03

2.32%

0.04

2.67%

0.05

2.81%

0.06

2.80%

0.07

2.69%

0.08

2.53%

0.09

2.35%

0.10

2.15%

.

Page | 93

ATI for the Second Proposed Beans Sampling Plan 350 300

ATI

250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.95: ATI for the second proposed beans sampling plan.

Table 2.35: ATI for the second proposed beans sampling plan at different values of p.

p

ATI

0.01

26

0.02

52

0.03

90

0.04

133

0.05

175

0.06

213

0.07

246

0.08

273

0.09

296

0.10

314

Page | 94

Probability of Acceptance for the Third Proposed Beans Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.96: Probability of acceptance for the third proposed beans sampling plan.

Table 2.36: Probability of acceptance for the third proposed beans sampling plan at different values of p.

p

Pa

0.01

0.9718

0.02

0.8637

0.03

0.7142

0.04

0.5659

0.05

0.4395

0.06

0.3393

0.07

0.2625

0.08

0.2042

0.09

0.1599

0.10

0.1258

Page | 95

AOQ for the Third Proposed Beans Sampling Plan 2.50%

AOQ

2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.97: AOQ for the third proposed beans sampling plan.

Table 2.37: AOQ for different values of p for the third proposed beans sampling plan.

p

AOQ

0.01

0.90%

0.02

1.58%

0.03

1.96%

0.04

2.08%

0.05

2.03%

0.06

1.89%

0.07

1.72%

0.08

1.53%

0.09

1.36%

0.10

1.19%

Page | 96

ATI for the Third Proposed Beans Sampling Plan 400 350 300

ATI

250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.98: ATI for the third proposed beans sampling plan.

Table 2.38: ATI for different values of p for the third proposed beans sampling plan.

p

ATI

0.01

40

0.02

84

0.03

139

0.04

192

0.05

238

0.06

274

0.07

302

0.08

323

0.09

340

0.10

352

Page | 97

Probablity of Acceptance for the Fourth Proposed Beans Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.99: Probability of acceptance for the fourth proposed beans sampling plan

Table 2.39: Probability of acceptance for the fourth proposed beans sampling plan at different values of p.

p

Pa

0.01

0.9515

0.02

0.7913

0.03

0.6027

0.04

0.4417

0.05

0.3209

0.06

0.2344

0.07

0.1730

0.08

0.1288

0.09

0.0965

0.10

0.0726

Page | 98

AOQ

AOQ for the Fourth Proposed Beans Sampling Plan 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.100: AOQ for the fourth proposed beans sampling plan.

Table 2.40: AOQ for the fourth proposed beans sampling plan at different values of p.

p

AOQ

0.01

0.86%

0.02

1.41%

0.03

1.62%

0.04

1.60%

0.05

1.46%

0.06

1.29%

0.07

1.12%

0.08

0.96%

0.09

0.81%

0.10

0.68%

Page | 99

ATI for the Fourth Proposed Beans Sampling Plan 400 350 300

ATI

250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.101: ATI for the fourth proposed beans sampling plan.

Table 2.41: ATI for the fourth proposed beans sampling plan at different values of p.

p

ATI

0.01

56

0.02

117

0.03

184

0.04

240

0.05

283

0.06

314

0.07

336

0.08

352

0.09

364

0.10

373

Page | 100

Probablity of Acceptance for the Fifth Proposed Beans Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.102: Probability of acceptance for the fifth proposed beans sampling plan.

Table 2.42: Probability of acceptance for the fifth proposed beans sampling plan at different values of p.

p

Pa

0.01

0.9262

0.02

0.7153

0.03

0.5025

0.04

0.3438

0.05

0.2364

0.06

0.1650

0.07

0.1167

0.08

0.0832

0.09

0.0595

0.10

0.0425

Page | 101

AOQ for the Fifth Proposed Beans Sampling Plan 1.40% 1.20%

AOQ

1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.103: AOQ for the fifth proposed beans sampling plan.

Table 2.42: AOQ for the fifth proposed beans sampling plan at different values of p.

p

AOQ

0.01

0.81%

0.02

1.25%

0.03

1.33%

0.04

1.23%

0.05

1.07%

0.06

0.90%

0.07

0.75%

0.08

0.61%

0.09

0.49%

0.10

0.39%

Page | 102

ATI

ATI for the Fifth Proposed Beans Sampling Plan 450 400 350 300 250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.104: ATI for the fifth proposed beans sampling plan.

Table 2.43: ATI for the fifth proposed beans sampling plan at different values of p.

p

ATI

0.01

74

0.02

151

0.03

223

0.04

277

0.05

314

0.06

340

0.07

357

0.08

369

0.09

378

0.10

384

Page | 103

Probablity of Acceptance for the Sixth Proposed Beans Sampling Plan 1.00

Pa

0.80 0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.105: Probability of acceptance for the sixth proposed beans sampling plan. Table 2.44: Probability of acceptance for the sixth proposed beans sampling plan at different values of p.

p

Pa

0.01

0.9506

0.02

0.7794

0.03

0.5696

0.04

0.3876

0.05

0.2533

0.06

0.1624

0.07

0.1035

0.08

0.0662

0.09

0.0426

0.10

0.0277

Page | 104

AOQ for the Sixth Proposed Beans Sampling Plan 1.40% 1.20%

AOQ

1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.106: AOQ for the sixth proposed beans sampling plan.

Table 2.44: AOQ for the sixth proposed beans sampling plan at different values of p.

p

AOQ

0.01

0.83%

0.02

1.34%

0.03

1.47%

0.04

1.33%

0.05

1.10%

0.06

0.85%

0.07

0.64%

0.08

0.47%

0.09

0.34%

0.10

0.25%

Page | 105

ATI

ATI for the Sixth Proposed Beans Sampling Plan 450 400 350 300 250 200 150 100 50 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.107: ATI for the sixth proposed beans sampling plan.

Table 2.45: ATI f for the sixth proposed beans sampling plan at different values of p.

p

ATI

0.01

67

0.02

131

0.03

204

0.04

267

0.05

312

0.06

343

0.07

364

0.08

377

0.09

385

0.10

390

Page | 106

Comparison between the Proposed Double Sampling Plans for Beans

Pa

Comparison between the Probablity of Acceptance for the Proposed Beans Double Sampling Plans 1.00 0.80 0.60 0.40 0.20 0.00

Plan 1 Plan 2 Plan 3 Plan 4 0.00

0.02

0.04

0.06

0.08

Plan 5

0.10

Plan 6

Lot fraction defective, p

Figure 2.108: Comparison between the probability of acceptance for the proposed beans double sampling plans. Table 2.46: Comparison between the probability of acceptance for the proposed beans double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

0.9955

0.9865

0.9718

0.9515

0.9262

0.9506

0.02

0.9721

0.9263

0.8637

0.7913

0.7153

0.7794

0.03

0.9265

0.8278

0.7142

0.6027

0.5025

0.5696

0.04

0.8633

0.7130

0.5659

0.4417

0.3438

0.3876

0.05

0.7892

0.5992

0.4395

0.3209

0.2364

0.2533

0.06

0.7106

0.4963

0.3393

0.2344

0.1650

0.1624

0.07

0.6325

0.4080

0.2625

0.1730

0.1167

0.1035

0.08

0.5582

0.3347

0.2042

0.1288

0.0832

0.0662

0.09

0.4898

0.2748

0.1599

0.0965

0.0595

0.0426

0.10

0.4281

0.2262

0.1258

0.0726

0.0425

0.0277

Page | 107

AOQ

Comparison between the AOQ for the Proposed Beans Double Sampling Plans 4.50% 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00%

Plan 1 Plan 2 Plan 3 Plan 4 Plan 5 0.00

0.02

0.04

0.06

0.08

0.10

Plan 6

Lot fraction defective, p Figure 2.109: Comparison between the AOQ for the proposed beans double sampling plans.

Table 2.47 : Comparison between the AOQ for the proposed beans double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

0.96%

0.94%

0.90%

0.86%

0.81%

0.83%

0.02

1.87%

1.74%

1.58%

1.41%

1.25%

1.34%

0.03

2.67%

2.32%

1.96%

1.62%

1.33%

1.47%

0.04

3.31%

2.67%

2.08%

1.60%

1.23%

1.33%

0.05

3.78%

2.81%

2.03%

1.46%

1.07%

1.10%

0.06

4.08%

2.80%

1.89%

1.29%

0.90%

0.85%

0.07

4.24%

2.69%

1.72%

1.12%

0.75%

0.64%

0.08

4.28%

2.53%

1.53%

0.96%

0.61%

0.47%

0.09

4.23%

2.35%

1.36%

0.81%

0.49%

0.34%

0.10

4.11%

2.15%

1.19%

0.68%

0.39%

0.25%

Page | 108

ATI

Comparison between the ATI for the Proposed Beans Double Sampling Plans 400 350 300 250 200 150 100 50 0

Plan 1 Plan 2 Plan 3 Plan 4 Plan 5 0.00

0.02

0.04

0.06

0.08

0.10

Plan 6

Lot fraction defective, p Figure 2.110: Comparison between the ATI for the proposed beans double sampling plans.

Table 2.48: Comparison between the ATI for the proposed beans double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

14

26

40

56

74

67

0.02

26

52

84

117

151

131

0.03

44

90

139

184

223

204

0.04

69

133

192

240

277

267

0.05

98

175

238

283

314

312

0.06

128

213

274

314

340

343

0.07

158

246

302

336

357

364

0.08

186

273

323

352

369

377

0.09

212

296

340

364

378

385

0.10

235

314

352

373

384

390

Page | 109

Comparison between the Costs of the Proposed Beans Double Sampling Plans 140000 120000 Plan 1

80000

Plan 2

60000

Plan 3

Cost

100000

40000

Plan 4

20000

Plan 5

0 0.00

0.02

0.04

0.06

0.08

0.10

Plan 6

Lot fraction defective, p Figure 2.111: Comparison between the costs of the proposed beans double sampling plans. Table 2.49: Comparison between costs for the proposed beans double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

29,051

28,218

27,191

26,003

24,698

25,244

0.02

56,430

52,533

47,826

42,813

37,881

40,750

0.03

80,411

70,196

59,285

49,151

40,427

44,610

0.04

99,730

80,633

62,942

48,574

37,567

40,747

0.05

113,907

84,916

61,557

44,693

32,892

33,696

0.06

123,125

84,717

57,508

39,676

27,982

26,354

0.07

127,985

81,630

52,341

34,537

23,393

20,011

0.08

129,281

76,896

46,898

29,680

19,294

14,993

0.09

127,834

71,365

41,588

25,246

15,730

11,193

0.1

124,397

65,568

36,588

21,281

12,699

8,377

Page | 110

As can be seen from figure 2.111, plans 5 and 6 have the lowest total costs. Plan 6 was deemed to the best because it also satisfied the constraint of Pa being at least 95% at p = 0.01.

Comparison between the As-is and Double Sampling Plans For Beans

Pa

Comparison between the Probability of Acceptance for the Beans As-Is and Double Sampling Plans 1.00 0.80 0.60 0.40 0.20 0.00

Double sampling plan 0 0.02 0.04 0.06 0.08 0.1

As is Plan

Lot fraction defective, p Figure 2.112: Comparison between the probability of acceptance for beans as-is and double sampling plans.

Table 2.50: Comparison between the probability of acceptance for the beans as- is and double sampling plans at different values of p.

p

As-Is

Double Sampling

0.01

0.8179

0.9506

0.02

0.6676

0.7794

0.03

0.5438

0.5696

0.04

0.4420

0.3876

0.05

0.3585

0.2533

0.06

0.2901

0.1624

0.07

0.2342

0.1035

0.08

0.1887

0.0662

0.09

0.1516

0.0426

0.10

0.1216

0.0277

Page | 111

AOQ

Comparison between the AOQ for the Beans As-Is and Double Sampling Plans 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00%

Double sampling plan As is Plan

0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p

Figure 2.113: Comparison between the AOQ for the beans as-is and double sampling plans.

Table 2.52: Comparison between the ATI for the beans as- is and double sampling plans at different Double p

As-Is

Sampling

0.01

0.78%

0.83%

0.02

1.27%

1.34%

0.03

1.55%

1.47%

0.04

1.68%

1.33%

0.05

1.70%

1.10%

0.06

1.65%

0.85%

0.07

1.56%

0.64%

0.08

1.43%

0.47%

0.09

1.30%

0.34%

0.10

1.15%

0.25%

Page | 112

ATI

Comparison between the ATI for the Beans As-Is and Double Sampling Plans 450 400 350 300 250 200 150 100 50 0

Double sampling plan As is Plan

0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.114: Comparison between the ATI for the beans as-is and double sampling plans.

Table 2.51: Comparison between the AOQ for the beans as- is and double sampling plan at different values of p.

p

As-Is

Double Sampling

0.01

89

67

0.02

146

131

0.03

193

204

0.04

232

267

0.05

264

312

0.06

290

343

0.07

311

364

0.08

328

377

0.09

342

385

0.10

354

390

values of p.

Page | 113

Comparison between Costs of the Beans As-Is and Double Sampling Plans Cost

60000 40000

Double sampling plan

20000 0

As-is plan 0 0.02 0.04 0.06 0.08 0.1 Lot fraction defective, p

Figure 2.115: Comparison between costs of the beans as-is and double sampling plans.

Table 2.53: Comparison between the costs of the beans as- is and double sampling plans at different values of p

p

As-Is

Double Sampling

0.01

23,595

25,244

0.02

38,521

40,750

0.03

47,098

44,610

0.04

51,093

40,747

0.05

51,863

33,696

0.06

50,440

26,354

0.07

47,600

20,011

0.08

43,916

14,993

0.09

39,808

11,193

0.1

35,571

8,377 .

Figure 2.115 shows that the cost of the double sampling plan is less than that of the as-is plan. Therefore, the cost of the double sampling was compared with that of the new single sampling plan the one with minimum cost was chosen. Page | 114

Comparison between Costs of the Beans New Single Sampling and Double Sampling Plans

Cost

60000 40000 Double sampling plan

20000

New Single sampling plan

0 0 0.02 0.04 0.06 0.08 0.1

Lot fraction defective, p Figure 2.116: Comparison between costs of the beans as-is and double sampling plans.

p

Double Sampling

New Single Sampling

0.01

25,244

24,194

0.02

40,750

41,761

0.03

44,610

48,798

0.04

40,747

46,811

0,05

33,696

39,632

0.06

26,354

30,752

0.07

20,011

22,386

0.08

14,993

15,545

0.09

11,193

10,445

0.1

8,377

6,889

Table 2.54: Comparison between the costs of the beans new single sampling and double sampling plans at different values of p.

As figure 2.116 shows, the double sampling plan gives a lower cost for most values of p. Therefore, the double sampling plan should be implemented.

Page | 115

2.7 Proposed New Single Sampling Plan for Tin Sheets As with the case of the beans, a new single sampling plan was developed using the nomograph and setting α to 5% and β to 10%: Therefore, the same plan of n = 70 and c = 2 was used. Probablity of Acceptance for the New Tin Sheets Single Sampling Plan 1.00

Pa

0.80 0.60 0.40 0.20 0.00 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.117: Probability of acceptance for the new tin sheets single sampling plan. Table 2.55: Probability of acceptance for the new tin sheets single sampling plan at different values of p.

p

Pa

0.01

0.9667

0.02

0.8350

0.03

0.6492

0.04

0.4656

0.05

0.3137

0.06

0.2013

0.07

0.1241

0.08

0.0740

0.09

0.0428

0.1

0.0242

Page | 116

AOQ for the New Tin Sheets Single Sampling Plan 2.50%

AOQ

2.00% 1.50% 1.00% 0.50% 0.00% 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.118: AOQ for the new tin sheets single sampling plan.

Table 2.56: AOQ for the new tin sheets single sampling plan at different values of p.

p

AOQ

0.01

0.97%

0.02

1.67%

0.03

1.95%

0.04

1.86%

0.05

1.57%

0.06

1.21%

0.07

0.87%

0.08

0.59%

0.09

0.39%

0.1

0.24%

Page | 117

ATI

ATI for the New Tin Sheets Single Sampling Plan 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p

Figure 2.119: ATI for the new tin sheets single sampling plan.

Table 2.57: ATI for the new tin sheets single sampling plan at different values of p.

p

ATI

0.01

14,073

0.02

69,367

0.03

147,366

0.04

224,498

0.05

288,253

0.06

335,467

0.07

367,887

0.08

388,935

0.09

402,011

0.1

409,846

Page | 118

Comparison between the New Single Sampling and As-Is Plans For Tin Sheets Comparison between the Probability of Acceptance for the Tin Sheets As-Is and New Single Sampling Plans 1.00

Pa

0.80 0.60

New Single Sampling Plan

0.40 0.20

As is Plan

0.00 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.120: Comparison between the probability of acceptance for the tin sheets as-is and new single sampling plans.

Table 2.58: Comparison between the probability of acceptance for the tin sheets as-is and new single sampling plans at different values of p.

New Single p

As-Is

Sampling

0.01

0.9044

0.9667

0.02

0.8171

0.8350

0.03

0.7374

0.6492

0.04

0.6648

0.4656

0.05

0.5987

0.3137

0.06

0.5386

0.2013

0.07

0.4840

0.1241

0.08

0.4344

0.0740

0.09

0.3894

0.0428

0.10

0.3487

0.0242 Page | 119

Comparison between the AOQ for the Tin Sheets As-Is and New Single Sampling Plans 3.50% 3.00%

AOQ

2.50% 2.00%

New Single Sampling Plan

1.50% 1.00%

As is Plan

0.50% 0.00% 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p

Figure 2.121: Comparison between the AOQ for the tin sheets as-is and new single sampling plans.

Table 2.59: Comparison between the AOQ for the tin sheets as-is and new single sampling plans at different values of p.

New Single p

As-Is

Sampling

0.01

0.90%

0.97%

0.02

1.63%

1.67%

0.03

2.21%

1.95%

0.04

2.95%

1.86%

0.05

2.99%

1.57%

0.06

2.69%

1.21%

0.07

2.42%

0.87%

0.08

2.17%

0.59%

0.09

1.95%

0.39%

0.10

1.74%

0.24%

Page | 120

ATI

Comparison between the ATI for the Tin Sheets AsIs and New Single Sampling Plans 450000 400000 350000 300000 250000 200000 150000 100000 50000 0

New Single Sampling Plan As is Plan

0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p

Figure 2.122: Comparison between the ATI for the tin sheets as-is and new single sampling plans.

Table 2.60: Comparison between the ATI for the tin sheets as-is and new single sampling plans at different values of p.

p

As-Is

New Single Sampling

0.01

40,169

14,073

0.02

76,838

69,367

0.03

110,289

147,366

0.04

140,777

224,498

0.05

168,536

288,253

0.06

193,787

335,467

0.07

216,732

367,887

0.08

237,561

388,935

0.09

256,449

402,011

0.1

273,559

409,846

Page | 121

Total cost

Comparison between Costs of the Tin Sheets As-Is and New Single Sampling Plan 100000 50000 0

New Single sampling Plan 00.02 0.04 0.06 0.080.1

As-is plan

Lot fraction defetive, p

Figure 2.123: Comparison between the costs of the tin sheets as-is and new single sampling plan.

Table 2.61: Comparison between the costs of the tin sheets as-is and new single sampling plans at different values of p.

p

As-Is

New Single Sampling

0.01

22,063

21,414

0.02

40,184

40,373

0.03

54,871

52,073

0.04

72,691

56,058

0.05

75,699

54,657

0.06

71,261

50,598

0.07

67,228

45,887

0.08

63,567

41,634

0.09

60,247

38,271

0.1

57,240

35,831

As figure 2.123 shows, the new single sampling plan has a much better Pa curve, with a value of 96.67% at p=.01 and much higher sensitivity to an increase in p. The total cost of the new plan is also smaller.

Page | 122

2.8 Proposed Double Sampling Plans For Tin Sheets Once again, six different double sampling plans were tested with the best plan chosen based on its total cost. The following table summarizes the paramters of the six proposed plans: Table 2.62: Summary of the parameters of the six proposed tin sheets double sampling plans

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

n1

5

10

10

15

20

35

n2

50

100

150

150

150

55

c1

0

0

0

0

0

0

c2

2

2

2

2

2

2

.

Page | 123

Probablity of Acceptance for the First Proposed Tin Sheets Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.124: Probability of acceptance for the first proposed tin sheets sampling plan.

Table 2.63: Probability of acceptance for the first proposed tin sheets sampling plan at different values of p.

p

Pa

0.01

0.9953

0.02

0.9732

0.03

0.9343

0.04

0.8852

0.05

0.8323

0.06

0.7798

0.07

0.7299

0.08

0.6836

0.09

0.6410

0.10

0.6019

Page | 124

AOQ

AOQ for the First Proposed Tin Sheets Sampling Plan 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.125: AOQ for the first proposed tin sheets sampling plan.

Table 2.64: AOQ for the first proposed tin sheets sampling plan at different values of p.

p

AOQ

0.01

1.00%

0.02

1.95%

0.03

2.80%

0.04

3.54%

0.05

4.16%

0.06

4.68%

0.07

5.11%

0.08

5.47%

0.09

5.77%

0.10

6.02%

Page | 125

ATI for the First Proposed Tin Sheets Sampling Plan 300,000 250,000

ATI

200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.126: ATI for the first proposed tin sheets sampling plan.

Table 2.65: Probability of acceptance for the first proposed tin sheets sampling plan at different values of p.

p

ATI

0.01

1,976

0.02

11,282

0.03

27,618

0.04

48,207

0.05

70,429

0.06

92,506

0.07

113,467

0.08

132,912

0.09

150,781

0.10

167,185

Page | 126

Probablity of Acceptance for the Second Proposed Tin Sheets Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.127: Probability of acceptance for the second proposed tin sheets sampling plan.

Table 2.66: Probability of acceptance for the second proposed tin sheets sampling plan at different values of p.

p

Pa

0.01

0.9731

0.02

0.8863

0.03

0.7833

0.04

0.6899

0.05

0.6109

0.06

0.5440

0.07

0.4863

0.08

0.4353

0.09

0.3898

0.10

0.3488

Page | 127

AOQ

AOQ for the Second Proposed Tin Sheets Sampling Plan 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.128: AOQ for the second proposed tin sheets sampling plan.

Table 2.67: AOQ for the second proposed tin sheets sampling plan at different values of p.

p

AOQ

0.01

0.97%

0.02

1.77%

0.03

2.35%

0.04

2.76%

0.05

3.05%

0.06

3.26%

0.07

3.40%

0.08

3.48%

0.09

3.51%

0.10

3.49%

Page | 128

ATI for the Second Proposed Tin Sheets Sampling Plan 300,000 250,000

ATI

200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.129: ATI for the second proposed tin sheets sampling plan.

Table 2.66: ATI of acceptance for the third proposed tin sheets sampling plan at different values

p

ATI

0.01

11,308

0.02

47,749

0.03

91,018

0.04

130,271

0.05

163,444

0.06

191,512

0.07

215,776

0.08

237,178

0.09

256,302

0.10

273,504

Page | 129

Probablity of Acceptance for the Third Proposed Tin Sheets Sampling Plan 1.00

Pa

0.80 0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.130: Probability of acceptance for the third proposed tin sheets sampling plan.

Table 2.67: Probability of acceptance for the third proposed tin sheets sampling plan at different values of p.

p

Pa

0.01

0.9562

0.02

0.8505

0.03

0.7511

0.04

0.6693

0.05

0.6000

0.06

0.5390

0.07

0.4841

0.08

0.4344

0.09

0.3894

0.10

0.3487

Page | 130

AOQ

AOQ for the Third Proposed Tin Sheets Sampling Plan 4.00% 3.50% 3.00% 2.50% 2.00% 1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.131 : AOQ for the third proposed tin sheets sampling plan.

Table 2.68: AOQ for the third proposed tin sheets sampling plan at different values of p.

P

AOQ

0.01

0.96%

0.02

1.70%

0.03

2.25%

0.04

2.68%

0.05

3.00%

0.06

3.23%

0.07

3.39%

0.08

3.48%

0.09

3.50%

0.10

3.49%

Page | 131

ATI for the Third Proposed Tin Sheets Sampling Plan 300,000 250,000

ATI

200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.132: ATI for the third proposed tin sheets sampling plan.

Table 2.69: ATI of acceptance for the third proposed tin sheets sampling plan at different values

p

ATI

0.01

18,420

0.02

62,796

0.03

104,552

0.04

138,882

0.05

167,986

0.06

193,641

0.07

216,696

0.08

237,553

0.09

256,447

0.10

273,558

Page | 132

Probablity of Acceptance for the Fourth Proposed Tin Sheets Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

Lot fraction defective, p

Figure 2.133: Probability of acceptance for the fourth proposed tin sheets sampling plan.

Table 2.70: Probability of acceptance for the fourth proposed tin sheets sampling plan at different values of p.

p

Pa

0.01

0.9347

0.02

0.7845

0.03

0.6511

0.04

0.5477

0.05

0.4648

0.06

0.3957

0.07

0.3368

0.08

0.2863

0.09

0.2430

0.10

0.2059

Page | 133

AOQ for the Fourth Proposed Tin Sheets Sampling Plan 2.50%

AOQ

2.00% 1.50% 1.00% 0.50% 0.00% 0.0000

0.0200

0.0400

0.0600

0.0800

0.1000

Lot fraction defective, p

Table 2.71: AOQ for the fourth proposed tin sheets sampling plan at different values of p.

p

AOQ

0.01

0.93%

0.02

1.57%

0.03

1.95%

0.04

2.19%

0.05

2.32%

0.06

2.37%

0.07

2.36%

0.08

2.29%

0.09

2.19%

0.10

2.06%

Page | 134

ATI

ATI for the Fourth Proposed Tin Sheets Sampling Plan 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.134: ATI for the fourth proposed tin sheets sampling plan.

Table 2.72: ATI for the fourth proposed tin sheets sampling plan at different values of p.

p

ATI

0.01

27,459

0.02

90,539

0.03

146,555

0.04

189,981

0.05

224,777

0.06

253,820

0.07

278,552

0.08

299,751

0.09

317,938

0.10

333,528

Page | 135

Probablity of Acceptance for the Fifth Proposed Tin Sheets Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.135: Probability of acceptance for the fifth proposed tin sheets sampling plan.

Table 2.73: Probability of acceptance for the fifth proposed tin sheets sampling plan at different values of p.

p

Pa

0.01

0.9135

0.02

0.7236

0.03

0.5645

0.04

0.4482

0.05

0.3601

0.06

0.2905

0.07

0.2343

0.08

0.1887

0.09

0.1516

0.10

0.1216

Page | 136

AOQ for the Fifth Proposed Tin Sheets Sampling Plan 2.00%

AOQ

1.50% 1.00% 0.50% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.136: AOQ for the fifth proposed tin sheets sampling plan.

Table 2.74: AOQ for the fifth proposed tin sheets sampling plan at different values of p.

p

AOQ

0.01

0.91%

0.02

1.45%

0.03

1.69%

0.04

1.79%

0.05

1.80%

0.06

1.74%

0.07

1.64%

0.08

1.51%

0.09

1.36%

0.10

1.22%

Page | 137

ATI for the Fifth Proposed Tin Sheets Sampling Plan 400,000 350,000 300,000

ATI

250,000 200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.137: ATI for the fifth proposed tin sheets sampling plan.

Table 2.75: ATI for the fifth proposed tin sheets sampling plan at different values of p.

p

ATI

0.01

36,383

0.02

116,108

0.03

182,929

0.04

231,777

0.05

268,765

0.06

297,999

0.07

321,588

0.08

340,745

0.09

356,311

0.10

368,940

Page | 138

Probablity of Acceptance for the Sixth Proposed Tin Sheets Sampling Plan 1.00 0.80

Pa

0.60 0.40 0.20 0.00 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p

Figure 2.138: Probability of acceptance for the sixth proposed tin sheets sampling plan.

Table 2.76: Probability of acceptance for the sixth proposed tin sheets sampling plan at different values of p.

p

Pa

0.01

0.9506

0.02

0.7794

0.03

0.5696

0.04

0.3876

0.05

0.2533

0.06

0.1624

0.07

0.1035

0.08

0.0662

0.09

0.0426

0.10

0.0277

Page | 139

AOQ

AOQ for the Sixth Proposed Tin Sheets Sampling Plan 1.80% 1.60% 1.40% 1.20% 1.00% 0.80% 0.60% 0.40% 0.20% 0.00% 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.139: AOQ for the sixth proposed tin sheets sampling plan.

Table 2.77: AOQ for the sixth proposed tin sheets sampling plan at different values of p.

p

AOQ

0.01

0.95%

0.02

1.56%

0.03

1.71%

0.04

1.55%

0.05

1.27%

0.06

0.97%

0.07

0.72%

0.08

0.53%

0.09

0.38%

0.10

0.28%

Page | 140

ATI

ATI for the Sixth Propsed Tin Sheets Sampling Plan 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 0.00

0.02

0.04

0.06

0.08

0.10

Lot fraction defective, p Figure 2.140: ATI for the sixth proposed tin sheets sampling plan.

Table 2.78: ATI for the sixth proposed tin sheets sampling plan at different values of p.

p

ATI

0.01

20,796

0.02

92,709

0.03

180,801

0.04

257,219

0.05

313,617

0.06

351,813

0.07

376,531

0.08

392,202

0.09

402,093

0.1

408,368

Page | 141

Comparison between the Proposed Double Sampling Plans for Tin Sheets Comparison between the Probablity of Acceptance for the Proposed Beans Double Sampling Plans 1.00

Pa

0.80

Plan 1

0.60

Plan 2

0.40

Plan 3

0.20

Plan 4

0.00

Plan 5 0

0.02

0.04

0.06

0.08

0.1

Plan 6

Lot fraction defective, p Figure 2.141: Comparison between the probability of acceptance for the proposed tin sheets sampling plans. Table 2.79: Comparison between the probability of acceptance for the proposed tin sheets double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

0.9953

0.9731

0.9562

0.9347

0.9135

0.9506

0.02

0.9732

0.8863

0.8505

0.7845

0.7236

0.7794

0.03

0.9343

0.7833

0.7511

0.6511

0.5645

0.5696

0.04

0.8852

0.6899

0.6693

0.5477

0.4482

0.3876

0.05

0.8323

0.6109

0.6000

0.4648

0.3601

0.2533

0.06

0.7798

0.5440

0.5390

0.3957

0.2905

0.1624

0.07

0.7299

0.4863

0.4841

0.3368

0.2343

0.1035

0.08

0.6836

0.4353

0.4344

0.2863

0.1887

0.0662

0.09

0.6410

0.3898

0.3894

0.2430

0.1516

0.0426

0.1

0.6019

0.3488

0.3487

0.2059

0.1216

0.0277

Page | 142

Comparison between the AOQ for the Proposed Tin Sheets Double Sampling Plans 7.00%

AOQ

6.00% 5.00%

Plan 1

4.00%

Plan 2

3.00%

Plan 3

2.00%

Plan 4

1.00%

Plan 5

0.00% 0

0.02

0.04

0.06

0.08

0.1

Plan 6

Lot fraction defective, p Figure 2.142: Comparison between the AOQ for the proposed tin sheets sampling plans.

Table 2.80: Comparison between the AOQ for the proposed tin sheets double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

1.00%

0.97%

0.96%

0.93%

0.91%

0.95%

0.02

1.95%

1.77%

1.70%

1.57%

1.45%

1.56%

0.03

2.80%

2.35%

2.25%

1.95%

1.69%

1.71%

0.04

3.54%

2.76%

2.68%

2.19%

1.79%

1.55%

0.05

4.16%

3.05%

3.00%

2.32%

1.80%

1.27%

0.06

4.68%

3.26%

3.23%

2.37%

1.74%

0.97%

0.07

5.11%

3.40%

3.39%

2.36%

1.64%

0.72%

0.08

5.47%

3.48%

3.48%

2.29%

1.51%

0.53%

0.09

5.77%

3.51%

3.50%

2.19%

1.36%

0.38%

0.1

6.02%

3.49%

3.49%

2.06%

1.22%

0.28%

Page | 143

ATI

Comparison between the ATI for the Proposed Beans Double Sampling Plans 500,000 400,000 300,000 200,000 100,000 0

Plan 1 Plan 2 Plan 3 Plan 4 0

0.02

0.04

0.06

0.08

Lot fraction defective, p

0.1

Plan 5 Plan 6

Figure 2.143: Comparison between the ATI for the proposed tin sheets sampling plans.

Table 2.81: Comparison between the ATI for the proposed tin sheets double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

1,976

11,308

18,420

27,459

36,383

20,796

0.02

11,282

47,749

62,796

90,539

116,108

92,709

0.03

27,618

91,018

104,552 146,555 182,929 180,801

0.04

48,207

130,271 138,882 189,981 231,777 257,219

0.05

70,429

163,444 167,986 224,777 268,765 313,617

0.06

92,506

191,512 193,641 253,820 297,999 351,813

0.07

113,467 215,776 216,696 278,552 321,588 376,531

0.08

132,912 237,178 237,553 299,751 340,745 392,202

0.09

150,781 256,302 256,447 317,938 356,311 402,093

0.1

167,185 273,504 273,558 333,528 368,940 408,368

Page | 144

Comparison between the Costs of the Proposed Tin Sheets Double Sampling Plans Total Cost

150000 Plan 1 100000

Plan 2 Plan 3

50000

Plan 4

0 0

0.02

0.04

0.06

0.08

0.1

Plan 5 Plan 6

Lot fraction defective, p

Figure 2.144: Comparison between the costs of the proposed tin sheets sampling plans.

Table 2.82: Comparison between the costs of the proposed tin sheets double sampling plans at different values of p.

p

Plan 1

Plan 2

Plan 3

Plan 4

Plan 5

Plan 6

0.01

21,114

21,345

21,522

21,747

21,968

21,581

0.02

41,843

40,921

40,540

39,838

39,191

39,783

0.03

61,109

56,325

55,304

52,134

49,389

49,550

0.04

78,202

67,894

66,812

60,394

55,143

51,948

0.05

92,943

76,594

75,796

65,814

58,082

50,199

0.06

105,488

83,120

82,639

69,043

59,063

46,905

0.07

116,126

87,881

87,627

70,550

58,669

43,501

0.08

125,156

91,141

91,019

70,729

57,355

40,568

0.09

132,829

93,113

93,058

69,914

55,471

38,240

0.1

139,334

93,986

93,963

68,383

53,279

36,461

Figure 2.144 shows that Plan 6 was the clear winner when it came to minimizing the total cost of sampling and it was therefore chosen as the best double sampling plan. Page | 145

Comparison between the As-Is and Double Sampling Plans for Tin Sheets

Pa

Comparison between the Probability of Acceptance for the Tin Sheets As-Is and Double Sampling Plans 1.00 0.80 0.60 0.40 0.20 0.00

Double Sampling Plan As is Plan 0

0.02 0.04 0.06 0.08 0.1

Lot fraction defective, p Figure 2.145: Comparison between the probability of acceptance for the tin sheets as-is and double sampling plans. Table 2.83: Comparison between the probability of acceptance for the tin sheets as- is and double sampling plans at different values of p.

p

As-Is

Double Sampling

0.01

0.9044

0.9506

0.02

0.8171

0.7794

0.03

0.7374

0.5696

0.04

0.6648

0.3876

0.05

0.5987

0.2533

0.06

0.5386

0.1624

0.07

0.4840

0.1035

0.08

0.4344

0.0662

0.09

0.3894

0.0426

0.10

0.3487

0.0277

Page | 146

Comparison between the AOQ for the Tin Sheets As-Is and Double Sampling Plans 3.50% 3.00%

AOQ

2.50% 2.00% 1.50%

Double Sampling Plan

1.00%

As is Plan

0.50% 0.00% 0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.146: Comparison between the AOQ for the as-is and new tin sheets sampling plans.

Table 2.84: Comparison between the AOQ for the tin sheets as- is and double sampling plans at different values of p.

Double p

As-Is

Sampling

0.01

0.90%

0.95%

0.02

1.63%

1.56%

0.03

2.21%

1.71%

0.04

2.95%

1.55%

0.05

2.99%

1.27%

0.06

2.69%

0.97%

0.07

2.42%

0.72%

0.08

2.17%

0.53%

0.09

1.95%

0.38%

0.10

1.74%

0.28%

Page | 147

ATI

Comparison between the ATI for Tin Sheets As-Is and Double Sampling Plans 450,000 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0

Double Sampling Plan As is Plan

0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p

Figure 2.147: Comparison between the ATI for the as-is and new tin sheets sampling plans.

Table 2.85: Comparison between the ATI for the tin sheets as- is and double sampling plans at different values of p

p

As-Is

Double Sampling

0.01

40,169

20,796

0.02

76,838

92,709

0.03

110,289

180,801

0.04

140,777

257,219

0.05

168,536

313,617

0.06

193,787

351,813

0.07

216,732

376,531

0.08

237,561

392,202

0.09

256,449

402,093

0.1

273,559

408,368 .

Page | 148

Total cost

Comparison between Costs of the Tin Sheets As-Is and Double Sampling Plans 80000 70000 60000 50000 40000 30000 20000 10000 0

Double sampling plan As-is plan

0

0.02

0.04

0.06

0.08

0.1

Lot fraction defective, p Figure 2.148: Comparison between the costs of the tin sheets as-is and double sampling plans.

Table 2.86: Comparison between the costs of the tin sheets as- is and double sampling plans at different values of p.

p

As-Is

Double Sampling

0.01

22,063

21,581

0.02

40,184

39,783

0.03

54,871

49,550

0.04

72,691

51,948

0.05

75,699

50,199

0.06

71,261

46,905

0.07

67,228

43,501

0.08

63,567

40,568

0.09

60,247

38,240

0.1

57,240

36,461

Page | 149

Figure 2.148 shows that the cost of the double sampling plan is less than that of the as-is plan. Therefore, the cost of the double sampling was compared with that of the new single sampling plan the one with minimum cost was chosen.

Total cost

Comparison between Costs of the Tin Sheets New Single Sampling and Double Sampling Plans 100000 0 00.02 0.04 0.06 0.080.1

Double sampling plan

Lot fraction defective, p

Table 2.87: Comparison between the costs of the tin sheets new single sampling and double Figure 2.149: Comparison between the costs of the tin sheets new single sampling and double sampling plans. sampling plans at different values of p.

p

Double Sampling

New Single Sampling

0.01

21,581

21,414

0.02

39,783

40,373

0.03

49,550

52,073

0.04

51,948

56,058

0.05

50,199

54,657

0.06

46,905

50,598

0.07

43,501

45,887

0.08

40,568

41,634

0.09

38,240

38,271

0.1

36,461

35,831

Page | 150

The double sampling plan has a lower cost as can be seen from figure 2,149 and therefore it was chosen as the best.

2. 9 Conclusion The overfilling problem was exposed and the root cause analysis indicated that the problem was with the culture in the factory rather than the process itself. By eliminating overfilling, the company can save around 68,000 KD per year. Also, the new quality control documentation will help the company track quality characteristics of their products and therefore facilitate future quality control efforts. Furthermore, by changing the timing of the PH test, defects can be detected sooner, thus minimizing cumulative costs of poor quality. Finally, the new sampling plans developed will ensure better relationships with suppliers as the chances of rejecting lots of good quality have been reduced and will also save money by reducing the overall sampling cost.

Page | 151

Page | 152

3. Cost Analysis

Page | 153

Page | 154

3.1 Introduction "Emerging technologies are revealing unprecedented opportunities for bringing new and improved products and systems into being that will be more cost effective in private and public sectors world-wide." (Fabrycky, Life –Cycle Cost and Economic Analysis) In these times of intensifying international competition, producers are searching for ways to gain sustainable competitive advantage in the marketplace. Hence, economic competitiveness is desired by corporations. Moreover, analyzing the costs of the company may help find areas of waste to be eliminated, therefore helping them generate more profit. The National Canned Food Company owns the only factory in Kuwait that fills canned food. It produces 35,869,495 cans, in twenty two different varieties, to satisfy the demand of local customers, as well as that of regional and international markets.

3.1.1 Problem Description After analyzing the costs of The National Canned Food Company, two main problems came to attention: 

High costs due to overfilling: The National Canned Food Company tends to overfill a lot of their products which significantly increases their material costs.



Transportation Costs: It was noticed that transportation costs are obscenely high due to high costs of sending to certain markets with comparatively low demand.

Page | 155

3.1.2 Objectives The main objective of cost analysis is to show substantial long-term gains and cost savings by eliminating areas of “waste.” As such, the objectives are as follows: a. Finding current costs of the company. b. Find the cost of overfilling. c. Try to minimize the transportation costs. d. Find the productivity of the system.

3.1.3 Solution Approach The variable and fixed costs were found for the process. Using them, the total cost, total revenue, and total profit of the company were calculated. The breakeven point for the company, as well as the breakeven point for each of the twenty-two varieties, separately, was found. This would help the company decide whether the demand is worth covering or not for a certain product, as well as offering a clear understanding of the current situation and standing of the company regarding how and where their money is being spent. After that, the cost of overfilling was found, and the alternatives for sending demand to local or regional areas that would cost less to ship to than the international markets, therefore maintaining revenue, and at the same time lowering their transportation costs. Moreover, the company’s current productivity level and level that what would be achieved by taking the project’s analysis and suggestions into consideration were found.

Page | 156

3.2 Analysis of As-Is System: 3.2.1 System Every system has resources going into it, with the decisions being made. The system also gets resource and system outputs. And overall, there would be a value or outcome to that output. In the case of the National Canned Food Company, the system is classified as follows:

Decisions

Resource Input

National Canned

Resource Output

Food Company Labor

Labor

Material

Material

Equipment

Equipment

Energy

Energy

Capital Other

Capital

System Output

Other

Outcome

Figure 3.1: The National Canned Food Company’s system.

Page | 157

1. Suppliers The National Canned Food Company imports all their material from numerous suppliers worldwide. 





Carton Suppliers: 

Carton Industries Company (Kuwait).



Arabian Packaging Company (UAE).



CeaserPac (Kuwait).



Interpack Company (Kuwait).

Labels Suppliers: 

British Industries Press (Kuwait).



Ms Shahid Printing Press (Kuwait).



Integrated Plastic Packaging (UAE).

Aluminum Lids Supplier: 





Glue Suppliers: 

Henkels Ashawa Adhesives (Saudi Arabia).



Al Hashmi Trd. (Kuwait).

Master Batch Supplier: 



Express Flexi-Pack (UAE).

Calrient (Kuwait).

Mushroom Suppliers: 

Welton International Group Ltd (China).



Xiamen Continent Economic Development Ltd (China).



Xiamen Gulong Imp & Exp Co. (China).



Xiamen Huilon Imp & Export Trading Co. (China).

Page | 158



Frozen Sweet Corn Supplier: 



Sweet Kernal Corn Supplier: 









Intralox Inc. (The Netherlands).



Carnaid Metalbox Engineering (England).



Soudronic AG (Switzerland).

Electrolytic Tinplate Suppliers: 

Containers Printers (Singapore).



Pacmetal Services (Australia).



Mitsui & Co Ltd (Japan).



Peter Cremer (Germany).



Al Rajhi Co. for Ind. & Trading (KSA).

Soudronic Wire Supplier:

Gulf Closures W.L.L (Bahrain).

Etimelt 103 Supplier: 



Holden Surface Coatings Ltd. (England).

White Wing Lok Closure Supplier: 



Asia Countries W.L.L (Kuwait).

Lacquer and Thinner Supplier: 



Lamex Foods (The Netherlands).

Spare Parts Suppliers:

 

Mirelite Foreign Trade (Hungary).

National Adhesives Limited (KSA).

Seaming Chucks and Seaming Rolls Supplier: 

T.A.J Engineers Ltd. (England).

Page | 159







Can Ends Suppliers: 

A.C.P International (Italy).



Mivisa Envases S.A. (Spain).



Impress Metal Packaging Capolo SPA (Italy).



Al Rajhi Co. for Ind. & Trading (KSA).

Flavors and Ingredients Suppliers: 

Ali Abdulkarim Trading Co. (Oman).



Tuncsan Salca Konserve Gisa San (Turkey).



Proguimac Color (Spain).



Crestar UK Ltd. (UK).



Food Specialties (UAE).



Leverbrook Ltd (England).



Aralco (France).

Beans and Peas Suppliers: 

Pars Ram Brothers (Australia).



Muelle SA (Peru).



Midgulf International (Jordan).



Rizhao Sunway International (China).



Lamex Foods (The Netherlands).



P.S. International Ltd (USA).



Pars Ram Brothers (Australia).



Peters Commodities Ltd (Australia).



The Great Canadian Bean (Canada).



KBC Trading and Processing Co. (USA).



Export Packets Company Ltd (Canada).



Anny Frantzen (Denmark).

Page | 160

2. Customers 

Local supermarkets (e.g. Co-ops).



Whole sale stores (e.g. Sultan Centers).



Small stores.



Regional and international markets.

3. Missions and Goals of The National Canned Food Company 

Provide the local, regional, and international markets with their demand for canned food, maintaining high quality standards and reasonable prices.



Satisfy all of their customers’ demand, without any delays.

4. Resources 

Labor Maintenance, engineers, laborers, machine operators, forklift operators, quality control, assistant operators, supervisors, technicians, sales person, accountant, secretary, data entry workers, messenger, invoice collector, senior accountant, assistant general manager, store keeper, assistant store keeper, watchman, transportation person.



Materials Baked beans, black eye beans, broad beans, chick peas, chick peas 10mm, chick peas with chili, green peas, hummus tehinah - chick peas 7mm, hummus tehinah with garlic, lima beans, mixed

vegetables, mushroom

pieces and

stems, whole

mushrooms, peas and carrots, peeled fava beans with chili, red kidney beans, red kidney beans with chili, sweet corn, fava beans, white beans.

Page | 161



Equipments Container and Product Technology, Electric Control Cabinet, Line Control Equipment, Labeler, Case Packer, Treadle Operated Case Stapler,

Hand Case Taper,

Crate Loader,

Crate Un-loader, Crate Frasers Horizontal Retorts, Associated Equipment for Retorts, MetaMatic Slat Chain Conveyor, MetaMatic Filled Can Washer, MetaMatic Gravity Changepart Twist, MetaMatic Slat Chain Conveyor, Incline Filled Can Magnetic Elevator, MetaMatic Gravity Roller Conveyor, Pea and Bean Filler, Cannery Seamer, MetaMatic No.1 De-palletizer, MetaMatic Vertical Magnetic Elevator and Change Parts Twist, MetaMatic Empty Can Cable Conveyor, MetaMatic Empty Can Rinse and Change Part Twist, Can Opening System, 2000 L Storage Tank, 900 L open Top Tank, 3000L Steam Jacketed Mixing Tanks,

Alpha Laval Plate Heat Exchanger, Ancillary

Equipment, C.I.P. Plant, Hot Water Rotary Blancher, Vibrator De-Watering Elevator,

Screen,

Inspection

Buffer Storage Hopper,

Gooseneck Elevator,

Conveyor,

Gooseneck

Intake Sack Tip Hopper,

Pneumatic Separator with Vibrator

Feeder, Belt Distribution Conveyor, Soaking Tanks, Flumes, Suction Tank and Buffer Storage Hopper, Vibrator De-Watering Screen. 

Energy Electricity, petrol, water.



Capital Land, building, capital (money).



Other maintenance, insurance, marketing, transportation.

Page | 162

5. Output 

Number of cans.



Revenue from sales.

6. Outcome 

Customer satisfaction.



Profit.



Assure canned food availability.

7. Performance Measures Performance measures are set to have some standards to adhere to. Meeting their performance measures allows The National Canned Food Company to fulfill their objectives. 

Utilization of machines (number or busy machines per hour).



Can production rate.



Can filling rate.



Amount of waste.



Number of defects.



Machine breakdowns.

8. Decisions The National Canned Food Company Should Consider What should the working hours of the workers in the office be? What should the working hours of the workers in the factory be? What are the operating hours of the factory? How many workers should the factory have? How many office workers should they have? Page | 163

How many hours is one shift? How many shifts are there during the day? What are the working hours of the workers in the factory? What should the salaries/wages of all labor Involved? What should the price of the products be? What variety of products should the company offer? How many of the products should they produce? What quality standards of production should the company maintain? What facility layout is appropriate for the factory? Delivery Decisions. Storage Decisions.

3.2.2 Productivity Indices The productivity indices used to calculate The National Canned Food Company’s productivity are the inputs and outputs of the company explained in the previous section (labor, material, equipment, energy, other). The numerical values for those inputs and outputs may be obtained by classifying the costs as direct costs, indirect costs, technical overheads, company overheads, and marketing overheads. And from that, the total cost and total revenue of The National Canned Food Company was calculated.

1. Direct Cost A direct cost is a cost that is directly attributable to the manufacture of a product (or provision of a service). A good example of a direct cost is the cost of the materials needed to make a product. The usage of the materials is directly related to the manufacture of the product. Direct costs are very often variable costs and vice-versa, but the two are not synonymous. There are three types of direct cost: 

Direct materials,



Direct labour, and



Direct expenses (mainly equipment).

Page | 164

Direct Labor Costs The direct labor costs include most of the labor in the can filling plant. They include all of the machine operators and the forklift operators, since those laborers are necessary for the production line.

Table 3.2: Direct labor costs.

Designation

Salary (KD/month)

Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Machine Operator Forklift Operator Forklift Operator Forklift Operator

248 195 150 135 225 150 180 113 135 135 120 105 105 165 Total

2160

Page | 165

Direct Material Cost

(1) Can Making Direct Material Cost: The National Canned Food Company produces the cans to be filled. Each can requires all of the materials listed in table 3.2. Also given are the cost of each of the materials individually, the quantity they require of each material annually, and their annual production. To obtain the direct cost of each can, certain calculations were used to convert the indiscrete units to cost/unit.

Page | 166

Table 3.3: Can making costs.

Order Quantity Per Year

Usage per year

Usage Quantity * Per can

Cost/can**

Cost*** (KD/Year)

Description

Unit

Cost (KD/unit)

Labels

PCS

0.0048

35,869,496

35,869,496

1

0.0048

172,173.58

Copper Wires

K.G

3.783

85,000

35,869,496

0.00237

0.0089646

321,555.00

Standard Lids

PCS

0.009

48,851,442

35,869,496

1.36192

0.0122573

439,662.98

Easy Open Lids

PCS

0.017

17,283,814

35,869,496

0.48185

0.0081915

293,824.84

Tin Sheets

PCS

0.56

1,303,796

35,869,496

0.03635

0.0203551

730,125.76

Cartons

PCS

0.018

2,880,000

35,869,496

0.08029

0.0014452

51,840.00

Shrink Film

PCS

0.96

28,234

35,869,496

0.00079

0.0007556

27,104.64

Glue

K.G

1.5

27,002

35,869,496

0.00075

0.0011292

40,503.00

Lacquer

K.G

1.2

24,714

35,869,496

0.00069

0.0008268

29,656.80

*Quantity per unit = Quantity per year / Annual Production **Cost/can = Quantity per unit * cost of material (KD/unit) ***Cost (KD/year) = cost (KD/unit) * Quantity per year

Page | 167

(2) Can Filling Direct Material Cost: (a) Beans Direct Cost The National Canned Food Company produces different types of products, including water, vinegar, ketchup and sausages which will not be included in this study since they are produced in a different line. The products presented in the table below, are the ones being considered. They are all considered to be direct costs. Given the cost in KD/ton and the quantity in kg/year, the cost in KD/year was calculated.

Table 3.4: Cost of direct material cost for the beans.

Description Baked Beans Black Eye Beans Broad Beans Chick Peas Chick Peas 10mm Chick Peas with Chili Fava Beans Fava Beans with Chili Green Peas Hummus Tehinah Hummus Tehinah with Garlic Lima Beans Mixed Vegetables Mushroom Pieces and Stems Whole Mushrooms Peas and Carrots Peeled Fava Beans with Chili Red Kidney Beans Red Kidney Beans with Chili Sweet Corn Fava Beans White Beans TOTAL

Production Cans/Year 3,489,494 494,928 4,949,942 6,581,088 856,454 46,080 5,284,656 66,960 7,272,720 3,925,008 27,014 94,464 351,936 182,534 234,864 51,264 230,918 772,934 21,600 631,238 174,027 129,370 35,869,496

Cost KD/year 83,107.30 14,005.60 116,117.50 126,880.54 77,293.75 888.40 74,886.58 948.86 52,628.31 29,292.47 201.61 23,661.92 32,008.32 35,191.80 43,989.75 963.37 4,632.81 48,588.55 1,357.83 40,057.24 3,491.42 26,165.11 836,359.04

Page | 168

(b) Additives Direct Cost: Each can is filled with the raw materials and certain additives. The exact ingredients and recipe of each product were considered confidential by The National Canned Food Company. Given the cost of their annual order of additives and the ingredients label on each can, the cost of each product with its respective additives were obtained, as is shown in table 3.6. Since ratios were used to obtain the relative costs, the following example on the broad beans will demonstrate how the costs were obtained in table 3.4. To make broad beans, only two additives were used; EDTA and citric acid: Annual Production of broad beans = 4,949,942 cans/year Annual Cost of EDTA = 2,400 KD/year Productions and annual production rates of different variety that include EDTA: Table 3.5: Sample of additive calculation for broad beans.

Description Black Eye Beans Broad Beans Chick Peas Chick Peas 10mm Chick Peas with Chili Fava Beans Fava Beans with Chili Lima Beans Peeled Fava Beans with Chili Foul Medames White Beans TOTAL



Annual Production 494,928 4,949,942 6,581,088 856,454 46,080 5,284,656 66,960 94,464 230,918 174,027 129,370 18,908,887

EDTA* KD/year 62.82 628.27 835.30 108.70 5.85 670.75 8.50 11.99 29.31 22.09 16.42 2,400.00

EDTA cost = (Annual cost of EDTA / Total can production using EDTA) * Broad bean annual production = (2400/ 18,908,887)*4,949,942 = 628.27 KD/year (EDTA use for broad beans)

The same procedure was done to obtain the figures for the citric acid. Page | 169

Table 3.5: Annual cost of additives.

Description

Unit

Cost per

Order Quantity

Unit

(Unit/year)

Cost (KD/year)

Given Tomato Paste

K.G

0.650

24,000

15,600.000

Lemon Juice

Ltr

2.900

6,000

17,400.000

Green Color

K.G

5.500

350

1,925.000

EDTA

K.G

1.000

2,400

2,400.000

Citric Acid

K.G

0.868

23,500

20,398.000

Camon Powder

K.G

1.500

1,950

2,925.000

Chick Peas

K.G

0.650

5,205

3,383.250

Spices

K.G

2.000

600

1,200.000

Whole Red Chili

K.G

1.650

819

1,351.350

Onion Powder

K.G

2.250

470

1,057.500

Powdered Red

K.G

0.950

624

592.800

Powder

Chili Total

68,232.900

Page | 170

Page | 171

Table 3.6: Direct material cost of additives

Page | 172

Table 3.6: Direct material cost of additives (continued).

(3) Total Direct Cost of Materials: The cost of materials is the total cost of both the beans and the additives of each product. The cost of the beans, shown in Table 3.3, and the cost of the total additives, from Tables 3.5 and 3.6, is added to give us the total cost, in KD, for each type. Then the following equation was used to give us the direct cost in KD/unit: Direct cost = Total cost (KD/Year) / Production (Units/Year) Table 3.7: Direct costs of materials (beans and additives).

Description

Annual Production Cans/year

Cost of Beans ( KD/year)

Total Cost

Direct Cost* (KD/unit)

83,107.3 14,005.6 116,117.5 126,880.54 77,293.75 888.4 74,886.58 948.86 52,628.31 29,292.47

Total Additive Cost (KD/year) 16,674.33 62.82 7,274.02 835.3 108.7 5.85 7,765.89 416.53 1,911.53 5,269.69

Baked Beans Black Eye Beans Broad Beans Chick Peas Chick Peas 10mm Chick Peas with Chili Fava Beans Fava Beans with Chili Green Peas Hummus Tahineh – Chick Peas 7mm HummusTahineh with Garlic Lima Beans Mixed Vegetables Mushroom Pieces and Stems Whole Mushrooms Peas and Carrots Peeled Fava Beans with Chili Red Kidney Beans Red Kidney Beans with Chili Sweet Corn Foul Medames White Beans TOTAL

34,89,494 494,928 4,949,942 6,581,088 856,454 46,080 5,284,656 66,960 7,272,720 3,925,008

99,781.63 14,068.42 123391.52 127715.84 77402.45 894.25 82,652.47 1365.39 54,539.84 34,562.16

0.0286 0.0284 0.0249 0.0194 0.0904 0.0194 0.0156 0.0204 0.0075 0.0088

27,014

201.61

36.27

237.88

0.0088

94,464 351,936 182,534

23,661.92 32,008.32 35,191.8

11.99 472.51 245.07

23,673.91 32,480.83 35,436.87

0.2506 0.0923 0.1941

234,864 51,264 230,918

43,989.75 963.37 4,632.81

0 13.47 15,481.58

43,989.75 976.84 20,114.39

0.1873 0.0191 0.0871

772,934 21,600

48,588.55 1,357.83

0 696.01

48,588.55 2,053.84

0.0629 0.0951

631,238 174,026 129,369 35,869,495

40,057.24 3,491.42 26,165.11 836,359.04

0 10,898.92 16.42

40,057.24 14,390.34 26,181.53 904,555.9

0.0635 0.0827 0.2024 0.0252

Page | 173

* Direct cost = Total cost (KD/Year) / Production (Units/Year) Total Direct Material Cost: The total material direct cost is the sum of the unit direct cost of each can, bean and additive, as presented in Table 3.8 below. The Total Direct Cost in KD

per

year

was

also

obtained

as shown

the

Table

8

below.

* Total Direct Cost (KD/year) = Total Material Direct Cost * Production (KD/unit)

(KD/Year)

Page | 174

Table 3.8: Total direct material costs.

Total

Total Direct

Material

Cost

Can

Direct Cost *

KD/year

KD/Year

KD/can

KD/unit

0.028594867

99,781.63

0.058725

0.084892

296,230.1586

494,928

0.028425185

14,068.42

0.058725

0.087464

43,288.38259

Broad Beans

4,949,942.4

0.02492787

123,391.52

0.058725

0.082686

409,290.9373

Chick Peas

6,581,088

0.019406493

127,715.84

0.058725

0.078038

513,574.9453

856,454.4

0.090375448

77,402.45

0.058725

0.149229

127,807.8337

46,080

0.019406467

894.25

0.058725

0.08274

3,812.6592

5,284,656

0.015640085

82,652.47

0.058725

0.073366

387,714.0721

66,960

0.020391129

1,365.39

0.058725

0.125798

8,423.43408

7,272,720

0.007499235

54,539.84

0.058725

0.066094

480,683.1557

3,925,008

0.008805628

34,562.16

0.058725

0.066766

262,057.0841

27,014.4

0.008805674

237.88

0.058725

0.150086

4,054.483238

Description

Baked Beans Black Eye Beans

Chick Peas 10mm Chick Peas with Chili Fava Beans Fava Beans with Chili Green Peas

Direct Cost

Direct Cost

Beans +

Beans +

Additives

Additives

Unit/Year

KD/unit

3,489,494.4

Annual Production

Direct Cost

Hummus Tahineh Chick Peas 7mm Hummus Tahineh with Garlic

Page | 175

Table 3.8: Total direct material Costs (continued).

Description

Lima Beans

Total Material Direct Cost *

Total Direct Cost

Direct Cost

Direct Cost

Direct Cost

Beans + Additives

Beans + Additives

Can

Unit/Year

KD/unit

KD/Year

KD/can

KD/unit

94,464

0.250613038

23,673.91

0.058725

0.311521

29,427.51974

351,936

0.092291866

32,480.83

0.058725

0.156114

54,942.1367

Annual Production

KD/year

Mixed Vegetables Mushroom Pieces and Stems Peeled Fava Beans with Chili Red Kidney Beans Red Kidney Beans with Chili

182,534.4

0.194138036

35,436.87

0.058725

0.263937

48,177.58193

230,918.400

0.087

20,114.390

0.059

0.146

33,718.705

772,934.400

0.063

48,588.550

0.059

0.122

93,979.548

21,600.000

0.095

2053.840

0.059

0.529

11,419.099

Sweet Corn

631,238.400

0.063

40,057.240

0.059

0.122

77,126.601

Foul Medames

174,026.900

0.083

14,390.340

0.059

0.164

28,578.698

White Beans

129,369.600

0.202

26,181.530

0.059

0.263

33,980.607

TOTAL

3,011,006.187

Page | 176

Equipment Direct Cost Since the can filling production line is in series, and all the equipment are vital and required to produce each unit of product, all the machines are considered to be direct costs. All the equipment was bought in 1984 and have not been replaced since. The lifespan of all machines is supposedly ten years. However, The National Canned Food Company still uses the same machines, even though it has been 25 years.

Page | 177

Table 3.9: Direct equipment costs.

Process

Machine

Description

Container and Product

Metal box available to

Technology

undertake tests on the

Cost (KWD) 10,226.4

compatibility of container and product Electrical Controls: Electric Control Cabinet

Dry product preparation

8,153.72

Soaking, blanching and product feed Filling, closing, and can handling Crate unloading, can drying , labeling, and case packing Line Control Equipment

To regulate flow of cans

1,329.43

and product Labeling and Case Packing Labeler

Labels the cans

3,573.51

Case Packer

To collate cans in 3*4*2

6,274.92

configuration Treadle Operated Case

797.66

Stapler Hand Case Taper

5.32

Page | 178

Table 3.9: Direct equipment costs (continued).

Process

Machine

Description

Cost (KWD)

Processing Crate Loader

Chain in-feed conveyor

3,589.47

Crate Un-loader

Discharge conveyor

6,593.98

Crate Frasers Horizontal Retorts

Steam retort

34,219.58

Associated Equipment for Retorts

Flat top trucks and crates with

7,147.026

loose bottoms Transporter trucks Filled Can Handling MetaMatic Slat Chain Conveyor

Conveys cans from seamed

1,239.03

discharge to filled can washer MetaMatic Filled Can Washer

Removes any slight traces of

3031.1

sauce or brine adhering to the can MetaMatic Gravity Changepart

From crate un-loader to slat chain

Twist

conveyor

MetaMatic Slat Chain Conveyor

Slat chain conveyor with fixed

204.94

1,239.03

speed drive MetaMatic Alpine Conveyor

Elevates cans to labeler in-feed

MetaMatic Gravity Changepart

Conveys cans to and from the

Twist

labeler and case packer

Incline Filled Can Magnetic

Elevates filled cans to filled can

Elevator

cable conveyor

MetaMatic Gravity Roller Conveyor

For filled case conveying

4,785.96 638.13

3,759.63

265.87

Page | 179

Process

Machine

Description

Cost (KWD)

Filling and Closing Table 3.9: Direct equipment costs (Continued).

Pea and Bean Filler

Solids and liquid twin head filler

29,247.50

for peas and beans Consists of guarding, level control, combined support for level control and/or mixer, duty Cannery Seamer

Closing cans

MetaMatic Vertical Magnetic

Discharge with gravity transfer to

Elevator and Change Parts

cable conveyor

25,331.20

Empty Can Handling 3,456.52

Twist MetaMatic Empty Can Cable

Conveys cans from elevator to

Conveyor

filling area

MetaMatic Empty Can Rinse

Pre-wash can prior to filling

2,233.45

1,967.56

and Change Part Twist Brine & Sauce Prep. Can Opening System

Opens tomato paste cans

439.74

2000 L Storage Tank

Stores vegetable oil

2,197.86

900 L open Top Tank

Premixes sugar, seasoning, etc.

1,475.87

3000L Steam Jacketed

Preheat sauce or brine

12,741.28

Sauce and brine heater

3,929.80

Mixing Tanks Alpha Laval Plate Heat Exchanger Ancillary Equipment

Control panel suitable for

10,770.44

temperature control, etc. C.I.P. Plant

Cleans brine and sauce

5,158.20

preparation equipment

Page | 180

(1) Depreciation:

 The National Canned Food Company use the straight line method to depreciate their equipment.

 Salvage value is assumed to be zero. Table 3.10: Depreciation of machines.

Machine

Life

Cost

Depreciated

Span

(KWD)

Value Per Year

(n) Container and Product Technology

25

10,226.4

409.1

Electric Control Cabinet

25

8,153.7

326.1

Line Control Equipment

25

1,329.4

53.2

Labeler

25

3,573.5

142.9

Case Packer

25

6,274.9

251.0

Treadle Operated Case Stapler

25

797.7

31.9

Hand Case Taper

25

5.3

0.2

Crate Loader

25

3,589.5

143.6

Crate Un-loader

25

6,594.0

263.8

Crate Frasers Horizontal Retorts

25

34,219.6

1,368.8

Associated Equipment for Retorts

25

7,147.0

285.9

MetaMatic Slat Chain Conveyor

25

1,239.0

49.6

MetaMatic Filled Can Washer

25

3,031.1

121.2

MetaMatic Gravity Changepart Twist

25

204.9

8.2

MetaMatic Slat Chain Conveyor

25

1,239.0

49.6

MetaMatic Alpine Conveyor

25

4,786.0

191.4

MetaMatic Gravity Changepart Twist

25

638.1

25.5

Incline Filled Can Magnetic Elevator

25

3,759.6

150.4

MetaMatic Gravity Roller Conveyor

25

265.9

10.6

Pea and Bean Filler

25

29,247.5

1,169.9

Cannery Seamer

25

25,331.2

1,013.2

MetaMatic No.1 De-palletizer

25

7,976.6

319.1

Page | 181

Table 3.10: Depreciation of machines (continued).

Machine

Life Spa n (n)

Cost (KWD)

Depreciate d Value Per Year

MetaMatic Vertical Magnetic Elevator and Change Parts Twist

25

3,456.50

138.3

MetaMatic Empty Can Cable Conveyor MetaMatic Empty Can Rinse and Change Part Twist

25 25

2,233.40 1,967.60

89.3 78.7

Can Opening System 2000 L Storage Tank 900 L open Top Tank 3000L Steam Jacketed Mixing Tanks

25 25 25 25

17.6 87.9 59 509.7

Alpha Laval Plate Heat Exchanger Ancillary Equipment

25 25

Vibrator De-Watering Screen Inspection Conveyor Gooseneck Elevator Buffer Storage Hopper Intake Sack Tip Hopper Gooseneck Elevator Pneumatic Separator with Vibrator Feeder Gooseneck Elevator Belt Distribution Conveyor Suction Tank and Buffer Storage Hopper Vibrator De-Watering Screen Forklift TOTAL

25 25 25 25 25 25 25

439.7 2,197.90 1,475.90 12,741.3 0 3,929.80 10,770.4 0 1,522.10 5,199.10 1,527.40 4,254.20 1,063.50 1,442.70 1,995.40

25 25 25 25 25

1,662.80 5,133.20 2,083.30 1,522.10 18,000

66.5 205.3 83.3 60.9 720 10,469.90

157.2 430.8 60.9 208 61.1 170.2 42.5 57.7 79.8

Page | 182

2. Indirect Costs Indirect costs are those costs that are needed but not essential to produce each part. In the case of the National Canned Food Company, all of the indirect costs are labor costs. Indirect costs are very often variable costs. There are three types of indirect cost: 

Indirect materials,



Indirect labour, and



Indirect expenses (mainly equipment).

The indirect costs of The National Canned Food Company are the following: a) Indirect Material: (none) b)

Indirect Labor: Table 3.11: Indirect labor costs.

Designation

Salary (KD/month)

Quality Controller

375

Quality Controller

270

Quality Controller

270

Quality Controller

255

Assistant Operator

128

Assistant Operator

105

Assistant Operator

173

Assistant Operator

98

Assistant Operator

98

Assistant Operator

98

Assistant Operator

180

Assistant Operator

90

Assistant Operator

90

Assistant Operator

98

Total

2,325 Page | 183



Workers may have the same designation with different salaries based on their work experiences, how hard working they are, and their nationality.



Office workers have no overtime.



Can plant workers are requested to stay overtime depending on the work requirement.



A maximum of 4 overtime hours are allowed per day.



On average, each worker in the National Canned Food Company works 40-50 overtime hours per month.



The overtime for plant workers is as follows: Normal days per hour = Total salary / 30 / 8*1.25 Fridays per hour = Total salary / 30 / 8*1.50 Holidays per hour = Total salary / 30 / 8*1.75



The total overtime cost is 1,750 KD per month.

c) Indirect Equipment: (none)

Page | 184

3. Overheads Overheads are those costs which are incurred in the running of the business and which are not directly associated with a specific job. Overhead costs are always fixed. There are three types of overheads:

Technical Overheads Technical or factory overheads are any expenses related to production but are not included in every unit. Table 3.12: Technical overheads costs.

Designation

Salary (KD/month)

Spare Parts

5,000

Equipment Maintenance

1,458.33

Supervisor

525

Technical

210

Laborer

98

Laborer

180

Laborer

180

Laborer

180

Laborer

180

Laborer

135

Laborer

90

Laborer

150

Laborer

90

Laborer

75

Laborer

75

Laborer

90

Laborer

105

Total

8,821.33

Page | 185

Company Overheads Company overheads are, as the name implies, those expenses that are not related to manufacturing the product but rather related to management and office.

Table 3.13: Company overheads costs.

Designation

Salary (KD/month)

Export and Import Accountant Data Entry 1 Secretary Messenger Invoice Collector Senior Accountant Assistant General Manager Data Entry Store Keeper Assistant Store Keeper Store Keeper Watchman Transportation Insurance Utilities: Water Utilities: Petrol Utilities: Electricity Land Total

451 442 400 255 527 527 680 1,275 170 300 180 105 135 14,880 833 600 2,450 700 500 25,409

Marketing Overheads The Marketing costs are 12,000 KD/year. They mainly use this amount for designs for the labels and posters.

The National

Canned Food Company doesn't advertise in Kuwait. Every year they attend a marketing exhibition in Dubai.

Page | 186

4) Modeling Costs Overview: a) Materials: Direct Materials Cost = 3,011,006.187 KD/year. Indirect Material Cost = 0 KD/year. Total Material Cost = 3,011,006.187 KD/year. b) Labors: Direct Labors Cost = 2,160 KD/year. Indirect Labors Cost = 2,325 KD/year. Total Labors Cost = 4,485 KD/year. c) Equipment: Direct Machine Cost = 10,469.9 KD/year. Indirect Machine Cost = 0 KD/year. Total Machine Cost = 10,469.9 KD/year. Total Direct Cost = 3,013,166.187 KD/year. Total Indirect Cost = 2,325 KD/year.

d) Overheads: Technical Overhead Cost = 8,821.33 KD/year. Company Overhead Cost = 25,409 KD/year. Marketing Overhead Cost = 12,000 KD/year.

Total Overhead Cost = 46,230.33 KD/year.

Page | 187

5. Variable Cost Variable costs are the costs that change according to the production rate. For The National Canned Food Company, the only variable costs are the material costs, utilities, and overtime since these are the costs that change with the production rate. Hence, the total material direct cost + utility cost is the unit variable cost. Multiplying the unit variable cost by the annual production rate will result in the variable cost in KD/year. Table 3.14: Variable costs.

Total Description

Annual

Material

Production

Direct Cost

Unit

Variable

Variable

Cost

Cost

Unit/Year

KD/unit

(KD/unit)

KD/year

Baked Beans

3,489,494

0.0873

0.0873

304,632.83

Black Eye Beans

494,928

0.0872

0.0872

43,157.72

Broad Beans

4,949,942

0.0837

0.0837

414,310.15

Chick Peas

6,581,088

0.0781

0.0781

513,982.97

Chick Peas 10mm

856,454

0.1491

0.1491

127,697.29

Chick Peas with Chili

46,080

0.0781

0.0781

3,598.85

Fava Beans

5284656

0.0744

0.0744

393,178.41

Fava Beans with Chili

66,960

0.0791

0.0791

5,296.54

Green Peas

7,272,720

0.0662

0.0662

481,454.06

Hummus Tahineh - Chick Peas 7mm

3,925,008

0.0675

0.0675

264,938.04

Hummus Tahineh with Garlic

27,014

0.0675

0.0675

1,823.45

Lima Beans

94,464

0.3093

0.3093

29,217.72

Mixed Vegetables

351,936

0.151

0.151

53,142.34

Mushroom Pieces with Stems

182,534

0.2529

0.2529

46,162.85

Whole Mushrooms

234,864

0.246

0.246

57,776.54

Peas and Carrots

51,264

0.0778

0.0778

3,988.34

Peeled Fava Beans with Chili

230,918

0.1458

0.1458

33,667.84

Red Kidney Beans

772,934

0.1216

0.1216

93,988.77

Page | 188

Table 3.14: Variable costs (continued).

Description

Annual Production

Total Material Direct Cost

Unit Variable Cost

Variable Cost

Unit/Year

KD/unit

(KD/unit)

KD/year

Red Kidney Beans with Chili

21,600

0.1538

0.1538

3,322.08

Sweet Corn

631,238

0.1222

0.1222

77,137.28

Foul Medames

174,026

0.1414

0.1414

24,607.28

White Beans

129,369

0.2611

0.2611

33,778.25

TOTAL

35869496

3,010,859

Variable Cost KD/month

Variable Cost

Variable Cost

KD/year

KD/unit

Utility: Water

600

7,200

0.000200728

Utility: Electricity

700

8,400

0.000234182

2,450

29,400

0.000819638

Utilities: Petrol TOTAL

Total Overtime Cost TOTAL VARIABLE COST

45,000

0.001254548

Variable Cost KD/month

Variable Cost

Variable Cost

KD/year

KD/unit

1,750

21,000

3,076,859.58

0.000585456 0.001840004 (Utilities +OT)

Page | 189

6. Fixed Costs Fixed costs are those costs that do not vary or change with the production rate. Therefore, the fixed cost in the case of the National Canned Food Company would be the sum of the overheads and the direct labor and equipment costs. Total Overheads = Technical Overhead + Company Overhead + Marketing Overhead = (8,821.33*12) + (21,659*12) + 12,000 = 105,855.96 + 259,908 + 12,000 = 377,763.96 KD/year Total Equipment Cost = 10,469.9 KD/Year Total Labor Costs = Direct Labor + Indirect Labor = (2160*12) + (2325*12) = 25,920 + 27,900 = 53,820 KD/year

Total Fixed Cost = Total Overheads + Total Equipment Costs + Total Labor Costs = 377,763.96 + 10469.9 + 53,820 = 442,053.86 KD/year

7. Total Cost The total cost is the sum of the variable and fixed cost. Total Cost = Variable Cost + Fixed Cost = 3,076,859.58+ 442,053.86 = 3,518,913.44 KD/year

Page | 190

8. Total Revenue: The total revenue is how much money the company makes from selling their products. The selling price is how much the product is being sold for, and the total revenue per year is obtained from multiplying the selling price by how much is being produced every year of each product. Table 3.15: Total revenue.

Description

Baked Beans

Annual

Selling

Production

Price

Cans/Year

KD/unit

Total Revenue; SP*X KD/Year

3,489,494

0.135

471,081.74

494,928

0.130

64,340.64

Broad Beans

4,949,942

0.120

593,993.09

Chick Peas

6,581,088

0.120

789,730.56

856,454

0.170

145,597.25

46,080

0.135

6,220.80

5,284,656

0.110

581,312.16

66,960

0.120

8,035.20

Green Peas

7,272,720

0.085

618,181.20

Hummus Tahineh - Chick

3,925,008

0.110

431,750.88

Hummus Tahineh with Garlic

27,014

0.120

3,241.73

Lima Beans

94,464

0.330

31,173.12

Mixed Vegetables

351,936

0.185

65,108.16

Mushroom Pieces with

182,534

0.300

54,760.32

234,864

0.300

70,459.20

51,264

0.130

6,664.32

Peeled Fava Beans with Chili

230,918

0.170

39,256.13

Red Kidney Beans

772,934

0.155

119,563.29

21,600

0.170

3,665.25

Sweet Corn

631,238

0.165

104,154.34

Foul Medames

174,027

0.175

30,454.71

White Beans

129,370

0.280

36,223.49

35,869,496

3.714

4,274,967.57

Black Eye Beans

Chick Peas 10mm Chick Peas with Chili Fava Beans Fava Beans with Chili

Peas 7mm

Stems Whole Mushrooms Peas and Carrots

Red Kidney Beans with Chili

TOTAL

Page | 191

9. Total Profit Total profit = Total Revenue – Total Cost = 4,274,967.57- 3,519,056.42 = 755,911.15 KD/Year

Profit Margin = Profit / Revenue = 755,911.15 / 4,323,882.3 = 17.48 %

The following table, Table 3.16, shows how the allocation of the cost, revenues and profits are for each of the products individually.

Page | 192

Table 3.16: Total profit.

Annual Description

Production

Unit Variable

Fixed Cost

Total Cost

Cost

Total Revenue

Total Profit

Cans/Year

(KD/unit)

KD/year

KD/Year

KD/Year

KD/Year

Baked Beans

3,489,494

0.089

43,004.348

354,057.893

471,081.744

117,023.851

Black Eye Beans

494,928

0.089

6,099.468

50,167.859

64,340.640

14,172.781

Broad Beans

4,949,942

0.086

61,002.836

484,420.928

593,993.088

109,572.160

Chick Peas

6,581,088

0.080

81,104.997

607,197.198

789,730.560

182,533.362

Chick Peas 10mm

856,454

0.151

10,554.896

139,828.126

145,597.248

5,769.122

46,080

0.080

567.888

4,251.523

6,220.800

1,969.277

5,284,656

0.076

65,127.834

468,030.029

581,312.160

113,282.131

66,960

0.081

825.212

6,244.954

8,035.200

1,790.246

7,272,720

0.068

89,628.635

584,464.532

618,181.200

33,716.668

3,925,008

0.069

48,371.601

320,531.671

431,750.880

111,219.209

27,014

0.069

332.919

2,206.098

3,241.728

1,035.630

Lima Beans

94,464

0.311

1,164.170

30,555.699

31,173.120

617.421

Mixed Vegetables

351,936

0.153

4,337.242

58,127.141

65,108.160

6,981.019

Chick Peas with Chili Fava Beans Fava Beans with Chili Green Peas Hummus Tahineh Chick Peas 7mm Hummus Tahineh with Garlic

Page | 193

Table 3.16: Total profit (continued).

Description

Annual Production

Unit Variable Cost

Fixed Cost

Total Cost

Total Revenue

Total Profit

Cans/Year

(KD/unit)

KD/year

KD/Year

KD/Year

KD/Year

Mushroom Pieces and Stems

182,534

0.255

2,249.54

48,748.35

54,760.32

6,011.97

Whole Mushroom

234,864

0.248

2,894.45

61,103.15

70,459.20

9,356.05

Peas and Carrots

51,264

0.08

631.775

4,714.44

6,664.32

1,949.88

230,918

0.148

2,845.82

36,938.62

39,256.13

2,317.51

772,934

0.123

9,525.60

104,936.63

119,563.29

14,626.67

21,600

0.156

266.197

3,628.02

3,665.25

37.229

Sweet Corn

631,238

0.124

7,779.35

86,078.16

104,154.34

18,076.18

Foul Medames

174,027

0.143

2,144.69

27,072.30

30,454.71

3,382.41

White Beans

129,370

0.263

1,594.34

35,610.78

36,223.49

612.708

TOTAL

35,869,496

2.942

442,053.80

3,518,914.09

4,274,967.57

756,053.47

Peeled Fava Beans with Chili Red Kidney Beans Red Kidney Beans with Chili

Page | 194

10. Productivity Analysis Results All the previously collected data were used to calculate the productivity of the National Canned Food Company. Total Productivity = Total Output / Total Input = Total Revenue /Total Cost = 42,749,67.57 / 3,518,913.44 = 1.214 > 1

Since the total productivity is greater than 1, it means that The National Canned Food Company is productive.

11. Break Even Point The breakeven point is the point that the company covers its losses and from then on starts making profit. This point is when the total profit is equal to zero. To graphically show the breakeven point, the total cost is plotted against total revenue. The point of intersection is the breakeven point. Figure 3.2 shows the breakeven point for the company as a whole. Appendix V contains the breakeven points for each product on its own. These can be helpful to show the company how much of a certain product should be produced to make a profit out of it.

Page | 195

a) Total Breakeven Point

Table 3.17: Total profit.

Annual Production

Total Cost

Total Revenue

Total Profit

Cans/Year 0 1000000 2000000 3000000 4000000 5000000 6000000 7000000 8000000 9000000 10000000 11000000 12000000 13000000 14000000 15000000 16000000 17000000 18000000 19000000 20000000 21000000 12597389

KD/Year 442053.798 575781.0707 709508.3435 843235.6162 976962.8889 1110690.162 1244417.434 1378144.707 1511871.98 1645599.253 1779326.525 1913053.798 2046781.071 2180508.343 2314235.616 2447962.889 2581690.162 2715417.434 2849144.707 2982871.98 3116599.253 3250326.525 2126668.272

KD/Year 0 168818.182 337636.364 506454.545 675272.727 844090.909 1012909.09 1181727.27 1350545.45 1519363.64 1688181.82 1857000 2025818.18 2194636.36 2363454.55 2532272.73 2701090.91 2869909.09 3038727.27 3207545.45 3376363.64 3545181.82 2126668.31

KD/Year -442053.8 -406962.89 -371871.98 -336781.07 -301690.16 -266599.25 -231508.34 -196417.43 -161326.53 -126235.62 -91144.707 -56053.798 -20962.889 14128.02 49218.929 84309.838 119400.75 154491.66 189582.57 224673.47 259764.38 294855.29 0.0341818

Page | 196

Total Cost

Total Breakeven Point

Total Revenue

4000000 3500000 3000000

2000000 1500000 1000000 500000

0 80 00 00 0 10 00 00 00 12 00 00 00 14 00 00 00 16 00 00 00 18 00 00 00 20 00 00 00

0

60 00 00

40 00 00

0

0 0 20 00 00

KD

2500000

Production Figure 3.2: Total breakeven point.

Page | 197

4. New System A. Overfilling: The National Canned Food Company tends to over fill their products. When over filling, the company is losing money. Depending on how much they over fill and how much they produce of the products they over fill, the company might actually have significant savings if they prevent over filling. In the table on the following page, the annual cost of over filling for each type of product is shown. The over filling is how many grams the product is being overfilled per can. The target is how much the company aims to fill each product. Although we’re working with the 400g cans, almost half of it is filled with brine, and not the problem with increased costs when overfilling. Hence, we’ll only consider the over filling of solid filling (the actually product itself.) The cost per gram is needed to find how much it costs to overfill. This was obtained by the following equation: Cost per gram = Cost per year / (# cans produced per year * target) Then the cost of overfilling in KD per year was obtained using the following equation: Cost of over filling = Amount over filled per year * cost per gram As shown in table 3.40, 68,001.66 KD/year can be saved if they prevent overfilling.

This

represents

about

2.04%

of

their

total

cost.

Given that they tend not to record everything, and that not all variety of products was covered, there is a very big possibility that costs of overfilling are even higher than what was estimated.

Page | 198

Table 3.18: Costs of over filling.

OVER FILLING Overfilling

Production

Target

Overfilling

Annual Cost

Cost Per Gram

Cost of Over Filling

g/can

can/year

g/can

g/year

KD/year

KD/g

KD/year

Baked Beans

-0.17

3,489,494

170

-582,746

83,107

0.0001401

-81.6

Fava Beans

-0.5

75,835

180

-37,918

75,835

0.0055556

-210.7

Green Peas

0.37

7,272,720

188

2,701,088

52,999

0.0000389

105

Hummos Tehina

-1.5

29,494

408

-44,241

29,494

0.002454

-108.6

Mix Vegetables

-2.5

351,936

233

-879,840

32,008

0.0003912

-344.2

Mushroom Pieces

0

182,534

215

0

35,192

0.0008967

0

Mushroom Whole

0

234,864

215

0

43,990

0.0008712

0

Description

TOTAL

67,361.70

Page | 199

B. Transportation Costs The transportation costs of The National Canned Food Company are very high compared to the rest of their costs. It amounts to 14,880 KD/month, which is 178,560 KD/year representing 5.1% of the company’s total cost. Table 3.41 shows the transportation costs and demand for The National Canned Food Company’s different markets. Local transportation costs are considered to be zero since local customers pick up their orders from the warehouse. Transporters for local and regional markets are trucks, while for international markets they are ships. Minimizing

their

transportation

costs

would

lower

their

total

cost.

Table 3.19: Transportation costs of The National Canned Food Company.

1. Transportation Forecast Cost for Year 1: 2009; Avg. Demand

Capacity of

Cost

(transporter/month)

transporter (carton)

(KD/transporter)

Local

28

2100

0

KSA

6

2100

200

UAE

5

2100

300

Bahrain

4

2100

290

Qatar

3

2100

300

Oman

3

2100

400

Iraq

3

2100

150

Tunisia

2

1650

815

USA

3

1650

980

Kenya

3

1650

1300

Totals

122400 cartons/month

14,880 Page | 200

It was noticed that the three markets with the least demand and highest transportation costs were Kenya, USA and Tunisia respectively. Due to increasing yearly demand by approximately 10% annually (see Appendix), the company are barely keeping up with demand, have huge amounts of overtime, and frequent machine breakdowns. Reallocating their demand to local and regional markets seems sensible especially since it costs more than twice the price to ship. Moreover, the amount demanded by each of Tunisia, Kenya, and the US are very small to have any substantial marketing value. The annual costs of the markets to be eliminated and allocated to are represented in tables 3.42 and 3.43. Table 3.20: Annual transportation costs to international markets.

Tunisia USA Kenya Total

Demand Cans/Year 950,400 1,425,600 1,425,600 3,801,600

Shipping Cost KD/year 19,560 35,280 46,800 101,640

Table 3.21: Annual transportation costs to local and regional markets.

Local Regional KSA UAE Bahrain Qatar Oman Iraq Total

Demand Cans/Year 16,934,400.00

Shipping Cost KD/year 0.00

% Total Demand Demand/Total Demand 0.480392157

3,628,800 3,024,000 2,419,200 1,814,400 1,814,400 1,814,400 14,515,200

14,400 18,000 13,920 10,800 14,400 5,400 76,920

0.103 0.086 0.069 0.058 0.051 0.051

In order to produce the 35,869,496 cans annually, The National Canned Food Company is operating their regular 8 hours, and utilizing their maximum overtime of 4 hours. Given these conditions, the maximum capacity the company can produce is 36,691,200 cans annually. Given that the demand is increasing by 10% every year, it can be noticed from Table 3.44 below that The National Canned Page | 201

Food Company won’t be able to cover demand for their local and regional customers. Therefore, it is only sensible to cover the difference in demand by allocating it from the country that is most expensive to send to, Kenya, then the second highest country to send to, US, and last Tunisia. Table 3.22: 2009 shipping costs and allocated demand to local and regional markets.

Local Regional KSA UAE Bahrain Qatar Oman Iraq Total

2009 Demand*

Demand Difference**

Increase 10%

Cans/year

18,627,840

1,693,440

3,991,680 3,326,400 2,661,120 1,995,840 1,995,840 1,995,840 34,594,560

362,880 302,400 241,920 181,440 181,440 181,440 3,144,960

Extra Transporters Needed Yearly***

Allocated Demand****

New Shipping***** Cost KD/year

1,693,440 7 6 5 4 4 4

362,880 302,400 241,920 181,440 181,440 181,440

15,840 19,800 15,312 11,880 15,840 5,940 84,612

* Demand = 2008Demand + (2008 Demand *0.1) ** Demand Difference = 2009 Demand – 2008 Demand *** Extra Trucks Needed Yearly = (Demand Difference/24)/2100 24 cans in a carton 2100 KD per truck **** Allocated Demand (see next page) ***** New Shipping Cost = Shipping Cost + (Extra Trucks*Truck Cost)

Page | 202

Allocated Demand: The demand for 2009 including that for international markets is 38,776,320 cans annually, exceeding the company’s maximum capacity, taking into account a maximum overtime of 4 hours daily, by 2,085,120 cans. Since Kenya is the country that costs most to send to, we’re going to allocate the demand from Kenya to the local and regional markets so satisfy all their demands. Kenya’s demand for 2009 is 1,045,440 cans, but to satisfy the local demand only, Kenya’s entire demand should be allocated to the local markets to cover the difference in demand, as well as 125,280 cans from the US. And since the company won’t be able to cover the demand for the regional markets for 2009, the difference in units should be allocated from The US, since Kenya has already been entirely omitted. The demands for KSA, UAE, Bahrain, Qatar and Oman can all be covered by allocating the demand from the USA. The demand for Iraq, however, won’t be covered from the US alone given that the demand of the US has already been allocated to the other regional countries. Hence, 8640 cans will be allocated from Tunisia to Iraq. Table 3.45 shows the new shipping costs and demand that’s going to be sent to international markets. Given that all the demand for Kenya and the US have been allocated to cover the demand for the local and regional markets, no units will be shipped to them, and Tunisia will have 26 transporters.

Table 3.23: 2009 shipping costs and demand for international markets.

New Demand

Cans Shipped

2009

2009

Tunisia

1,045,440

1,036,800

Total

1,045,440

Number of Transporters Annually 26

New Shipping Cost

KD/year 21,338 21,338

Page | 203

2. Transportation Forecasted Cost for Year 2: 2010; Table 3.46 shows the shipping costs and demand for local and regional markets. Demand forecasts (see appendix) suggests the demand will increase by 10%. In that case, the demand for local and regional markets alone will be 38,054,016 cans. Their maximum capacity, however, is 36,691,200 cans annually. Hence, the demand to Tunisia will not be met, and will be allocated to the local market. Even after the allocation, none of the demand will be met. So, it is going to be assumed that the company will use up their 4 hours of overtime and produce with maximum capacity. Hence, the difference between the maximum capacity and the demand in 2009 will be divided by the number of markets they’re willing to send to, in this case 1 local market, and 6 regional ones. This number will be added to each of the demand for this year to be able to satisfy it as much as possible. When adding those numbers, it can be seen in Table 3.46, that not all markets require this increase. Consequently, the amounts with negative deficit (implying their demand is being exceeded by the number given) will be removed from those markets respectively and added to the local market since it’s the one with the highest deficit. Table 3.24: 2010 demand and demand deficit for local and regional markets.

Demand to Be Met Demand

Demand Deficit Cans/year

Local

18927360

1563264

KSA

4291200

99648

UAE

3625920

33120

Bahrain

2960640

-33408

Qatar

2295360

-99936

Oman

2295360

-99936

Iraq

2295360

-99936

Regional

Page | 204

Table 3.47 shows the shipping costs and demand for local and regional markets after readjusting the demand deficits for the regional customers. Table 3.48 shows what’s left of the international market, Tunisia. Since all of its demand will be allocated to the local market, and the company is already working at maximum capacity, nothing will be sent to Tunisia. So by 2010, The National Canned Food Company will be working at maximum capacity and still won’t be satisfying their local and the two major regional markets.

Table 3.25: 2010 shipping costs and demand for local and regional markets.

2010 Demand*

Increase 10%

Demand To Be Met** Cans/year

Demand Deficit*** Cans/year

Extra Transporters Needed

New Shipping***** Cost KD/year

Yearly**** Local Regional KSA UAE Bahrain Qatar Oman Iraq Total

20,490,624

19,260,576

1,230,048

-

0

4,390,848 3,659,040 2,927,232 2,195,424 2,195,424 2,195,424 38,054,016

4,291,200 3,625,920 2,927,232 2,195,424 2,195,424 2,195,424

99,648 33,120 0 0 0 0

6 6 5 4 4 4

15,589 19,189 14,976 11,592 15,192 6,192 82,729

* 2010 Demand = 2009 Demand + (0.1* 2009 Demand) ** Demand To Be Met =Demand + (Shipped to Tunis/7) + (Capacity-Demand)/7 *** Demand Deficit = Demand 2010 - Demand to be met **** Extra Transporters Needed Yearly = (Demand Difference/24)/2100 24 cans in a carton 2100 KD per truck ***** New Shipping Cost = Shipping Cost + (Extra Trucks*Truck Cost)

Page | 205

Table 3.26: 2010 shipping costs and demand for international markets.

2010 Demand

Tunisia

Increase 10% 1,149,984

Demand Shipped 2010 0

Since the National Canned Food Company is the only can filling company in Kuwait, it is firmly believed that they should first cover their local customers. Since there is a huge deficit in satisfying the local market with 1,230,048 cans annually, a regional market should be omitted to firstly satisfy the local customers to minimize transportation costs. Given all the transportation costs in Table 3.41, Oman’s transportation cost is the most expensive from all the regional shipping costs opposed to the average shipping cost of 248KD of all the other regional countries. So, it is highly recommended that the demand from Oman should be reallocated to the local market, and to KSA, and UAE. Table 3.27: 2010 shipping costs and demand for local and regional markets, considering re-allocating demands from Oman.

Local Regional KSA UAE Bahrain Qatar Oman Iraq Total

Year 2 Demand 10% 20,490,624

Demand To Be Met Cans/year 20,490,624

Transporters Needed Yearly

4,390,848 3,659,040 2,927,232 2,195,424 2,195,424 2,195,424 17,563,392

4,390,848 3,659,040 2,927,232 2,195,424 832,608 2,195,424

87 73 58 44 17 44

-

New Shipping Cost KD/year 0 17,424 21,780 16,843 13,068 6,608 6,534 82,257

Page | 206

3. Transportation Forecast Cost for Year 3: 2011; With a 10% demand increase 3 years from now, the demand deficit for the local customers is going to be very high. So as has been suggested previously, to minimize their costs, the National Canned Food Company should start eliminating one regional market at a time from the highest shipping cost to the lowest to try to prioritize the local market.

Table 3.28: 2011 demand for local and regional markets.

22,539,686

Annual Demand To Be Met 20,717,760

4,829,933 4,024,944 3,219,955 2,414,966 2,414,966 2,414,966 41,859,418

4,390,848 3,659,040 2,927,232 2,195,424 832,608 2,195,424 36,918,336

2011 Demand 10% increase Local Regional KSA UAE Bahrain Qatar Oman Iraq Total

Demand Deficit 1,821,926 439,085 365,904 292,723 219,542 1,582,358 219,542

Re-allocating all the demand from Oman wouldn’t cover the local market, so the country with the second highest shipping cost will start to be omitted to satisfy the local market. In this situation, two countries have a shipping cost of 300KD/month. However, since Qatar has lower demand than the UAE, it should be eliminated first after totally depleting Oman’s demand.

Page | 207

Table 3.29: 2011 demand for local and regional Markets after Oman’s demand has been depleted.

Local Regional KSA UAE Bahrain Qatar Oman Iraq

2011 Demand 10% 22539686

Demand To Be Met 21550368

Demand Deficit 989318

4829932 4024944 3219955 2414966 2414966 2414966

4390848 3659040 2927232 2195424 0 2195424

439084 365904 292723 219542 219542

Since the local market will only require 989,318 cans to fully cover its demand, it will come out of Qatar’s demand. Qatar will also fulfill the demands of other countries with demand deficits. The priority is to provide for the local market of course, and from then on, providing for countries with the least transportation cost. So after the local market, satisfying Iraq’s demand will be prioritized followed by KSA and Bahrain, and finally the UAE since it’s the most expensive to ship to from remaining regions. By doing so, all the regional demands will be satisfied with the exception of some of the UAE’s demands. Table 3.30: 2011 demand and shipping costs for local and regional markets after Oman and Qatar’s demands have been depleted.

Local Regional KSA UAE Bahrain Qatar Oman Iraq TOTAL

Year 3 Demand 10% increase

Demand To Be Met Cans/year

Demand Deficit

Transporters Needed

Shipping Cost

cans/year

Yearly

KD/year

22,539,686

22,539,686

0

0

0

4,829,933 4,024,944 3,219,955 2,414,966 2,414,966 2,414,966 41,859,418

4,829,933 3,686,659 3,219,955 0 0 2,414,966 36,691,200

0 338,285 0 0

96 73 64 0 0 48

19,200 21,900 18,560 0 0 7,200 66,860

Page | 208

4. Transportation Forecast Cost for Year 4; 2012

The UAE has the highest shipping cost from the remaining regional customers, hence demand will be reallocated from the UAE to the local market initially, and then to regional markets from the ones with lower shipping costs to higher ones.

Table 3.31: 2012 demand and demand to be met for local and the remaining regional markets.

Transporters Shipping Needed Cost Yearly KD/year

2012 Demand

Demand To Be Met

Demand Deficit

24793655.04

24,793,655

0

KSA

5312926.08

5,312,926

0

106

21,200

UAE

4427438.4

386,205

4,041,233

8

2,400

Bahrain

3541950.72

3,541,951

0

71

20,590

Iraq

2656463.04

2,656,463

0

53

7,950

TOTAL

40732433.28

36,691,200

Local

0

Regional

52,140

Page | 209

5. Transportation Forecast Cost for Year 5; 2013 The local demand deficit has been covered up by what was left of the UAE demand. The next market that was eliminated was Bahrain since it had transportation costs of 290 KD, compared with 200KD, and 150KD for each of KSA and Iraq respectively. Therefore, UAE and Bahrain are not going to be covered anymore, and the only regional markets remaining are KSA, and Iraq. Table 3.32: 2013 demand and shipping costs for local and the remaining regional markets.

2013 Demand Local 27,273,021 Regional KSA 5,844,219 UAE 4,870,182 Bahrain 3,896,146 Iraq 2,922,109 TOTAL 44,805,677

Demand To Be Met Cans/year 27,273,021 5,844,219 0 651,851 2,922,109 36,691,200

Demand Deficit Cans/year 0

Transporters Needed Yearly

Shipping Cost KD/year

0 3,244,295 0

116

23,191

13 58

3,751 8,697 35,639

6. Transportation Forecast Cost for Year 6; 2014 Bahrain has the highest transportation cost from the remaining regional customers. Hence, its demand will be allocated first to the local market and then to Iraq. Finally, what’s left is allocated to the KSA to cover their demand deficit. Table 3.33: 2014 demand and shipping costs for local and the remaining regional markets.

Demand Deficit Cans/year 0

Transporters Needed Trucks/year

Shipping Cost KD/year

30,000,323

Demand To Be Met Cans/year 30,000,323

6,428,641 3,214,320 43,929,044

3,476,557 3,214,320 36,691,200

2,952,084 0 7,237,844

69 64 728

13,796 18,495 32,291

2014 Demand Local Regional KSA Iraq TOTAL

Page | 210

6. Transportation Forecast Cost for after Year 6 The National Canned Food Company should follow the same procedure by forecasting demand and eliminating the markets that have the highest transportation costs by allocating their demands to the local market and then other regional markets which are cheaper to send to. Eventually, the total shipping cost would go down to 0 KD/year given that there is no transportation costs for the local market because the customer picks up the products from the National Canned Food Company’s warehouse.

New Fixed Cost = Total Fixed Cost – Transportation Cost = 442,053.86 – 178,560 = 263,493.86 KD/year New Total Cost = Total Cost – Total Fixed Cost + New Fixed Cost = 3,518,913.44 - 442,053.86 + 263,493.86 = 3,340,353.44 KD/year Savings in Total Cost =

3,518,913.44 - 3,340,353.44

= 178,560 KD

Page | 211

5. Conclusion The costs of the National Canned Food Company were classified into direct, indirect, technical overheads, company overheads, and marketing overheads costs. From those costs, the variable and fixed costs were calculated. The total cost was found to be 3,518,913.44 KD/year. The total revenue and total profits were also calculated and found to be 4,274,967.57 KD/year and 755,911.15 KD/Year, respectively, with a profit margin of 17.48%. This number suggests that the company is doing quite well. The total productivity of the National Canned Food Company was calculated to be 1.214 which is greater than 1, suggesting the company is productive. The breakeven point for the company was also obtained as well as the breakeven point for each individual product. This can help the company decide how much of each product to produce in order to make a profit. The total breakeven point was 12,597,389 cans, which means they broke even in a quarter of a year, which is quite reasonable. Although the numbers seem rather outstanding, when further analysis was done, it was noticed that the company has very high material costs due to overfilling their products. The cost of overfilling for each product was calculated and the total overfilling cost was found to be 68,001.8 KD/year. Another major cost issue the company was facing is the very high transportation costs of 178,560 KD/year. When the transportation costs were analyzed in detail, it was noticed that the National Canned Food Company had three major markets, local, regional and international. The international markets were the smallest customers with the highest transportation costs. Using demand forecasts, it was observed that within the next 2 years, the company would not be able to meet even its local customers because they’d already be producing at maximum capacity. Thus, it only seemed logical to start re-allocating their demands from their international markets to local and regional ones. The priority was given to the local market, due to the company being the only supplier and the fact that there is no local transportation cost, and then supplying markets with lower transportation costs. By eliminating one market at a time through the year, eventually the National Canned Food Company will only supply the local market and there would be no transportation costs, lowering their total cost to 3,340,353.44 KD, saving 178,560 KD. Page | 212

If the National Canned Food Company takes into consideration the analysis of this study, they would eventually be saving 68,001.6 KD annually due to overfilling in addition to 178,560 KD annually due to transportation costs. Overall, the company would be saving 246,561.8 KD yearly. This figure represents 7% of their total costs, and is considered substantial savings in the long run.

Page | 213

Page | 214

4. Production Line Analysis and System Maintenance

Page | 215

Page | 216

4.1 Introduction

The factory has two lines (can making line and can filling line) and both lines are continuous and the machines are connected in series, hence the failure of one machine causes the stoppage of the whole line, adversely affecting the production rate of the factory. Thus, it is important to analyze the maintenance system of the factory. The maintenance policies that the factory currently applies were studied and the reliability and availability of the factory were calculated. The performance of the factory was improved by introducing better maintenance policies to reduce the failure rate of the different machines. Since analytical methods assume very simple situations and do not apply to the factory’s situation, the as-is layout was modeled using Arena simulation software to analyze and improve it. For both lines, only 400 g size cans were considered since most of the factory production is of this size. For example, the production of the most recent four months was as follows: Table 4. 6: The production for July, August, September, and October.

Month

400 g

220 g

450 g

(cartons) (cartons) (cartons)

Total

400 g

220 g

450 g

(cartons) (%)

(%)

(%)

July

38142

1340

7120

46602

81.8

2.9

15.3

August

61767

1671

0

63438

97.4

2.6

0

September

62006

2471

1558

66035

93.9

3.7

2.4

October

26685

1976

0

28661

93.1

6.9

0

Total of 4

188600

7458

8678

204736

92.1

3.6

4.2

months

Page | 217

4% 4% 400 g 92%

200 g 450 g

Figure 4.2: Pie chart of the production of four months sample.

Problem Statement The current maintenance schedule causes too much downtime and is not optimized. The reliability of the can filling line is too low. The process can barely keep up with demand.

Objectives 

Improve the system reliability.



Increase the daily production.



Reduce the maintenance cost.

Solution Approach New maintenance plans were proposed that increased machine reliability and availability while minimizing the maintenance cost. These plans were evaluated using Arena simulation software to choose the best alternative amongst them, after verifying and validating the Arena models.

Page | 218

4.2 Part List

Part lists provide a listing of the components of the product. A part list includes part number, part name, and number of parts per product. Table 4. 7: Part list of 400 g canned food.

Company National Canned Food Co.

Prepared by: -

Product

Date: -

400 g canned food

Part NO.

Part Name

Quantity Material

Size (cm)

Make/Buy

001

Sheet metal

1

23 x 11

Buy

Coated

8 cm

Buy

Steel

diameter

Coated Steel

002

Lid

2

003

Label

1

Paper

23 x 8

Buy

004

Food

240 g

-

-

Buy

Page | 219

4.3 Bill of Materials (BOM)

The Bill of materials is a product structure hierarchy refereeing to the level of the product assembly. Level 0: Final product. Level 1: Subassemblies and components that feed directly to the final product. Level 2: Subassemblies and components that feed directly to level 1.

400 g Canned Food

Level 0 Level 1 Level 2

Empty can

Sheet metal 001

Food 004

Upper lid 002

Label 003

Lower lid 002

Figure 4.3: BOM of 400 g cans.

Page | 220

4.4 Component Part Drawing A component part drawing provides the part specifications and dimensions in sufficient detail to allow part fabrication.

D=8 cm 002

Figure 4.4: Component No. 002 (Lid)

23 cm

001

11 cm [Type a quote from the document or the summary of an interesting point. You can position the text box anywhere in the document. Use the Text Box Tools tab to change the formatting of the pull quote text box.]

Figure 4.5: Component No. 001 (400 g canned food).

Page | 221

Final Product

Figure 4.6: Final product (Canned food).

Page | 222

4.5 Process Description

The factory consists of two lines; the can making line and the can filling line. The process of the Can Making Line can be described as follows: Slitting: Tin sheets are cut into blanks of desired dimensions Blanks are manually fed to the welder Welding: the two ends of the blanks are welded to form a cylindrical shape Welded blanks are transported to the lacquering machine by the conveyer belt Lacquering: applying a varnish coat in the inner face of the welded blanks Curing: in this process the welded blanks are moved to the flanging machine by a magnetic belt and the varnished is cured and dried during this process Flanging: can is flanged at both ends to prepare it for seaming Seaming: one end of the can is seamed by a seamer Palletizing: every 2940 cans are place in a pallet and moved by a forklift to the empty can storage area. Note: The can production line follows FIFO (First in First out) procedure. Therefore, the stored empty cans are taken to the filling line, first.

Page | 223

The process of the can filling line can be described as follows: Soaking: the food is soaked for 8-14 hours in a hopper depending on the type of food (Peas, kidney beans, mushroom, etc). The factory has a total of five hoppers and the capacity of each hopper is 3000 Kg (meat and corn do not go into this process). Reel washing: the food is cleaned by showering and the excess water is drained. The food is transported to the blancher by a bucket elevator. Blanching: the food is blanched for 5 to 30 minutes to release gases and enzymes. De-stoning: the food is moved to the de-stoner to remove stones. Inspection belt: the food is sorted manually to remove any dark or broken pieces. The food is held in the filling hopper. Solid filling: the empty can is filled with solid food. Liquid filling: a liquid solution is added to the can. The can is vacuumed by the shower filler machine under a temperature of 75 °C to 85 °C. This process makes the expiry date of the canned food longer and protects consumers. Seaming: the other lid is seamed to the can using double seaming. Coding: a code is printed on the lid of the can using the coding machine to show the production and expiration dates of the product. Crate loading: 700 cans are put on a crate, and 7 layers of crates are taken to sterilizing the stage by a trolley. Sterilizing: the can in the crates are sterilized under a temperature of 121ºC. This process takes between 10 and 70 minutes depending on the type of product and the type of liquid used. Then, it is cooled suddenly to kill the remaining bacteria. The cans are then dried. Crate unloading: the cans are unloaded from the crate to the labeler. Labeling: the cans are labeled by the labeling machine. Page | 224

Label inspection: The labels are checked to determine whether they were applied correctly. Packaging: 12 cans are kept in a tray. Two trays are then wrapped together by the shrink wrapper. Every 20 cartons are put in a pallet by two workers and one fork lift. Storing: the final products are stored for four days before a sample is taken to carry out three types of tests (physical, chemical and biological), ensuring that the product meets standard and is ready for distribution. Notes: Cans are de-palletized before entering the filling line. In the filling line, empty cans are sterilized by hot water and steam while preparing the beans. The liquid solution is prepared prior production hours.

Page | 225

4.6 Process Flow on the Factory Layout

Figure 4.7: Process flow on the factory layout.

Page | 226

4.7 Operation Process Chart Company: National Canned Food Production and Trading Company

Prepared by:__________

Products: Can Making Line

Date:________________

Sheet Metal 001

011

Slitting

021

Welding

031

Lacquering

041

Curing

051

Flanging

Lid 002

SA1

071

Seaming Palletizing

Figure 4.8: Operation Process Chart for the can making line.

Page | 227

Company: National Canned Food Production and Trading Company

Prepared by:__________

Products: Can filling Line

Date:________________

Empty Can From store

013

023

Food 004

De-palletizing Sterilizing

012

Soaking

022

Reel Washing

032

Blanching

042

De-stoning

I1

Inspection Belt

SA

Solid Filling

2

072

Liquid Filling

A1

Seaming

092

Coding

102

Crate Loading

112

Sterilizing

122

Crate Unloading

A2

Labeling

Lid 00 2

Label 003

I2

Label Inspection

152

Packaging

I3

Testing

Figure 4.9: Operation process chart for the can filling line.

Page | 228

4.8 Route sheets

Table 4. 8: Route sheet of sheet metal.

Company: National Canned Food Production and Trading co. Produce:

Part Name: Sheet Metal

Part No.: 001

Prepared By: Date:

Operation Operation No. Description

Machine Rate

Materials or Parts Description

Machine Type

Dept.

011

Slitting

Slitting Machine

Coated Steel sheet metal Production 500 sheets/hr 23x11 cm

021

Welding

Welder

Production 160 cans/min

031

Lacquering

Lacquering Machine

Production 160 cans/min

041

Curing

Curing Machine

Production 160 cans/min

051

Flanging

Flanging Machine

Production 160 cans/min

071

Palletizing

Palletizer

Production

Page | 229

Table 4. 9: Route sheet of lid.

Company: National Canned Food Production and Trading co. Produce:

Part Name: Lid

Part No.: 002

Prepared By: Date:

Operation Operation

Operation

Materials or Parts

Time

Description

No.

Description

Machine Type

Dept.

SA1/A1

Seaming

Seamer

Production 500 sheets/hr

Lid 8 cm in diameter

Page | 230

Company: National Canned Food Production and Trading co. Produce:

Part Name: Food

Part No.: 004

Prepared By: Date:

Operation No.

Operation Description

Machine Type

Dept.

Machine Rate

012

Soaking

Hopper

Production

8-14 hours

022

Reel Washing

Shower

Production

032

Blanching

Blancher

Production

042

Destoning

Destoner

Production

I1

Inspection Belt

Inspection Belt

Production

SA2

Solid Filling

Solid Filler

Production

072

Liquid Filling

Liquid Filler

Production

092

Coding

Coding Machine

Production

102

Crate Loading

Crate Loader

Production

112

Sterilizing

Retort

Production

122

Crate Unloading

Crate Unloader

Production

A2

Labeling

Labeler

Production

I2

Label Inspection

152

Packaging

Shrink Wrapper

Production

30 cartons/min

I3

Inspection

-

QC Lab

4 days

Materials or Parts Description

5-30 min

140 cans/min

10-70 min

140 cans/min

Production

Page | 231

4.9 Data Collection and Fitting

The following table shows the demand inter-arrival distributions and the quantity distributions of each product. Table 4. 10: Distribution summary of inter-arrival and quantity of the demand.

Demand Inter-arrival1

Quantity2

(Days)

(Cartons)

Fava beans

0.5 + 8 * BETA(0.568, 1.52)

50 + 2.83e+003 * BETA(0.577, 0.802)

Peas

0.5 + WEIB(2.7, 1.5)

UNIF(50, 2.31e+003)

Chickpeas

0.5 + WEIB(1.95, 1.33)

470 + 2.59e+003 * BETA(0.889, 0.774)

Beans

0.5 + 7 * BETA(0.827, 2.05)

79 + 3.1e+003 * BETA(0.603, 1.26)

Corn

UNIF(1.5, 17.5)

TRIA(103, 188, 957)

Mushroom

0.5 + EXPO(7.05)

NORM(412, 230)

Entity

For more information about the daily production of the two lines, see Appendix (B).

1 2

See Appendix (F) for more details See Appendix (E) for more details

Page | 232

Table 4. 11: Summary of the mean time between failure (MTBF) of the machines and their repair time.

MTBF

Repair time

(Days)

(Min)

-0.5 + EXPO(5.16)

4.66

60

Palletizer/De-Palletizer

-0.5 + EXPO(12.2)

11.7

30

Process Line

-0.5 + EXPO(8.37)

7.87

30

Fillers and Seamer

-0.5 + EXPO(6)

5.5

60

Crate Loader

0.999 + EXPO(18.8)

19.799

5

Retort

-0.5 + EXPO(18.8)

18.3

30

Crate Unloader

1.5 + EXPO(23.2)

24.7

5

Labeler

-0.5 + EXPO(7.55)

7.05

60

Shrink Wrapper

0.999 + EXPO(13.5)

14.499

60

Machine3

MTBF4 Distribution (Days)

Can Plant

The MTBF in the table above is calculated as follows MTBF = E(X+EXPO(1\λ)) = X + 1\λ For example the MTBF of the can plant is E(-0.5 + EXPO(5.16)) = -0.5 + 5.16 = 4.66 days.

3 4

For more information about the machines, see Appendix (A) See Appendix (H) for more details

Page | 233

4.10 Maintenance Types

The factory has two types of maintenance; corrective maintenance and preventive maintenance.

Corrective Maintenance (CM)

Corrective maintenance is unscheduled maintenance actions performed as a result of system failure, to restore the system to specified condition. The failure rate λ = 1/MTBF Table 4. 12: Summary of the MTBF and the failure rate of the machines.

Failure Rate λ

Machine

MTBF (Days)

Palletizer/De-Palletizer

11.7

0.085

Process Line

7.87

0.127

Fillers and Seamer

5.5

0.182

Crate Loader

19.799

0.051

Retort

18.3

0.055

Crate Unloader

24.7

0.04

Labeler

7.05

0.142

Shrink Wrapper

14.499

0.069

Can Plant

4.66

0.215

(Failure/day)

Page | 234

Preventive Maintenance (PM)

Preventive maintenance is all scheduled maintenance actions performed to retain a system in a specified condition. The factory performs preventive maintenance once a month (every 26 days) during non-production hours and it takes 10 hours. f = 1/26 = 0.0385 preventive maintenance/day

Page | 235

4.11 Maintenance Plan

Maintenance Model 

Corrective maintenance is done by one mechanical technician, one electrician and one helper.



Preventive maintenance is done by two mechanical technicians, two electricians and two helpers.



Preventive maintenance is applied during non-production days, and lasts for 10 hours.



Mechanical technicians and electricians are paid 170 KD/month



Helpers are paid 50 KD/month.



26 days/month *12 month/year *10 hours/year = 3120 hours/year



Production rate of filling line = 140 cans/min



Production rate of can making line = 160 cans/min



Revenue/can = 0.232955 KD



CM Cost = (Mct/MTBF) * 3120 * cost/hr



PM Cost = fpt * Mpt * 3120 * cost/hr



Production Loss Cost= # units/min * Mct * λ * 3120 * Rev/unit



The new failure rate of the machine is calculated using the following equation: 1

𝑀𝑇𝐵𝑀 = 𝜆+𝑓 , by keeping the MTBM of the current maintenance plan the same and changing the preventive maintenance rate.

Page | 236

Current Maintenance Plan The factory currently applies preventive maintenance once a month (every 26 days). The following table shows the failure rate, PM rate and the mean time between maintenance (MTBM) of each machine.

Table 4. 13: Summary of the failure rate, preventive maintenance rate, and mean time between maintenance (MTBM) of the machines.

Machine

Failure Rate (failure/day)

PM Rate (actions/day)

MTBM (days)

Palletizer/De-Palletizer

0.085

1/26

8.07

Process Line

0.127

1/26

6.04

Fillers and Seamer

0.182

1/26

4.54

Crate Loader

0.051

1/26

11.23

Retort

0.055

1/26

10.74

Crate Unloader

0.040

1/26

12.66

Labeler

0.142

1/26

5.54

Shrink Wrapper

0.069

1/26

9.30

Can Plant

0.215

1/26

3.95

Page | 237

The annual corrective and preventive maintenance costs and the production loss cost of the current maintenance plan were also calculated. Table 4. 14: Summary of the corrective maintenance (CM) cost, preventive maintenance (PM) cost, and production loss cost of the machines.

CM Cost

PM Cost

Production Loss Cost

(KD/year)

(KD/year)

(KD/year)

Palletizer/De-Palletizer

20

72.07

26,090.960

Process Line

29.733

72.07

38,788.340

Fillers and Seamer

85.091

36.04

111,005.175

Crate Loader

1.970

18.02

2,569.694

Retort

12.787

36.04

16,681.106

Crate Unloader

1.579

18.02

2,059.813

Labeler

66.383

18.02

86,599.782

Shrink Wrapper

32.278

18.02

42,108.315

Can Plant

100.429

72.07

131,014.692

Total

350.25

360.36

456,917.88

Machine

Total Cost = CM cost + PM cost + Production loss cost = 350.25 + 360.36 + 456,917.88 = 457,628.5 KD/year.

Page | 238

Proposed Maintenance Plans Three alternative maintenance plans were proposed. They are as follows:

Alternative 1 Alternative proposed applying additional preventive maintenance actions twice a month (every 13 days), instead of once a month (every 26 days), during nonproduction days. This should reduce the failure rates of the machines. By keeping the MTBM of the current maintenance plan the same, the following results were obtained: Table 4. 15: Summary of the new failure rate, preventive maintenance rate, and mean time between maintenance (MTBM) of the machines.

Machine

Failure Rate

PM Rate (action/day)

MTBM (days)

(failure/day) Palletizer/De-Palletizer

0.047

1/13

8.07

Process Line

0.089

1/13

6.04

Fillers and Seamer

0.143

1/13

4.54

Crate Loader

0.012

1/13

11.23

Retort

0.016

1/13

10.74

Crate Unloader

0.002

1/13

12.66

Labeler

0.103

1/13

5.54

Shrink Wrapper

0.031

1/13

9.30

Can Plant

0.176

1/13

3.95

Page | 239

The annual corrective and preventive maintenance costs and the production loss cost of alternative 1 were also calculated as follows: Table 4. 16: Summary of the corrective maintenance (CM) cost, preventive maintenance (PM) cost, and production loss cost of alternative 1.

Production

CM Cost

PM Cost

(KD/year)

(KD/year)

Palletizer/De-Palletizer

11.010

143.99

14,362.708

Process Line

20.743

143.99

27,060.088

Fillers and Seamer

67.110

72.00

87,548.672

Crate Loader

0.471

36.00

614.985

Retort

3.797

72.00

4,952.854

Crate Unloader

0.081

36.00

105.104

Labeler

48.402

36.00

63,143.279

Shrink Wrapper

14.298

36.00

18,651.812

Can Plant

82.449

143.99

107,558.188

Total

248.360

719.97

323,997.689

Machine

Loss Cost (KD/year)

Total Cost = CM cost + PM cost + Production loss cost = 248.360 + 719.97 + 323,997.689 = 324,966 KD/year. Alternative 1 reduced costs by 29%.

Page | 240

Alternative 2 This alternative proposed applying preventive maintenance weekly (every 5 days) during non-production days. Once again, the same MTBM was used and the following results were obtained: Table 4. 17: Summary of the new failure rate, preventive maintenance rate, and mean time between maintenance (MTBM) of the machines.

Machine

Failure Rate (failure/day)

PM Rate (action/day)

MTBM (days)

Palletizer/De-Palletizer

-0.076

1/5

8.07

Process Line

-0.034

1/5

6.04

Fillers and Seamer

0.020

1/5

4.54

Crate Loader

-0.111

1/5

11.23

Retort

-0.107

1/5

10.74

Crate Unloader

-0.121

1/5

12.66

Labeler

-0.020

1/5

5.54

Shrink Wrapper

-0.093

1/5

9.30

Can Plant

0.053

1/5

3.95

Since only the fillers and seamer and the can plant have positive failure rates, they are the only machines were performing preventive maintenance actions every week is applicable. Thus, alternative 2 reduces to: Applying PM weekly on the fillers and seamer and the can plant, and twice a month on the remaining machines.

Page | 241

Table 4. 18: Summary of the new failure rate, preventive maintenance rate, and mean time between maintenance (MTBM) of the machines.

Machine

Failure Rate (failure/day)

PM Rate (action/day)

MTBM (days)

Palletizer/De-Palletizer

0.047

1/13

8.07

Process Line

0.089

1/13

6.04

Fillers and Seamer

0.020

1/5

4.54

Crate Loader

0.012

1/13

11.23

Retort

0.016

1/13

10.74

Crate Unloader

0.002

1/13

12.66

Labeler

0.103

1/13

5.54

Shrink Wrapper

0.031

1/13

9.30

Can Plant

0.053

1/5

3.95

Page | 242

The annual costs of alternative were found to be as follows: Table 4. 19: Summary of the corrective maintenance (CM) cost, preventive maintenance (PM) cost, and production loss cost of alternative 2.

Machine

CM Cost

PM Cost

(KD/year)

(KD/year)

Production Loss Cost (KD/year)

Palletizer/De-Palletizer

11.010

143.99

14,362.708

Process Line

20.743

143.99

27,060.088

Fillers and Seamer

9.50

187.2

12,404.828

Crate Loader

0.471

36.00

614.985

Retort

3.797

72.00

4,952.854

Crate Unloader

0.081

36.00

105.104

Labeler

48.402

36.00

63,143.279

Shrink Wrapper

14.298

36.00

18,651.812

Can Plant

24.847

187.2

32,414.344

Total

133.157

878.379

173,710.0026

Total Cost = CM cost + PM cost + Production loss cost = 133.157 + 878.379 + 173,710.0026 = 174,721.5 KD/year. Alternative 2 reduced the cost by 61.8%.

Page | 243

Alternative 3: In this alternative, it was suggested that PM be applied just before the failure occurs (Reliability centered maintenance). Table 4. 16 shows the MTBF of the current policy and the suggested mean time between preventive maintenance (MTBPM). Table 4. 20: Summary of the current MTBF and the proposed MTBPM.

MTBPM Machine

MTBF (days) (days/action)

Palletizer/De-Palletizer

11.7

11.6

Process Line

7.87

7.77

Fillers and Seamer

5.5

5.4

Crate Loader

19.799

19.70

Retort

18.3

18.20

Crate Unloader

24.7

24.60

Labeler

7.05

6.90

Shrink Wrapper

14.499

14.40

Can Plant

4.66

4.56

Page | 244

Using the same MTBM of the current policy and the suggested mean time between preventive maintenance, the following results are obtained: Table 4. 21: Summary of the new mean time between failures.

MTBFnew

Failure Rate

(days)

λ(Failure/day)

Palletizer/De-Palletizer

26.51

0.038

Process Line

27.15

0.037

Fillers and Seamer

28.49

0.035

Crate Loader

26.17

0.038

Retort

26.20

0.038

Crate Unloader

26.11

0.038

Labeler

28.27

0.035

Shrink Wrapper

26.33

0.038

Can Plant

29.62

0.034

Machine

Page | 245

The annual corrective and preventive maintenance costs and the production loss cost for alternative 3 were found to be as follows:. Table 4. 22: Summary of the corrective maintenance (CM) cost, preventive maintenance (PM) cost, and production loss cost of alternative 3.

Machine

CM Cost

PM Cost

(KD/year)

(KD/year)

Production Loss Cost (KD/year)

Palletizer/De-Palletizer

8.83

72

11,516.01

Process Line

8.62

72

11,241.73

Fillers and Seamer

16.42

36

21,426.21

Crate Loader

1.49

18

1,943.78

Retort

8.93

36

11,649.28

Crate Unloader

1.49

18

1,948.45

Labeler

16.56

18

21,599.26

Shrink Wrapper

17.78

18

23,189.42

Can Plant

15.80

72

20,608.73

Total

95.91

360.00

125,122.87

Total Cost = CM cost + PM cost + Production loss cost=125,578.78 KD/year. The cost has been reduced by 72.56%.

Page | 246

4.12 The Reliability of the Lines

Reliability is the probability that the system will perform in a satisfactory manner for a given period of time, when used under specified operating conditions. It is calculated with the following equation: R (T) = e-λt , where λ is the failure rate and t is the given period of time. Table 4. 23: Summary of the mean failure rate of the machines and their reliability over one day.

Failure Rate

Reliability over

λ(Failure/day)

one day (%)

Palletizer/De-Palletizer

0.085

91.81

Process Line

0.127

88.07

Fillers and Seamer

0.182

83.38

Crate Loader

0.051

95.07

Retort

0.055

94.68

Crate Unloader

0.040

96.03

Labeler

0.142

86.78

Shrink Wrapper

0.069

93.34

Can Plant

0.215

80.69

Machine

Page | 247

Alternative 1:

Table 4. 24: Summary of the failure rate of the machines and their reliability over one day for alternative 1.

Failure Rate

Reliability over

Improvement

λ(Failure/day)

one day (%)

(%)

Palletizer/De-Palletizer

0.047

95.40

3.91

Process Line

0.089

91.52

3.92

Fillers and Seamer

0.143

86.64

3.91

Crate Loader

0.012

98.80

3.92

Retort

0.016

98.39

3.92

Crate Unloader

0.002

99.79

3.92

Labeler

0.103

90.17

3.91

Shrink Wrapper

0.031

96.99

3.91

Can Plant

0.176

83.85

3.91

Machine

Page | 248

Alternative 2:

Table 4. 25: Summary of the failure rate of the machines and their reliability over one day for alternative 2.

Failure Rate

Reliability over

Improvement

λ(Failure/day)

one day (%)

(%)

Palletizer/De-Palletizer

0.047

95.40

3.92

Process Line

0.089

91.52

3.92

Fillers and Seamer

0.020

97.99

17.53

Crate Loader

0.012

98.80

3.92

Retort

0.016

98.39

3.92

Crate Unloader

0.002

99.79

3.92

Labeler

0.103

90.17

3.92

Shrink Wrapper

0.031

96.99

3.92

Can Plant

0.053

94.83

17.53

Machine

Page | 249

Alternative 3:

Table 4. 26: Summary of the failure rate of the machines and their reliability over one day for alternative 3.

Failure Rate

Reliability over

Improvement

λ(Failure/day)

one day (%)

(%)

Palletizer/De-Palletizer

0.038

96.30

4.89

Process Line

0.037

96.38

9.44

Fillers and Seamer

0.035

96.55

15.80

Crate Loader

0.038

96.25

1.24

Retort

0.038

96.26

1.66

Crate Unloader

0.038

96.24

0.22

Labeler

0.035

96.52

11.23

Shrink Wrapper

0.038

96.27

3.15

Can Plant

0.034

96.68

19.82

Machine

Page | 250

4.13 Results

Can Making Line

Can Plant

Figure 10: Schematic illustration of the can production line.

Current Maintenance Plan From Table 4. (19) and Figure (9), it was concluded that the reliability of the can production line is 80.65%.

Proposed Maintenance Plan Alternative 1: From Table (20) and Figure (9), it was concluded that the reliability of the can making line has become 83.85%, an increase of 3.91%. Alternative 2: From Table (21) and Figure (9), it was concluded that the reliability of the can making line has become 94.83%, an increase of 17.53%. Alternative 3: From Table (22) and Figure (9), it was concluded that the reliability of the can making line has become 96.68%, an increase in of 19.82%.

Page | 251

Can Filling Line

Palletizer/ DePalletizer (1) Crate

Fillers & Seamer

Crate

(3)

Loader

Retort (5)

Shrink

unloader

Labeler

Wrapper

(6)

(7)

(8)

(4)

Process Line (2)

Figure 11: Schematic illustration of the can filling line.

Current Maintenance Plan From Table (19) and Figure (10), it was concluded that the reliability of the can filling line is R(1 day) = [1-(1-R1)(1-R2)] (R3) (R4) (R5) (R6) (R7) (R8) = [1-(1-0.9181)(1-0.8807)](0.8338)(0.9507)(0.9468)(0.9603)(0.8678)(0.9334) = 0.5781 = 57.81% Proposed Maintenance Plan Alternative 1: From Table (20) and Figure (9); R(1 day) = [1-(1-R1)(1-R2)] (R3) (R4) (R5) (R6) (R7) (R8) = [1-(1-0.9540)(1-0.9152)](0.8664)(0.9880)(0.9839)(0.9979)(0.9017)(0.9699) = 0.7322 = 73.22%

Page | 252

It can be concluded that the reliability of the can filling line has become 73.22%, an increase of 26.65%.

Alternative 2: From Table (20) and Figure (9); R(1 day) = [1-(1-R1)(1-R2)] (R3) (R4) (R5) (R6) (R7) (R8) = [1-(1-0.9540)(1-0.9152)](0.9799)(0.9880)(0.9839)(0.9979)(0.9017)(0.9699) = 0.8281 = 82.81% It can be concluded that the reliability of the can filling line has become 82.81%, an increase of 43.24%. Alternative 3: From Table (20) and Figure (9); R(1 day) = [1-(1-R1)(1-R2)] (R3) (R4) (R5) (R6) (R7) (R8) = [1-(1-0.9630)(1-0.9638)](0.9655)(0.9625)(0.9626)(0.9624)(0.9652)(0.9627) = 0.7989 = 79.89% It can be concluded that the reliability of the can filling line has become 79.89%, an increase of 38.19%.

Page | 253

4.14 Availability of the Machines

Availability is the percentage of time or the probability that a system will be ready or available when required. Availability is expressed differently; three common Figures of Merit (FOM) are defined below:

Inherent Availability (Ai): Probability that an equipment (or system), when used under stated conditions in an ideal support environment (i.e. readily available tools, spares, maintenance personnel, etc), will operate satisfactorily at any time as required. It excludes: Preventive/scheduled maintenance. Logistic Delays (maintenance down time that is expended as a result of waiting for a spare part to become available, waiting for the availability of testing equipment, waiting for use of a facility, etc). Administrative delays (portion of down time during which maintenance is delayed for administrative reasons). The Inherent Availability is calculated with the following equation: 𝑀𝑇𝐵𝐹

Ai= 𝑀𝑇𝐵𝐹 +𝑀 𝐶𝑇 Where, MTBF = mean time between failures 𝑀CT = mean corrective maintenance time

Page | 254

Achieved Availability (Aa) The probability that a system or equipment, when used under stated conditions in an ideal support environment (i.e. readily available tools, spares, personnel etc.) will operate satisfactorily at point in time. This definition (Aa) is similar to that of Ai. However, preventive maintenance is included. It excludes the logistic delays, administrative delays etc.

The Achieved Availability is calculated with the following equation: 𝑀𝑇𝐵𝑀

Aa= 𝑀𝑇𝐵𝑀 +𝑀 Where, MTBM = Mean time between maintenance 𝑀=Mean active maintenance time And MTBM & 𝑀 are a function of corrective and preventive maintenance actions.

Operational Availability (Ao) Probability that system or equipment, when used under stated conditions in an actual operational environment, will operate satisfactorily when called upon. 𝑀𝑇𝐵𝑀

Ao=𝑀𝑇𝐵𝑀 +𝑀𝐷𝑇 Where MDT=Mean Maintenance Down Time.

Page | 255

Table 4. 27: Summary of the availability of the machines.

Machine

Ai (%)

Aa (%)

Ao (%)

Palletizer/De-Palletizer

99.57

95.90

60.15

Process Line

99.37

95.71

53.40

Fillers and Seamer

98.21

94.64

46.33

Crate Loader

99.96

96.25

67.39

Retort

99.73

96.04

66.36

Crate Unloader

99.97

96.26

69.75

Labeler

98.60

95.00

51.17

Shrink Wrapper

99.32

95.66

63.18

Can Plant

97.90

94.34

43.00

Page | 256

4.15 Spare Parts

Data related to the spare parts required for each machine and the number ordered per year was collected. The factory orders some of the spare parts locally and some others from the UK, Germany and Italy. The following table shows each machine, its spare parts and the number ordered per year. Table 4. 28: Summary of the machines, spare parts, and the number ordered per year.

Machine

Plletizer/De-Palletizer

Spare Part

No. of orders per year

Sensors

2

Bearings

4

Pneumatics valves

2

Sprocket

1

Shaft

10

Rollers

1

Belt

5

Sprocket

4

Bearings

7

Clutch

1

Seaming roller

4

Chuck

8

Bearings

2

Sprocket

1

Process Line

Fillers and Seamer

Crate Loader

Page | 257

Machine

Retort

Spare Part

No. of orders per year

Electrical fuses

4

Conductors

1

Pipe fittings

10

Gasket

25

Valves

4

Bearings

2

Conductors

2

Electric motor

2

Driving belt

1

Belt

3

Glue valves

2

Electrical fuses

4

Bearings

5

Glue nozzle

2

Glue Filters

2

Conductors

2

Belt

2

Bearings

12

Belt

5

Sprocket

4

Crate Unloader

Labeler

Shrink Wrapper

Can Plant

Page | 258

Machine

Spare Part

No. of orders per year

Conductors

4

Cylinder

1

Page | 259

4.16 System Simulation

Nowadays, manufacturers are facing rapid and fundamental changes in the ways business is done. Producers are looking for simulation systems increasing throughput and profit, reducing cycle time, improving due-date performance and reducing WIP. Manufacturing systems, often requiring large investments in capital, equipment and supporting software, are costly and time-consuming to acquire, integrate, and operate. Simulation technology is a tool of proven effectiveness in improving the efficiency of manufacturing system design, operation, and maintenance. Simulation models can be used to perform “what-if” analyses and make better-informed decisions. Manufacturing simulation has been one of the primary application areas of simulation technology. It has been widely used to improve and validate the designs of a wide range of manufacturing systems.. The following are some of the specific issues that simulation is used to address in manufacturing systems: 

The quantity of equipment:



Number and type of machines for a particular objective.



Number, type, and physical arrangement of transporters, conveyors, and other support equipment (pallets and forklifts).



Location and size of inventory buffers.



Evaluation of a change in product volume or mix.



Labor-requirements planning.



Performance evaluation:



Throughput analysis.



Time-in-system analysis.



Bottleneck analysis.

Page | 260

Evaluation of operational procedures: 

Production scheduling.



Control strategies.



Reliability analysis (effect of preventive maintenance).

Following are some of the performance measures commonly estimated by simulation: 

Throughput.



Time in system for parts.



Time parts spend in queues.



Queue sizes.



Timeliness of deliveries.



Utilization of equipment or personnel.

Arena was used to simulate the can making and can filling lines to study the effect of changing the rate of the preventive maintenance on the daily production of the lines.

Page | 261

Problem Formulation System entities Can Making Line: The entities of this line are the boxes that contain the tin sheets. Every day, two boxes containing 1300 sheets each, with 28 cans of size 400 g produced from each sheet, are processed. Can Filling Line: The different products were split into separate categories. Products of the same category undergo the exact same processes, with the only difference being the sauces used. However, the model was not affected by this because one or two workers come two hours prior to production hours to heat the holding tank and mix the sauce. Therefore, the entities are the number of boxes ordered (each box contains 24 cans).

Page | 262

Material handling system Material handling is an activity that uses the right method to provide the right amount of the right material at the right place, at the right time, in the right sequence, in the right position and at the right cost. Material handling for the can making line is as follows: 

Conveyer Belt: The belt transports 160 welded blanks per minute to the lacquering machine.



Magnetic Belt: In the curing process, 160 welded blanks are moved per minute to the flanging machine by the magnetic belt. The varnish is cured and dried during this process.



Palletizer: Every 2940 cans are put in a pallet, (14 layers with 210 cans in each layer) and moved by a forklift to the empty can storage area.



The material handling for the filling line consists of:



Bucket elevator: All the solid material (depending on the demand) is transported to the blancher by this elevator.



Inspection belt: All the solid material (depending on the demand) is sorted manually to remove any dark or broken pieces.



Crate: Crate holding 720 cans (split into 6 layers) are loaded to the sterilizing stage then unloaded to the labeler.



Forklift: Every 90 cartons are put in a pallet by two workers before being transported by a single forklift.

Page | 263

Current Problems in the Layout In the current layout, both lines are physically connected and the empty cans are supposed to go to the filling line through this link automatically once they are manufactured. However, this link is not being utilized, with the empty cans being transported manually to the filling line, instead. The cans are then palletized before they are filled. The reasons behind not using this connection are: 

The difference in production plans of both lines.



Some of the empty cans might be defective and thus cannot be filled.



The factory has to work overtime to meet demand.



Also, the failure rates of the machines are high because the machines are very old. Therefore, the production lines are stopped in every breakdown. This will cause a delay meaning the factory will not meet deadlines or work overtime to do so.

Work Schedule In our model we have a total of 4 workers and their schedule is: 26 days/month 5 days/week 1 shift/day 10 hours/shift

Workers have breaks from 8-9 AM and 12-1:30 PM. All machines in the model are used for 10 hours.

Page | 264

Scrap Estimate In the can making line, only 0.15% of the total cans produced are defective per day. See Appendix (B) for details.

Policies The factory has some policies related to processing the entities and they are: The factory does not process the order as it is placed; but wait for other orders to come before processing them together. They store two containers for prime items and fill them again once they are used.

Page | 265

Simplification Assumptions In this section we will list the assumptions we used to simplify the model Can Making Line Assumptions: 

Lids already fed in the seamer.



Overtime is not included.



Setup times and warming up time are done outside of production hours.

Can Filling Line Assumptions: The entities are the number of cartons ordered from the following categories: Fava beans, Peas, Chick peas, Beans, Mushroom, and Corn. 

The soaking step is not considered since it is done overnight and is finished before production starts at 6:30 AM.



Reel washing, De-stoning, Blanching, and Inspection belt are considered as one process and are called the Process Line.



The rate of the process line is equivalent to 120 cans/minute.



Empty cans are ready to be filled.



Overtime is not included.



Soaking and mixing in the holding tank is done outside of production hours.



Setup times and warming up time are done outside of production hours.

Page | 266

Coding the Arena Model of the As-Is System Can Making line

Create 2

Proc es s 8

As s ign 1

Dis pos e 5

As s ign 2

0 N.Create 2 TBA : -0.5+LOGN(4.28,7.15) day Entity per arrival =1

0 N. process0 8 Delay Delay: constant=1hr

Variable 1=IRF(Slitter)==1 Variable 2=IRF(Welder)==1 Variable 3=IRF(Lacquering machine)==1 Variable 4=IRF(Curing machine)==1 Variable 5=IRF(Flanger)==1 Variable 6=IRF(Seamer)==1

Variable 1=IRF(Slitter)==0 Variable 2=IRF(Welder)==0 Variable 3=IRF(Lacquering machine)==0 Variable 4=IRF(Curing machine)==0 Variable 5=IRF(Flanger)==0 Variable 6=IRF(Seamer)==0

0 Create 1

Separate 1 O r iginal

Slitting

0 N.Create 1 TBA :constant=1 day Entity per arrival =ANINT(DISC(0.18,1,0.94,2,1,3))

0

Welding

Lac quering

N.Separate 1 Type: dublicate

N.Slitting 0 S-D-R Res:slitter , Q=1 Delay: constant=7.2 sec

Size:1299

N.Wedling 0 S-D-R Res:welder,Q=1 Delay: constant=10.5 sec

N.Lacquring 0 S-D-R Res:lacquering machine,Q=1 Delay: constant=10.5 sec

Daily Produc tion

0 Seaming

Separate 2 O r iginal

0 N.Seaming S-D-R Res:Seamer, Q=1 Delay: constant 10.5 sec

0 N.Separate 2 Type: dublicate Size:27

Curing

Flanging

Duplicat e

0 D ecide 1

0

N.Flanging 0 S-D-R Res:flanger ,Q=1 Delay: constant=10.5 sec

Dis pos e 3

0

Tr ue

2 way by chance 99.85%

Duplicat e

N.Curing S-D-R 0 Res:curing machine,Q=1 Delay: constant=10.5 sec

N.Daily Production Type: count Value=1

False

Sc rap

Dis pos e 4

0 N.Scrap Type: count Value=1

No. of replication:10 Rep. length:10 hours Hours / day:24

Page | 267

Figure 12: Arena model code of the can production line.

Explanation of the As-is Model of the Can Making Line As mentioned in the problem formulation section, the entities here are the boxes that contain the sheet metals. One, two, or three boxes/day are processed according to demand which follows the distribution ANINT(DISC(0.18, 1, 0.94, 2, 1, 3) (See Appendix (B) for more details). Each box contains 1300 sheets, with each sheet capable of producing 28 cans. First, the box arrives to the line. Then the module “separate” was used to convert the box to 1300 sheets. Note that the process time per sheet (per 28 cans) was used because the process time per can would be too small. The sheet is then cut to the desired length by the slitter before it goes to the welding machine to be welded, to the lacquering machine to add the varnish in the inner face, to the curing machine to cure the varnish, to the flanger to flange both ends and finally to the seamer to seam the lid onto one end. The process time from the welding machine to the seamer is constant at 10.5 seconds/sheet. Again, the separate module was used to convert one sheet into 28 cans. In the decide module the scrap rate of this line, which is 0.15% of the total production, was added. Finally, empty cans are palletized are transported to the storage area. The failure of the can making line was also modeled, where the mean time between failures follows the distribution -0.5 + LOGN(4.28, 7.15) (See Appendix (H) for more details).

Page | 268

Can Filling line N.Fava Beans Att, type=1

TBA : 0.5 + 8 * BETA(0.568, 1.52) days

Fa v a Be ans

Entity per arrival =ANINT(50 +

As s ig n 1

Att,proctime=52

0

2.83e+003 * BETA(0.577, 0.802))

Att, type=2 N.Chickpeas

Att,proctime=52

TBA : 0.5 + WEIB(1.95, 1.33) days

Ch ic k pe as

Entity per arrival =anint(470 +

As s ig n 2

0

Att, type=3

2.59e+003 * BETA(0.889, 0.774))

Att,proctime=27

N.peas TBA :

Pe as

Att, First item= tupe

As s ig n 3

Var, Switch=0

0.5 + WEIB(2.7, 1.5)days

0

Entity per arrival =anint(UNIF(50,

0

2.31e+003)) N.Corn TBA :

D ecide 2

Tr ue

As s ig n 7 2 way by condition

Co rn

As s ig n 4

UNIF(1.5, 17.5)days

Att, type=4

0

Entity per arrival =anint(TRIA(103,

Att,proctime=27

0

Fals e

2 way by condition

IF(first item == following item)

IF(switch == following item)

0 D ecide 3

188, 957))

Tr ue

PL

FS

N.Mushroom TBA : 0.5 + EXPO(7.05) days

N.PL

M us h roo m

Entity per arrival

As s ig n 5

0

Att, type=5

As s ig n 8

0

Att, Following item= tupe Var,Switch=1

N.Beans

0 9 N.Process

N.FS

0

Be an s

S-D-R

Res:Process line , Q=1

Res:Filler Seamers , Q=1

Delay:

Delay:

constant=12 sec

constant=10.3 sec

Delay: constant=30 min

As s ig n 6 Att, type=6

0

Att,proctime=45

3.1e+003 * BETA(0.603, 1.26))

Page | 269 Figure 13: Arena model code of the can filling line – Part 1.

0

S-D-R

Delay

TBA :

Entity per arrival =anint(79 +

Proc e s s 9

Att,proctime=30

=anint(NORM(412, 230))

0.5 + 7 * BETA(0.827, 2.05) days

Fals e

Coding

S terillizing

Crate Loading

0 N.Coding

0 N.Crate Loading

N.Sterilizing 0

S-D-R

Type: Temporary

S-D-R

Res:Coding machine,Q=1

Size:30

Res:Retort,Q=1 Delay: expression=proctime min

Delay: 10.3 sec

N.Labeling S-D-R

2 way by chance

Res:Labeller,Q=1

95.8%

Delay: constant=10.3 sec

0

0 Crate Unloading

S eparate 1

Decide 1

Labeling

T ru e

S hrink W rapping

daily production

Dispose 2

0 0

N.Crate Unloading S-D-R Res:Crate Unloader,Q=1

N.Separate 1

0

0

Type: Split Exiting

Fa ls e

0 N.Shrink Wrapping S-D-R

Batch

Res:Shrink Wrapper,Q=1

Delay: constant=5 Min

N.Daily Production Type: count Value=1

Delay: constant=10.3 sec

scrap

N.Scrap

variable , switch=1

No. of replication:32

Type: count

Warm-up:2 hours

Value=1

Rep. length:10 hours Hours / day:24

Figure 14: Arena model code of the can filling line – Part 2.

Page | 270

Explanation of the As-is Model of the Can Filling Line Entities were split into six categories (fava beans, chickpeas, peas, corn, mushroom, beans); all the products which have the same properties (eg: process time) were put in the same group. Each group belonged to the same create with TBA that represents the demand in days and with entities per arrival that represents the number of cartons (see Appendix (E) and Appendix (F) for more details about the distributions). An assign for each category was used to assign the type needed for the flag, as is explained later, and for assigning the process time that is needed for sterilizing. The flag: A decide module was added to check if the system variable changed or not (since a variable called switch=1 was identified). Therefore, it allows the entities with the same type to pass together with same variable value. Then a second decide module was added to check the type; so the first type will pass and the next one will be delayed for 30 min during which the line is cleaned. The entity will pass through a process called “process line” which takes 12 sec for each carton, then through the fillers and seamer which takes 10.3 for each carton. Finally, coding has the same process time for each carton. After that a batch module was added to load every 30 cartons in the same crate. The crate then goes to the sterilizing process, whose process time depends on the type of the product identified in the assign module, as afore-mentioned. Afterwards, the crate will be unloaded and this process will take 5 min/crate. A separate module is used for this purpose. Each carton will then pass through the labeler, which takes 10.3 sec, and a decide module is added to return the scrapped cans to the labeler to be relabeled. Finally, every two cartons will be packed together using the shrink wrapper machine which takes 10.3 sec and a counter is added to count the daily production in cartons.

Page | 271

Modeling the failures of the machines was done as follows: Resource module: Table 4.29: Summary of the resource module.

Resource

Failure

Failure Rule

Process Line

Failure 1

Preempt

Fillers and seamers

Failure 2

Preempt

Retort

Failure 3

Preempt

Labeller

Failure 4

Preempt

Shrink Wrapper

Failure 5

Preempt

Failure module: Table 4.30: Summary of failure module.

Name

Up time (days)

Down time (min)

Failure 1

EXPO(7.87)

30

Failure 2

EXPO (5.5)

60

Failure 3

EXPO(18.3)

30

Failure 4

EXPO (7.05)

60

Failure 5

EXPO (14.499)

60

Page | 272

Verification and Validation Can Making Line The Arena model that was coded for the can making line was verified and it was observed that the model works properly. Validating the daily production: The replication parameters are: Replication Length: 10 hours/day. Number of replications: 33 (see Appendix (I) for more details about the sample size).

For validation, the following two performance measures were used: The daily production. The scrap cans.

Page | 273

Validating the Daily Production: Since the daily production is normally distributed for both the real system and the asis model, see Appendix (C), hypothesis tests were applied to find the confidence intervals.

Table 4.31: Real system and as-is model statistics summary.

Real system

As-is model1

n

53

33

𝐱 (cans)

69,147.4

63,879.45

S (cans)

18,289.5

15,817.06

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if f0> f α/2, n1-1, n2-1 Significance: α= 0.05 f0= 1.337 f0.025, 52, 32 =1.65 p-value = 0.383 Since f0< f0.025, 52, 32, H0 was not rejected and both variances are equal.

1

See Appendix (J) for more details

Page | 274

Testing the equality of two means:

H0: μ1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, n1+n2-2 or p-value < α t0 = 1.413 p-value= 0.162 Since p-value > α. H0 was not rejected and both means are equal.

Confidence interval 𝑥1-𝑥2 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ1- µ2 ≤ 𝑥1-𝑥2 + t α/2,v

𝑆2

+ 𝑛2

2

-2,156.64≤ µ1- µ2 ≤ 12,692.54 There is a 95% chance that the difference between the two means is within [2,156.64, 12,692.54]. Since zero is within this interval, both means are equal. The power of this test is 90%. Thus, the model is valid.

Page | 275

Validating the Daily Scrap Since the daily scrap is normally distributed for both the real system and the as-is model, see Appendix (C), hypothesis tests can be applied to find the confidence intervals. Table 4.32: Real system and as-is model statistics summary.

As-is model1

Real system n

53

33

𝐱 (cans)

97.94

96.56

S (cans)

34.09

25

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if f0> f α/2, n1-1, n2-1 Significance: α= 0.05 f0 = 1.86 f0.025, 53, 32 = 1.65 p-value = 0.064 Since f0< f0.025, 52, 32, H0 was not rejected and both variances are equal.

1

See Appendix (J) for more details

Page | 276

Testing the equality of two means: H0: μ1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, n1+n2-2 or P-value < α t0 = 0.217 df = 82.06 P-value = 0.829 Since p-value > α, H0 was not rejected and both variances are equal. Confidence interval: 𝑥1-𝑥2 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ1- µ2 ≤ 𝑥1-𝑥2 + t α/2,v

𝑆2

+ 𝑛2

2

-11.34 ≤ µ1- µ2 ≤ 14.10 There is a 95% chance that the difference between the two means is within [-11.34, 14.10]. Since zero is within this interval, both means are equal. The power of this test is 90%. Thus, the model is valid.

Page | 277

Can Filling Line The Arena model that was coded for the can filling line was verified and it was observed that the model works properly. Validating the daily production: The replication parameters are: Replication Length: 10 hours/day. Number of replications: 32 (see Appendix (I) for more details about the sample size).

For validation, the daily production was used as a performance measure.

Page | 278

Validating the Daily Production: Since the daily production is normally distributed for both the real system and the asis model, see Appendix (C), hypothesis tests were applied to find the confidence intervals.

Table 4.33: Real system and as-is model statistics summary.

Real system

As-is model

N

58

32

𝐱 (carton)

2,338.1

2364.375

S (carton)

599.1

99.18

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if f0> f α/2, n1-1, n2-1 Significance: α= 0.05 f0 = 36.49 f0.025, 57, 31 = 1.95 p-value = 1.071*10-17 Since f0> f0.025, 57, 31, H0 was rejected and there the variances are not equal.

Page | 279

Testing the equality of two means:

H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = -0.362 p-value= 0.746 Since p-value > α, H0 was not rejected and both means are equal. Confidence interval: 𝑥1-𝑥2 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ1- µ2 ≤ 𝑥1-𝑥2 + t α/2,v

𝑆2

+ 𝑛2

2

-187.36 ≤ µ1- µ2 ≤ 134.81 There is a 95% chance that the difference between the two means is between [187.36, 134.81]. Since zero is within this interval, both means are equal. The power of this test is 90%. Thus, the model is valid.

Page | 280

4.17 Analysis of Daily Production Runs and Improvement

In this section, the statistical analysis used to compare between the daily production of each alternative and the as-is model, based on the simulation models, is shown.

Can Making Line For the can making line, only the most common case (2 boxes of sheet metal per day) was simulated to reduce the variability in the output. Alternative 1 The same Arena code of the can making line that was described in section 20.1 was run but with the new values of the mean time between failures obtained from alternative 1.

1

Table 4. 34: As-is model and alternative 1 statistics summary .

As-is model

Alternative 1

N

33

33

𝐱 (carton)

72,691.06

72,691.06

S (carton)

2.086

2.086

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if p-value< α Significance: α= 0.05 f0= 1

1

See Appendix (J) for more details

Page | 281

p-value = 1 Since p-value> α, H0 was not rejected and both variances are equal.

Testing the equality of two means: H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = 0 p-value= 1 Since p-value > α, H0 was not rejected and both means are equal.

Confidence interval: 𝑥2-𝑥1 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ2- µ1 ≤ 𝑥2-𝑥1 + t α/2,v

𝑆2

+ 𝑛2

2

-1.03 ≤ µ2- µ1 ≤ -1.03 There is a 95% chance that there is no significant difference between the as-is model and alternative 1. Thus, there is no improvement. The power of this test is 90%.

Page | 282

Alternative 2 The same Arena code of the can making line that was described in section 20.1 was used but with the new values of the mean time between failures obtained from alternative 2.

1

Table 4. 35: As-is model and alternative 2 statistics summary .

As-is model

Alternative 2

N

33

33

𝐱 (carton)

72,691.06

72,691.06

S (carton)

2.086

2.086

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if p-value< α Significance: α= 0.05 f0= 1 p-value = 1 Since p-value> α, H0 is not rejected and both variances are equal.

1

See Appendix (J) for more details

Page | 283

Testing the equality of two means: H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = 0 p-value= 1 Since p-value > α, H0 is not rejected and both means are equal.

Confidence interval: 𝑥2-𝑥1 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ2- µ1 ≤ 𝑥2-𝑥1 + t α/2,v

𝑆2

+ 𝑛2

2

-1.03 ≤ µ2- µ1 ≤ -1.03 There is a 95% chance that there is no significant difference between the as-is model and alternative 2. Thus, there is no improvement. The power of this test is 90%.

Page | 284

Alternative 3 The same Arena code of the can making line that was described in section 20.1 was used but with the new values of the mean time between failures obtained from alternative 3.

1

Table 4. 36: As-is model and alternative 3 statistics summary .

As-is model

Alternative 3

N

33

33

𝐱 (carton)

72,691.06

72,691.06

S (carton)

2.086

2.086

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if p-value< α Significance: α= 0.05 f0= 1 p-value = 1 Since p-value> α, H0 was not rejected and both variances are equal.

1

See Appendix (J) for more details

Page | 285

Testing the equality of two means: H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = 0 p-value= 1 Since p-value > α, H0 was not rejected and both means are equal. Confidence interval: 𝑥2-𝑥1 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ2- µ1 ≤ 𝑥2-𝑥1 + t α/2,v

𝑆2

+ 𝑛2

2

-1.03 ≤ µ2- µ1 ≤ -1.03 There is a 95% chance that there is no significant difference between the as-is model and alternative 3. Thus, there is no improvement. The power of the test is 90%.

Page | 286

Can Filling Line Alternative 1 The same Arena code of the can making line that was described in section 20.2 was used but with the new values of the mean time between failures obtained from alternative 1.

Table 4. 37: Summary of failure module of alternative 1.

Name

Up time (days)

Down time (min)

Failure 1

EXPO (11.24)

30

Failure 2

EXPO (6.99)

60

Failure 3

EXPO (62.5)

30

Failure 4

EXPO (9.71)

60

Failure 5

EXPO (32.26)

60

1

Table 4. 38: As-is model and alternative 1 statistics summary .

1

As-is model

Alternative 1

N

32

32

𝐱 (carton)

2,364.375

2,403.438

S (carton)

99.18433

88.25145

See Appendix (K) for more details

Page | 287

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if p-value< α Significance: α= 0.05 p-value = 0.51 Since p-value> α, H0 was not rejected and both variances are equal.

Testing the equality of two means:

H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = -1.69 p-value= 0.095 Since p-value > α, H0 was not rejected and both means are equal.

Page | 288

Confidence interval: 𝑥2-𝑥1 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ2- µ1 ≤ 𝑥2-𝑥1 + t α/2,v

𝑆2

+ 𝑛2

2

-7.86 ≤ µ2- µ1 ≤ 85.99 There is a 95% chance that the difference between the two means is within [-7.86, 85.99]. Since zero is within this interval then both means are equal. Thus, there is no improvement. The power of this test is 90%.

Page | 289

Alternative 2 The same Arena code of the can making line that was described in section 20.2 was used but with the new values of the mean time between failures obtained from alternative 2.

Table 4. 39: Summary of failure module of alternative 2.

Name

Up time (days)

Down time (min)

Failure 1

EXPO (11.24)

30

Failure 2

EXPO (50)

60

Failure 3

EXPO (62.5)

30

Failure 4

EXPO (9.71)

60

Failure 5

EXPO (32.26)

60

1

Table 4. 40: As-is model and alternative 1 statistics summary .

1

As-is model

Alternative 2

N

32

32

𝐱 (carton)

2,364.375

2,426.281

S (carton)

99.18433

60.79221

See Appendix (K) for more details

Page | 290

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if p-value< α Significance: α= 0.05 f0= 2.66 p-value = 7.02E-03 Since p-value< α, H0 was rejected and the variances are not equal.

Testing the equality of two means: H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = -3.05 p-value= 3.5E-03 Since p-value < α, H0 is not rejected and the means are not equal.

Page | 291

Confidence interval: 𝑥2-𝑥1 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ2- µ1 ≤ 𝑥2-𝑥1 + t α/2,v

𝑆2

+ 𝑛2

2

20.63 ≤ µ2- µ1 ≤ 103.18 There is a 95% chance that the difference between the two means is within [20.63, 103.18] and the mean of alternative 2 is always greater than the mean of the as-is model. Thus, there is an improvement. The power of this test is 90%. From the above confidence interval, it can be concluded that by applying the maintenance plan of alternative 2, the factory can increase production by 21 to 103 cartons daily. This translates a reduction in overtime hours and cost by 3.51-17.22%.

Page | 292

Alternative 3 The same Arena code of the can making line that was described in section 20.2 was used but with the new values of the mean time between failures obtained from alternative 3.

Table 4. 41: Summary of failure module of alternative 3.

Name

Up time (days)

Down time (min)

Failure 1

EXPO (27.15)

30

Failure 2

EXPO (28.49)

60

Failure 3

EXPO (26.20)

30

Failure 4

EXPO (28.27)

60

Failure 5

EXPO (26.33)

60

1

Table 4. 42: As-is model and alternative 1 statistics summary .

1

As-is model

Alternative 3

n

32

32

𝐱 (carton)

2,364.375

2,438.875

S (carton)

99.18433

51.271

See Appendix (K) for more details

Page | 293

Testing the equality of two variances: H0: 𝜎12= 𝜎22 H1: 𝜎12≠ 𝜎22 Test Statistic: f0 Decision Rule: Reject Ho if p-value< α Significance: α= 0.05 f0= 3.74 p-value = 3.4E-04 Since p-value< α, H0 was rejected and the variances are not equal.

Testing the equality of two means: H0: μ 1= µ2 H1: μ1≠ µ2 Test Statistic: t0 Significance Level: α= 0.05 Decision Rule: Reject H0 if | t0|> t α/2, v or p-value < α t0 = -3.83 p-value= 3.68E-04 Since p-value < α, H0 was rejected and the means are not equal.

Page | 294

Confidence interval: 𝑥2-𝑥1 - t α/2,v

𝑆12 𝑛1

𝑆2

𝑆12

2

𝑛1

+ 𝑛2 ≤ µ2- µ1 ≤ 𝑥2-𝑥1 + t α/2,v

𝑆2

+ 𝑛2

2

34.78 ≤ µ2- µ1 ≤ 114.22 There is a 95% confident that the difference between the two means is within [34.78, 114.22] and the mean of alternative 3 is always greater than the mean of the as-is model. Thus, there is an improvement. The power of this test is 90%. From the above confidence interval, it is concluded that by applying the maintenance plan of alternative 3, the factory can increase production by 35 to 114 cartons daily. This translates to a reduction in overtime hours and cost of 5.85-19.06%.

Page | 295

4.18 Summary of the Proposed Alternatives

After analyzing each alternative and comparing it to the as-is situation; the reduction in maintenance cost and the increase in both reliability and daily production for both lines, under each alternative, are summarized in the following table. Table 4. 43: Summary of proposed alternatives.

Criteria

Alternative 1

Alternative 2

Alternative 3

Maintenance Cost

-29%

-61.8%

-72.65%

+3.91%

+17.53%

+19.82%

+26.65%

+43.24%

+38.19%

No improvement

No improvement

No improvement

No improvement

+0.89 to +4.36%

+1.48 to +4.82%

No improvement

-3.51 to -17.22%

-5.85 to -19.06%

Reliability of Can Making Line Reliability of Filling Line Daily Production of Can Making Line Daily Production of Filling Line Overtime cost

As shown, alternative 3 is the best in all criteria.

Page | 296

4.18 Conclusion

The maintenance policies that the factory currently applies were studied and both the reliability and the maintenance cost were calculated. Then, the as-is system was simulated using Arena software under the current operational conditions and failure rates. Moreover, new maintenance policies were proposed to reduce the failure rates of the machines, the reliability and the maintenance cost were calculated for each alternative. The new policies were then simulated and compared with the as-is model and the best policy was selected based on the following criteria: highest increase in the reliability and production rate, and greatest reduction in the maintenance cost.

Page | 297

Page | 298

5. Inventory Management and Production Planning

Page | 299

Page | 300

5.1 Introduction

The National Canned Food Company produces a variety of canned foods produced based on demand. The lead time between placing an order and receiving it is 21 days. This period is set to ensure the availability of the relevant raw materials. In addition to its factory in Subhan, the company has a warehouse in Kabd for packing material, as well as a warehouse for exported goods located in Mina Abdullah. The factory has three raw material inventories. One is for labels (including can labels and special offers labels), spices inventory (for example, sugar and salt) and can plant inventory (such as copper wires and glue). The final product inventory has a capacity of 100,000 cans.

Figure 5.15: Inventory flow in the factory.

Problem description Page | 301



The company cannot meet the demand on time due to poor production plans.



Some processes take longer due to poor planning.



Excessive inventory is held in the system.



Lead time is relatively long for the final product.

Solution approach 1. Demand was forecasted for all 27 types of goods produced using past data. 2. The current production capacity was calculated

to determine if

demand can comfortably be covered. 3. Inventory plans were developed for raw material and production plans for finished products.

Methodology 1. Collected data for past three years for all goods. 2. Applied forecasting methods to determine the demand for the next year. 3. Selected best forecasting method. 4. Analyzed the current inventory system and order quantities for raw material. 5. Applied inventory models to determine optimum order quantities and compare with the current system. 6. Analyzed current production plan and lot sizes 7. Applied production planning models and determined optimum lot sizes for all products. 8. Checked the production capacity and matched it with the plan. 9. Adjusted capacity according to the demand. 10. Applied service level calculation to determine safety stock.

5.2 Analysis

Page | 302

1- Demand forecasting Demand forecasting is the activity of estimating the demand of products that consumers will purchase in the future. It involves techniques such as methods that can be used to predict the future demands or sales. Forecasting depends on the trend of the historical data ,and the company’s demand of the final products have a trend and seasonality in every September of each year, considering year (2006-2007-2008) . In our project the demand was forecasted for the next five years for capacity planning but only the demand forecasted of year 2009 was used for production planning. The appropriated method that will apply to forecast must be with least error after testing the MAD (Mean Absolute Deviation) from each method. The tested forecasting methods are: 

Moving average method



Exponential smoothing with trend method



Regression method



Winter’s method



Holt’s method

In our project Holt's Method has the least error, therefore it was used. Page | 303

Holt’s method This method is designed to track time series with linear trend. Two smoothing constant α and β must be specified for two smoothing equations. The equations are: St * = (α)*(Dt*) + (1-α)*(St-1* + Gt-1) Gt* = (β)*(St* - St-1*) + (1-β)*(Gt-1*) St-1* = Dt-1* Gt-1* = (Di* - Dj*) / (i – j) Ft,t+τ * =St* + τGt* Ft = Ft* (CQt*) Where St * is the value of the intercept, Gt* is the value of the slope, Ft* symbolizes the forecast of the deseasonalized unit and F t is the final forecast of the original units. To compute the value of Gt-1*, an approximate trend line should be obtained by eyeballing the data. The first point the trend line through is the value of ( i ) and the last point is the value of ( j ).

Baked Beans Page | 304

Table 5.1: The data of Avg. MA (12) and Ct for beaked beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

9330

Feb-06

9087

Mar-06

8481

Apr-06

9208

May-06

8724

Jun-06

9572

Jul-06

10299

10135.790

1.016

Aug-06

12480

10212.526

1.222

Sep-06

15751

10285.729

1.531

Oct-06

10299

10359.437

0.994

Nov-06

9208

10434.154

0.883

Dec-06

8724

10510.385

0.830

Jan-07

10263

10593.180

0.969

Feb-07

9996

10688.091

0.935

Mar-07

9330

10805.720

0.863

Apr-07

10129

10914.262

0.928

May-07

9596

10995.542

0.873

Jun-07

10529

11070.259

0.951

Jul-07

11329

11145.481

1.016

Aug-07

13728

11222.218

1.223

Sep-07

17326

11295.421

1.534

Oct-07

11329

11369.128

0.996

Nov-07

10129

11443.845

0.885

Dec-07

9596

11520.077

0.833

Jan-08

11195

11602.872

0.965

Page | 305

Feb-08

10905

11697.783

0.932

Mar-08

10178

11815.412

0.861

Apr-08

11050

11923.954

0.927

May-08

10468

12005.234

0.872

Jun-08

11486

12079.951

0.951

Jul-08

12359

Aug-08

14976

Sep-08

18901

Oct-08

12359

Nov-08

11050

Dec-08

10468

Baked Beans

Figure 5.2: Forecasting model for seasonality & trend for baked beans.

Page | 306

As mentioned previously the value of Gt-1* can only be determined if a trend line passing through the deseasonalized demand is drawn. The trend line passes through D10* and D30* which are the values of (i) and (j) respectively. All the forecasting data can be seen in Appendix O (D10* is 10344). Different values of α and β were generated. It happens to be that when α is 0.9 and β is 0.1, the error is at its minimum.

Baked Beans

Figure 5.3: Forecasted demand for baked beans.

From the figure 5.3 above, it can be seen that the forecasted demand is almost overlapping the actual demand. This indicates that the error is very low. After applying Holt's method, the following results were achieved: Mean Absolute Deviation = 12.542 Mean Square Error = 385.972 The following figures and tables pertain to the remaining products which were dealt with in exactly the same manner as the baked beans.

Page | 307

Black Eye Beans Table 5.2. The data of Avg. MA (12) and Ct for black eye beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

1323

Feb-06

1289

Mar-06

1203

Apr-06

1306

May-06

1237

Jun-06

1358

Jul-06

1461

1437.597

1.016

Aug-06

1770

1448.481

1.222

Sep-06

2234

1458.863

1.531

Oct-06

1461

1469.318

0.994

Nov-06

1306

1479.915

0.883

Dec-06

1237

1490.727

0.830

Jan-07

1456

1502.470

0.969

Feb-07

1418

1515.932

0.935

Mar-07

1323

1532.616

0.863

Apr-07

1437

1548.010

0.928

May-07

1361

1559.539

0.873

Jun-07

1493

1570.136

0.951

Jul-07

1607

1580.805

1.016

Aug-07

1947

1591.689

1.223

Sep-07

2457

1602.072

1.534

Oct-07

1607

1612.526

0.996

Nov-07

1437

1623.123

0.885

Dec-07

1361

1633.935

0.833

Page | 308

Jan-08

1588

1645.679

0.965

Feb-08

1547

1659.140

0.932

Mar-08

1444

1675.824

0.861

Apr-08

1567

1691.219

0.927

May-08

1485

1702.747

0.872

Jun-08

1629

1713.345

0.951

Jul-08

1753

Aug-08

2124

Sep-08

2681

Oct-08

1753

Nov-08

1567

Dec-08

1485

Black Eye Beans

Figure 5.4: Forecasting model for seasonality & trend for black eye beans.

It can clearly be seen in figure 5.4 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 1467 and 1713, respectively.

Page | 309

Black Eye Beans

Figure 5.5: Forecasted demand for balck eye beans.

The error, as shown below, is quite low. This indicates that the forecasting method used is applicable. Mean Absolute Deviation = 1.779 Mean Square Error = 7.765

Page | 310

Broad Beans Table 5.3: The data of Avg. MA (12) and Ct for broad beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

13234

Feb-06

12890

Mar-06

12031

Apr-06

13062

May-06

12375

Jun-06

13578

Jul-06

14609

14377.893

1.016

Aug-06

17703

14486.745

1.222

Sep-06

22343

14590.585

1.531

Oct-06

14609

14695.142

0.994

Nov-06

13062

14801.130

0.883

Dec-06

12375

14909.267

0.830

Jan-07

14558

15026.713

0.969

Feb-07

14180

15161.347

0.935

Mar-07

13234

15328.207

0.863

Apr-07

14369

15482.177

0.928

May-07

13612

15597.475

0.873

Jun-07

14936

15703.463

0.951

Jul-07

16070

15810.168

1.016

Aug-07

19473

15919.020

1.223

Sep-07

24578

16022.860

1.534

Oct-07

16070

16127.417

0.996

Nov-07

14369

16233.405

0.885

Dec-07

13612

16341.542

0.833

Page | 311

Jan-08

15881

16458.988

0.965

Feb-08

15469

16593.622

0.932

Mar-08

14437

16760.482

0.861

Apr-08

15675

16914.452

0.927

May-08

14850

17029.750

0.872

Jun-08

16294

17135.738

0.951

Jul-08

17531

Aug-08

21244

Sep-08

26812

Oct-08

17531

Nov-08

15675

Dec-08

14850

Broad Beans

Figure 5.6: Forecasting model for seasonality & trend for broad beans.

It can clearly be seen in figure 5.6 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 14673 and 17127, respectively.

Page | 312

Broad Beans

Figure 5.7: Forecasted demand.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 17.791 Mean Square Error = 776.660

Page | 313

4. Chick Peas Table 5.4: The data of Avg. MA (12) and Ct for chick peas.

Time

Demand (Dt)

Jan-06

17595

Feb-06

17138

Mar-06

15996

Apr-06

17367

May-06

16453

Jun-06

18052

Jul-06

Avg.MA(12)

Index (Ct)

19423

19115.814

1.016

Aug-06

23537

19260.537

1.222

Sep-06

29706

19398.595

1.531

Oct-06

19423

19537.605

0.994

Nov-06

17367

19678.520

0.883

Dec-06

16453

19822.290

0.830

Jan-07

19355

19978.439

0.969

Feb-07

18852

20157.438

0.935

Mar-07

17595

20379.284

0.863

Apr-07

19103

20583.990

0.928

May-07

18098

20737.283

0.873

Jun-07

19858

20878.197

0.951

Jul-07

21366

21020.064

1.016

Aug-07

25890

21164.787

1.223

Sep-07

32677

21302.845

1.534

Oct-07

21366

21441.855

0.996

Nov-07

19103

21582.770

0.885

Dec-07

18098

21726.540

0.833 Page | 314

Jan-08

21114

21882.689

0.965

Feb-08

20566

22061.688

0.932

Mar-08

19195

22283.534

0.861

Apr-08

20840

22488.240

0.927

May-08

19743

22641.533

0.872

Jun-08

21663

22782.447

0.951

Jul-08

23308

Aug-08

28244

Sep-08

35648

Oct-08

23308

Nov-08

20840

Dec-08

19743

Chick Peas

Figure 5.8: Forecasting model for seasonality & trend for chick peas.

It can clearly be seen in figure 5.8 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 19508 and 22771, respectively.

Page | 315

Chick Peas

Figure 5.9: Forecasted demand for chick peas.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 23.654 Mean Square Error = 1372.860

Page | 316

5. Chick Peas 10mm Table 5.5: The data of Avg. MA (12) and Ct. for chick peas 10 mm.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

2290

Feb-06

2230

Mar-06

2082

Apr-06

2260

May-06

2141

Jun-06

2349

Jul-06

2528

2487.708

1.016

Aug-06

3063

2506.542

1.222

Sep-06

3866

2524.508

1.531

Oct-06

2528

2542.599

0.994

Nov-06

2260

2560.937

0.883

Dec-06

2141

2579.648

0.830

Jan-07

2519

2599.969

0.969

Feb-07

2453

2623.263

0.935

Mar-07

2290

2652.134

0.863

Apr-07

2486

2678.774

0.928

May-07

2355

2698.724

0.873

Jun-07

2584

2717.062

0.951

Jul-07

2781

2735.524

1.016

Aug-07

3369

2754.358

1.223

Sep-07

4253

2772.325

1.534

Oct-07

2781

2790.416

0.996

Nov-07

2486

2808.754

0.885

Dec-07

2355

2827.464

0.833

Page | 317

Jan-08

2748

2847.785

0.965

Feb-08

2676

2871.080

0.932

Mar-08

2498

2899.951

0.861

Apr-08

2712

2926.591

0.927

May-08

2569

2946.540

0.872

Jun-08

2819

2964.879

0.951

Jul-08

3033

2859.3087

Aug-08

3676

Sep-08

4639

Oct-08

3033

Nov-08

2712

Dec-08

2569

Chick Peas 10mm

Figure 5.10: Forecasting model for seasonality & trend for chick peas 10mm.

It can clearly be seen in figure 5.10 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 2539 and 2963, respectively.

Page | 318

Chick Peas 10mm

Figure 5.11: Forecasted demand for chick peas 10mm.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 3.078 Mean Square Error = 23.251

Page | 319

6. Chick Peas with Chili Table 5.6: The data of Avg. MA (12) and Ct for chick peas with chilli.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

123

Feb-06

120

Mar-06

112

Apr-06

122

May-06

115

Jun-06

126

Jul-06

136

133.847

1.016

Aug-06

165

134.860

1.222

Sep-06

208

135.827

1.531

Oct-06

136

136.800

0.994

Nov-06

122

137.787

0.883

Dec-06

115

138.793

0.830

Jan-07

136

139.887

0.969

Feb-07

132

141.140

0.935

Mar-07

123

142.693

0.863

Apr-07

134

144.127

0.928

May-07

127

145.200

0.873

Jun-07

139

146.187

0.951

Jul-07

150

147.180

1.016

Aug-07

181

148.193

1.223

Sep-07

229

149.160

1.534

Oct-07

150

150.133

0.996

Nov-07

134

151.120

0.885

Dec-07

127

152.127

0.833

Page | 320

Jan-08

148

153.220

0.965

Feb-08

144

154.473

0.932

Mar-08

134

156.027

0.861

Apr-08

146

157.460

0.927

May-08

138

158.533

0.872

Jun-08

152

159.520

0.951

Jul-08

163

Aug-08

198

Sep-08

250

Oct-08

163

Nov-08

146

Dec-08

138

ilihC htiw saeP kcihC

Figure 5.12: Forecasting model for seasonality & trend for chick peas with chili.

It can clearly be seen in figure 5.12 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 137 and 159, respectively.

Page | 321

ilihC htiw saeP kcihC

Figure 5.13: Forecasted demand for chick peas with chili.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.166 Mean Square Error = 0.067

Page | 322

7. Fava Beans Table 5.7: The data of Avg. MA (12) and Ct for fava beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

14129

Feb-06

13762

Mar-06

12845

Apr-06

13946

May-06

13212

Jun-06

14496

Jul-06

15597

15350.121

1.016

Aug-06

18900

15466.335

1.222

Sep-06

23854

15577.196

1.531

Oct-06

15597

15688.823

0.994

Nov-06

13946

15801.978

0.883

Dec-06

13212

15917.427

0.830

Jan-07

15542

16042.815

0.969

Feb-07

15138

16186.553

0.935

Mar-07

14129

16364.696

0.863

Apr-07

15340

16529.077

0.928

May-07

14533

16652.171

0.873

Jun-07

15946

16765.327

0.951

Jul-07

17157

16879.246

1.016

Aug-07

20790

16995.460

1.223

Sep-07

26240

17106.321

1.534

Oct-07

17157

17217.948

0.996

Nov-07

15340

17331.103

0.885

Dec-07

14533

17446.552

0.833

Page | 323

Jan-08

16955

17571.940

0.965

Feb-08

16515

17715.678

0.932

Mar-08

15414

17893.821

0.861

Apr-08

16735

18058.202

0.927

May-08

15854

18181.296

0.872

Jun-08

17395

18294.452

0.951

Jul-08

18716

Aug-08

22680

Sep-08

28625

Oct-08

18716

Nov-08

16735

Dec-08

15854

snaeB avaF

Figure 5.14: Forecasting model for seasonality & trend for fava beans.

It can clearly be seen in figure 5.14 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 15665 and 18286, respectively.

Page | 324

snaeB avaF

Figure 5.15: Forecasted demand for fava beans.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 18.994 Mean Square Error = 885.247

Page | 325

8. Fava Beans with Chili Table 5.8: The data of Avg. MA (12) and Ct for fava beans with chili.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

179

Feb-06

174

Mar-06

163

Apr-06

177

May-06

167

Jun-06

184

Jul-06

198

194.496

1.016

Aug-06

239

195.968

1.222

Sep-06

302

197.373

1.531

Oct-06

198

198.788

0.994

Nov-06

177

200.221

0.883

Dec-06

167

201.684

0.830

Jan-07

197

203.273

0.969

Feb-07

192

205.094

0.935

Mar-07

179

207.351

0.863

Apr-07

194

209.434

0.928

May-07

184

210.994

0.873

Jun-07

202

212.428

0.951

Jul-07

217

213.871

1.016

Aug-07

263

215.343

1.223

Sep-07

332

216.748

1.534

Oct-07

217

218.163

0.996

Nov-07

194

219.596

0.885

Dec-07

184

221.059

0.833 Page | 326

Jan-08

215

222.648

0.965

Feb-08

209

224.469

0.932

Mar-08

195

226.726

0.861

Apr-08

212

228.809

0.927

May-08

201

230.369

0.872

Jun-08

220

231.803

0.951

Jul-08

237

Aug-08

287

Sep-08

363

Oct-08

237

Nov-08

212

Dec-08

201

ilihC htiw snaeB avaF

Figure 5.16: Forecasting model for seasonality & trend for fava beans with chili.

It can clearly be seen in figure 5.16 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 198 and 232, respectively.

Page | 327

ilihC htiw snaeB avaF

Figure 5.17: Forecasted demand for fava beans with chili.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.241 Mean Square Error = 0.142

Page | 328

9. Egyptian Foul Medames Table 5.9: The data of Avg. MA (12) and Ct for foul medames - Egyptian.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

1686

Feb-06

1642

Mar-06

1533

Apr-06

1664

May-06

1576

Jun-06

1730

Jul-06

1861

1831.608

1.016

Aug-06

2255

1845.475

1.222

Sep-06

2846

1858.703

1.531

Oct-06

1861

1872.023

0.994

Nov-06

1664

1885.524

0.883

Dec-06

1576

1899.300

0.830

Jan-07

1855

1914.262

0.969

Feb-07

1806

1931.413

0.935

Mar-07

1686

1952.669

0.863

Apr-07

1830

1972.283

0.928

May-07

1734

1986.971

0.873

Jun-07

1903

2000.473

0.951

Jul-07

2047

2014.066

1.016

Aug-07

2481

2027.933

1.223

Sep-07

3131

2041.161

1.534

Oct-07

2047

2054.481

0.996

Nov-07

1830

2067.983

0.885

Dec-07

1734

2081.758

0.833

Page | 329

Jan-08

2023

2096.720

0.965

Feb-08

1971

2113.871

0.932

Mar-08

1839

2135.127

0.861

Apr-08

1997

2154.742

0.927

May-08

1892

2169.430

0.872

Jun-08

2076

2182.932

0.951

Jul-08

2233

Aug-08

2706

Sep-08

3416

Oct-08

2233

Nov-08

1997

Dec-08

1892

semadeM luoF naitpygE

Figure 5.18: Forecasting model for seasonality & trend for foul medames - Egyptain.

It can clearly be seen in figure 5.18 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 1869 and 2182, respectively.

Page | 330

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 2.266 Mean Square Error = 12.604

10. Saudi Foul Medames Table 5.10: The data of Avg. MA (12) and Ct Saudi Foul Medames.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

100

Feb-06

98

Mar-06

91

Apr-06

99

May-06

94

Jun-06

103

Jul-06

111

109.169

1.016

Aug-06

134

109.995

1.222

Sep-06

170

110.784

1.531

Oct-06

111

111.578

0.994

Nov-06

99

112.382

0.883

Dec-06

94

113.203

0.830

Jan-07

111

114.095

0.969

Feb-07

108

115.117

0.935

Mar-07

100

116.384

0.863

Apr-07

109

117.553

0.928

May-07

103

118.429

0.873

Jun-07

113

119.234

0.951

Jul-07

122

120.044

1.016

Aug-07

148

120.870

1.223

Page | 331

Sep-07

187

121.659

1.534

Oct-07

122

122.453

0.996

Nov-07

109

123.257

0.885

Dec-07

103

124.078

0.833

Jan-08

121

124.970

0.965

Feb-08

117

125.992

0.932

Mar-08

110

127.259

0.861

Apr-08

119

128.428

0.927

May-08

113

129.304

0.872

Jun-08

124

130.109

0.951

Jul-08

133

Aug-08

161

Sep-08

204

Oct-08

133

Nov-08

119

Dec-08

113

Saudi Foul Medames

Figure 5.20: Forecasting model for seasonality & trend for Saudi Foul Medames.

Page | 332

It can clearly be seen in figure 5.20 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 111 and 130, respectively.

Saudi Foul Medames

Figure 5.21: Forecasted demand for Saudi Foul Medames.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 570.825 Mean Square Error = 338502.937

Page | 333

11. Lebanese Foul Medames Table 5.11: The data of Avg. MA (12) and Ct for Lebanese foul medames.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

540

Feb-06

526

Mar-06

491

Apr-06

533

May-06

505

Jun-06

554

Jul-06

596

586.667

1.016

Aug-06

722

591.108

1.222

Sep-06

912

595.345

1.531

Oct-06

596

599.612

0.994

Nov-06

533

603.936

0.883

Dec-06

505

608.349

0.830

Jan-07

594

613.141

0.969

Feb-07

579

618.634

0.935

Mar-07

540

625.443

0.863

Apr-07

586

631.725

0.928

May-07

555

636.430

0.873

Jun-07

609

640.754

0.951

Jul-07

656

645.108

1.016

Aug-07

795

649.550

1.223

Sep-07

1003

653.787

1.534

Oct-07

656

658.053

0.996

Nov-07

586

662.378

0.885

Dec-07

555

666.790

0.833

Page | 334

Jan-08

648

671.582

0.965

Feb-08

631

677.076

0.932

Mar-08

589

683.884

0.861

Apr-08

640

690.167

0.927

May-08

606

694.871

0.872

Jun-08

665

699.196

0.951

Jul-08

715

Aug-08

867

Sep-08

1094

Oct-08

715

Nov-08

640

Dec-08

606

Lebanese Foul Medames

Figure5.22: Forecasting model for seasonality & trend for Lebanese foul medames.

It can clearly be seen in figure 5.22 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 599 and 699, respectively.

Page | 335

Lebanese Foul Medames

Figure 5.23: Forecasted demand for Lebanese foul medames.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.726 Mean Square Error = 1.293

Page | 336

12. Green Peas Table 5.12: The data of Avg. MA (12) and Ct for green peas.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

19444

Feb-06

18939

Mar-06

17677

Apr-06

19192

May-06

18182

Jun-06

19949

Jul-06

21465

21124.768

1.016

Aug-06

26010

21284.701

1.222

Sep-06

32828

21437.268

1.531

Oct-06

21465

21590.888

0.994

Nov-06

19192

21746.611

0.883

Dec-06

18182

21905.492

0.830

Jan-07

21389

22078.050

0.969

Feb-07

20833

22275.862

0.935

Mar-07

19444

22521.021

0.863

Apr-07

21111

22747.242

0.928

May-07

20000

22916.644

0.873

Jun-07

21944

23072.368

0.951

Jul-07

23611

23229.143

1.016

Aug-07

28611

23389.076

1.223

Sep-07

36111

23541.643

1.534

Oct-07

23611

23695.263

0.996

Nov-07

21111

23850.986

0.885

Dec-07

20000

24009.867

0.833

Page | 337

Jan-08

23333

24182.425

0.965

Feb-08

22727

24380.237

0.932

Mar-08

21212

24625.396

0.861

Apr-08

23030

24851.617

0.927

May-08

21818

25021.019

0.872

Jun-08

23939

25176.743

0.951

Jul-08

25758

Aug-08

31212

Sep-08

39394

Oct-08

25758

Nov-08

23030

Dec-08

21818

Green Peas

Figure 5.24: Forecasting model for seasonality & trend for green peas.

It can clearly be seen in figure 5.24 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 21559 and 25165, respectively.

Page | 338

Green Peas

Figure 5.25: Forecasted demand.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 26.140 Mean Square Error = 1676.581

Page | 339

13. Hummus Tahineh - Chick Peas 7 mm Table 5.13: The data of Avg. MA (12) and Ct for Hummus Tahineh - Chick Peas 7 mm.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

10494

Feb-06

10221

Mar-06

9540

Apr-06

10358

May-06

9813

Jun-06

10767

Jul-06

11584

11400.808

1.016

Aug-06

14037

11487.122

1.222

Sep-06

17717

11569.461

1.531

Oct-06

11584

11652.368

0.994

Nov-06

10358

11736.410

0.883

Dec-06

9813

11822.156

0.830

Jan-07

11543

11915.284

0.969

Feb-07

11244

12022.041

0.935

Mar-07

10494

12154.351

0.863

Apr-07

11393

12276.439

0.928

May-07

10794

12367.864

0.873

Jun-07

11843

12451.906

0.951

Jul-07

12743

12536.516

1.016

Aug-07

15441

12622.830

1.223

Sep-07

19489

12705.169

1.534

Oct-07

12743

12788.076

0.996

Nov-07

11393

12872.118

0.885

Dec-07

10794

12957.864

0.833

Page | 340

Jan-08

12593

13050.992

0.965

Feb-08

12266

13157.749

0.932

Mar-08

11448

13290.059

0.861

Apr-08

12429

13412.148

0.927

May-08

11775

13503.572

0.872

Jun-08

12920

13587.615

0.951

Jul-08

13901

Aug-08

16845

Sep-08

21260

Oct-08

13901

Nov-08

12429

Dec-08

11775

Hummus Tahineh - Chick Peas 7 mm

Figure 5.26: Forecasting model for seasonality & trend for Hummus Tahineh - Chick Peas 7 mm.

It can clearly be seen in figure 5.26 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 11635 and 13581, respectively.

Page | 341

Hummus Tahineh - Chick Peas 7 mm

Figure 5.27: Forecasted demand for Hummus Tahineh - Chick Peas 7 mm.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 14.107 Mean Square Error = 488.328

Page | 342

14. Hummus Tahineh with Garlic Table 5.14: The data of Avg. MA (12) and Ct for Hummus Tahineh with Garlic.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

72

Feb-06

70

Mar-06

66

Apr-06

71

May-06

68

Jun-06

74

Jul-06

80

78.468

1.016

Aug-06

97

79.062

1.222

Sep-06

122

79.628

1.531

Oct-06

80

80.199

0.994

Nov-06

71

80.777

0.883

Dec-06

68

81.368

0.830

Jan-07

79

82.009

0.969

Feb-07

77

82.743

0.935

Mar-07

72

83.654

0.863

Apr-07

78

84.494

0.928

May-07

74

85.124

0.873

Jun-07

82

85.702

0.951

Jul-07

88

86.284

1.016

Aug-07

106

86.878

1.223

Sep-07

134

87.445

1.534

Oct-07

88

88.016

0.996

Nov-07

78

88.594

0.885

Dec-07

74

89.184

0.833

Page | 343

Jan-08

87

89.825

0.965

Feb-08

84

90.560

0.932

Mar-08

79

91.471

0.861

Apr-08

86

92.311

0.927

May-08

81

92.940

0.872

Jun-08

89

93.519

0.951

Jul-08

96

Aug-08

116

Sep-08

146

Oct-08

96

Nov-08

86

Dec-08

81

Hummus Tahineh with Garlic

Figure 5.28: Forecasting model for seasonality & trend for Hummus Tahineh with Garlic.

It can clearly be seen in figure 5.28 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 80 and 93,

respectively.

Page | 344

Hummus Tahineh with Garlic

Figure 5.29: Forecasted demand for Hummus Tahineh with Garlic.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.097 Mean Square Error = 0.023

Page | 345

15. Hotdog Sausage Table 5.15: The data of Avg. MA (12) and Ct for hotdog sausage.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

63

Feb-06

61

Mar-06

57

Apr-06

62

May-06

59

Jun-06

64

Jul-06

69

68.011

1.016

Aug-06

84

68.526

1.222

Sep-06

106

69.017

1.531

Oct-06

69

69.512

0.994

Nov-06

62

70.013

0.883

Dec-06

59

70.524

0.830

Jan-07

69

71.080

0.969

Feb-07

67

71.717

0.935

Mar-07

63

72.506

0.863

Apr-07

68

73.234

0.928

May-07

64

73.780

0.873

Jun-07

71

74.281

0.951

Jul-07

76

74.786

1.016

Aug-07

92

75.301

1.223

Sep-07

116

75.792

1.534

Oct-07

76

76.287

0.996

Nov-07

68

76.788

0.885

Dec-07

64

77.299

0.833

Page | 346

Jan-08

75

77.855

0.965

Feb-08

73

78.492

0.932

Mar-08

68

79.281

0.861

Apr-08

74

80.009

0.927

May-08

70

80.555

0.872

Jun-08

77

81.056

0.951

Jul-08

83

Aug-08

100

Sep-08

127

Oct-08

83

Nov-08

74

Dec-08

70

Hotdog Sausage

Figure 5.30: Forecasting model for seasonality & trend for hotdog sausage.

From It can clearly be seen in figure 5.30 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 69 and 81, respectively.

Page | 347

Hotdog Sausage

Figure 5.31: Forecasted demand for hotdog sausage.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.084 Mean Square Error =0.017

Page | 348

16. Frankfurter Sausage Table 5.16: The data of Avg. MA (12) and Ct for frankfurter sausage.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

103

Feb-06

100

Mar-06

94

Apr-06

102

May-06

96

Jun-06

106

Jul-06

114

111.929

1.016

Aug-06

138

112.777

1.222

Sep-06

174

113.585

1.531

Oct-06

114

114.399

0.994

Nov-06

102

115.224

0.883

Dec-06

96

116.066

0.830

Jan-07

113

116.980

0.969

Feb-07

110

118.028

0.935

Mar-07

103

119.327

0.863

Apr-07

112

120.526

0.928

May-07

106

121.424

0.873

Jun-07

116

122.249

0.951

Jul-07

125

123.079

1.016

Aug-07

152

123.927

1.223

Sep-07

191

124.735

1.534

Oct-07

125

125.549

0.996

Nov-07

112

126.374

0.885

Dec-07

106

127.216

0.833

Page | 349

Jan-08

124

128.130

0.965

Feb-08

120

129.178

0.932

Mar-08

112

130.477

0.861

Apr-08

122

131.676

0.927

May-08

116

132.574

0.872

Jun-08

127

133.399

0.951

Jul-08

136

Aug-08

165

Sep-08

209

Oct-08

136

Nov-08

122

Dec-08

116

Frankfurter Sausage

Figure 5.32: Forecasting model for seasonality & trend for frankfurter sausage.

It can clearly be seen in figure 5.32 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 114 and 133, respectively.

Page | 350

Frankfurter Sausage

Figure 5.33: Forecasted demand for frankfurter sausage.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.139 Mean Square Error = 0.047

Page | 351

17. Cocktail Sausage Table 5.17: The data of Avg. MA (12) and Ct for cocktail sausage.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

283

Feb-06

276

Mar-06

257

Apr-06

279

May-06

265

Jun-06

290

Jul-06

312

307.429

1.016

Aug-06

379

309.757

1.222

Sep-06

478

311.977

1.531

Oct-06

312

314.213

0.994

Nov-06

279

316.479

0.883

Dec-06

265

318.791

0.830

Jan-07

311

321.302

0.969

Feb-07

303

324.181

0.935

Mar-07

283

327.749

0.863

Apr-07

307

331.041

0.928

May-07

291

333.506

0.873

Jun-07

319

335.773

0.951

Jul-07

344

338.054

1.016

Aug-07

416

340.382

1.223

Sep-07

526

342.602

1.534

Oct-07

344

344.838

0.996

Nov-07

307

347.104

0.885

Dec-07

291

349.416

0.833

Page | 352

Jan-08

340

351.927

0.965

Feb-08

331

354.806

0.932

Mar-08

309

358.374

0.861

Apr-08

335

361.666

0.927

May-08

318

364.131

0.872

Jun-08

348

366.398

0.951

Jul-08

375

Aug-08

454

Sep-08

573

Oct-08

375

Nov-08

335

Dec-08

318

Cocktail Sausage

Figure 5.34: Forecasting model for seasonality & trend for cocktail sausage.

It can clearly be seen in figure 5.34 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 314 and 366, respectively.

Page | 353

Cocktail Sausage

Figure 5.35: Forecasted demand for cocktail sausage.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.380 Mean Square Error = 0.355

Page | 354

18. Lima Beans Table 5.18: The data of Avg. MA (12) and Ct for lima beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

253

Feb-06

246

Mar-06

230

Apr-06

249

May-06

236

Jun-06

259

Jul-06

279

274.386

1.016

Aug-06

338

276.463

1.222

Sep-06

426

278.445

1.531

Oct-06

279

280.440

0.994

Nov-06

249

282.463

0.883

Dec-06

236

284.526

0.830

Jan-07

278

286.768

0.969

Feb-07

271

289.337

0.935

Mar-07

253

292.521

0.863

Apr-07

274

295.460

0.928

May-07

260

297.660

0.873

Jun-07

285

299.683

0.951

Jul-07

307

301.719

1.016

Aug-07

372

303.796

1.223

Sep-07

469

305.778

1.534

Oct-07

307

307.773

0.996

Nov-07

274

309.796

0.885

Dec-07

260

311.860

0.833

Page | 355

Jan-08

303

314.101

0.965

Feb-08

295

316.670

0.932

Mar-08

276

319.855

0.861

Apr-08

299

322.793

0.927

May-08

283

324.993

0.872

Jun-08

311

327.016

0.951

Jul-08

335

Aug-08

405

Sep-08

512

Oct-08

335

Nov-08

299

Dec-08

283

Lima Beans

Figure 5.36: Forecasting model for seasonality & trend for lima beans.

It can clearly be seen in figure 5.36 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 280 and 327, respectively.

Page | 356

Lima Beans

Figure 5.37: Forecasted demand for lima beans.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.340 Mean Square Error = 0.283

Page | 357

19. Mixed Vegetables Table 5.19: The data of Avg. MA (12) and Ct for mixed vegetables.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

941

Feb-06

917

Mar-06

855

Apr-06

929

May-06

880

Jun-06

965

Jul-06

1039

1022.254

1.016

Aug-06

1259

1029.993

1.222

Sep-06

1589

1037.376

1.531

Oct-06

1039

1044.810

0.994

Nov-06

929

1052.346

0.883

Dec-06

880

1060.034

0.830

Jan-07

1035

1068.384

0.969

Feb-07

1008

1077.957

0.935

Mar-07

941

1089.820

0.863

Apr-07

1022

1100.767

0.928

May-07

968

1108.965

0.873

Jun-07

1062

1116.501

0.951

Jul-07

1143

1124.087

1.016

Aug-07

1385

1131.827

1.223

Sep-07

1747

1139.210

1.534

Oct-07

1143

1146.643

0.996

Nov-07

1022

1154.179

0.885

Dec-07

968

1161.867

0.833

Page | 358

Jan-08

1129

1170.218

0.965

Feb-08

1100

1179.790

0.932

Mar-08

1026

1191.654

0.861

Apr-08

1114

1202.601

0.927

May-08

1056

1210.798

0.872

Jun-08

1158

1218.334

0.951

Jul-08

1246

Aug-08

1510

Sep-08

1906

Oct-08

1246

Nov-08

1114

Dec-08

1056

Mixed Vegetables

Figure 5.38: Forecasting model for seasonality & trend for mixed vegetables.

It can clearly be seen in figure 5.38 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 1043 and 1218, respectively.

Page | 359

Mixed Vegetables

Figure 5.39 Forecasted demand for mixed vegetables.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 1.265 Mean Square Error = 3.926

Page | 360

20. Mushroom Pieces and Stems Table 5.20: The data of Avg. MA (12) and Ct for mushroom pieces and stems.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

488

Feb-06

475

Mar-06

444

Apr-06

482

May-06

456

Jun-06

501

Jul-06

539

530.200

1.016

Aug-06

653

534.214

1.222

Sep-06

824

538.043

1.531

Oct-06

539

541.899

0.994

Nov-06

482

545.807

0.883

Dec-06

456

549.795

0.830

Jan-07

537

554.126

0.969

Feb-07

523

559.091

0.935

Mar-07

488

565.244

0.863

Apr-07

530

570.922

0.928

May-07

502

575.174

0.873

Jun-07

551

579.082

0.951

Jul-07

593

583.017

1.016

Aug-07

718

587.031

1.223

Sep-07

906

590.860

1.534

Oct-07

593

594.716

0.996

Nov-07

530

598.624

0.885

Page | 361

Dec-07

502

602.612

0.833

Jan-08

586

606.943

0.965

Feb-08

570

611.907

0.932

Mar-08

532

618.061

0.861

Apr-08

578

623.738

0.927

May-08

548

627.990

0.872

Jun-08

601

631.899

0.951

Jul-08

646

Aug-08

783

Sep-08

989

Oct-08

646

Nov-08

578

Dec-08

548

Mushroom Pieces and Stems

Figure 5.40: Forecasting model for seasonality & trend for mushroom pieces and stems.

It can clearly be seen in figure 5.40 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 541 and 632, respectively.

Page | 362

Mushroom Pieces and Stems

Figure 5.41: Forecasted demand for mushroom pieces and stems.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.656 Mean Square Error = 1.056

Page | 363

21. Whole Mushrooms Table 5.21: The data of Avg. MA (12) and Ct for whole mushrooms.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

628

Feb-06

612

Mar-06

571

Apr-06

620

May-06

587

Jun-06

644

Jul-06

693

682.200

1.016

Aug-06

840

687.365

1.222

Sep-06

1060

692.292

1.531

Oct-06

693

697.253

0.994

Nov-06

620

702.281

0.883

Dec-06

587

707.412

0.830

Jan-07

691

712.985

0.969

Feb-07

673

719.373

0.935

Mar-07

628

727.290

0.863

Apr-07

682

734.596

0.928

May-07

646

740.066

0.873

Jun-07

709

745.095

0.951

Jul-07

762

750.158

1.016

Aug-07

924

755.323

1.223

Sep-07

1166

760.250

1.534

Oct-07

762

765.211

0.996

Nov-07

682

770.240

0.885

Dec-07

646

775.371

0.833

Page | 364

Jan-08

754

780.943

0.965

Feb-08

734

787.331

0.932

Mar-08

685

795.248

0.861

Apr-08

744

802.554

0.927

May-08

705

808.025

0.872

Jun-08

773

813.054

0.951

Jul-08

832

Aug-08

1008

Sep-08

1272

Oct-08

832

Nov-08

744

Dec-08

705

Whole Mushrooms

Figure 5.42: Forecasting model for seasonality & trend for whole mushrooms.

It can clearly be seen in figure 5.42 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 696 and 813, respectively.

Page | 365

Whole Mushrooms

Figure 5.43: Forecasted demand for whole mushrooms.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.844 Mean Square Error = 1.748

Page | 366

22. Peas and Carrots Table 5.22:The data of Avg. MA (12) and Ct for peas and carrots .

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

137

Feb-06

134

Mar-06

125

Apr-06

135

May-06

128

Jun-06

141

Jul-06

151

148.904

1.016

Aug-06

183

150.032

1.222

Sep-06

231

151.107

1.531

Oct-06

151

152.190

0.994

Nov-06

135

153.288

0.883

Dec-06

128

154.408

0.830

Jan-07

151

155.624

0.969

Feb-07

147

157.018

0.935

Mar-07

137

158.746

0.863

Apr-07

149

160.341

0.928

May-07

141

161.535

0.873

Jun-07

155

162.633

0.951

Jul-07

166

163.738

1.016

Aug-07

202

164.865

1.223

Sep-07

255

165.941

1.534

Oct-07

166

167.023

0.996

Nov-07

149

168.121

0.885

Dec-07

141

169.241

0.833

Page | 367

Jan-08

164

170.457

0.965

Feb-08

160

171.852

0.932

Mar-08

150

173.580

0.861

Apr-08

162

175.174

0.927

May-08

154

176.368

0.872

Jun-08

169

177.466

0.951

Jul-08

182

Aug-08

220

Sep-08

278

Oct-08

182

Nov-08

162

Dec-08

154

Peas and carrots

Figure 5.44: Forecasting model for seasonality & trend for peas and carrots.

It can clearly be seen in figure 5.44 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 152 and 177, respectively.

Page | 368

Peas and carrots

Figure 5.45: Forecasted demand for peas and carrots.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.184 Mean Square Error = 0.083

Page | 369

23. Peeled Fava Beans with Chilli Table 5.23: The data of Avg. MA (12) and Ct for peeled fava with chilli .

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

617

Feb-06

601

Mar-06

561

Apr-06

609

May-06

577

Jun-06

633

Jul-06

682

670.739

1.016

Aug-06

826

675.817

1.222

Sep-06

1042

680.661

1.531

Oct-06

682

685.539

0.994

Nov-06

609

690.483

0.883

Dec-06

577

695.528

0.830

Jan-07

679

701.007

0.969

Feb-07

661

707.288

0.935

Mar-07

617

715.072

0.863

Apr-07

670

722.255

0.928

May-07

635

727.634

0.873

Jun-07

697

732.578

0.951

Jul-07

750

737.556

1.016

Aug-07

908

742.634

1.223

Sep-07

1147

747.478

1.534

Oct-07

750

752.356

0.996

Nov-07

670

757.300

0.885

Dec-07

635

762.345

0.833

Page | 370

Jan-08

741

767.824

0.965

Feb-08

722

774.104

0.932

Mar-08

674

781.889

0.861

Apr-08

731

789.071

0.927

May-08

693

794.450

0.872

Jun-08

760

799.395

0.951

Jul-08

818

Aug-08

991

Sep-08

1251

Oct-08

818

Nov-08

731

Dec-08

693

Peeled Fava Beans with Chili

Figure 5.46: Forecasting model for seasonality & trend for peeled fava beans with chili.

It can clearly be seen in figure 5.46 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 685 and 799, respectively

Page | 371

Peeled Fava Beans with Chilli

Figure 5.47: Forecasted demand for peeled fava beans with chili.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.830 Mean Square Error = 1.690

Page | 372

24. Red Kidney Beans Table 5.24: The data of Avg. MA (12) and Ct for red kidney beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

2067

Feb-06

2013

Mar-06

1879

Apr-06

2040

May-06

1932

Jun-06

2120

Jul-06

2281

2245.111

1.016

Aug-06

2764

2262.108

1.222

Sep-06

3489

2278.323

1.531

Oct-06

2281

2294.649

0.994

Nov-06

2040

2311.199

0.883

Dec-06

1932

2328.085

0.830

Jan-07

2273

2346.424

0.969

Feb-07

2214

2367.447

0.935

Mar-07

2067

2393.502

0.863

Apr-07

2244

2417.545

0.928

May-07

2126

2435.549

0.873

Jun-07

2332

2452.099

0.951

Jul-07

2509

2468.761

1.016

Aug-07

3041

2485.758

1.223

Sep-07

3838

2501.973

1.534

Oct-07

2509

2518.299

0.996

Nov-07

2244

2534.849

0.885

Dec-07

2126

2551.735

0.833

Page | 373

Jan-08

2480

2570.074

0.965

Feb-08

2415

2591.097

0.932

Mar-08

2254

2617.152

0.861

Apr-08

2448

2641.195

0.927

May-08

2319

2659.199

0.872

Jun-08

2544

2675.749

0.951

Jul-08

2737

Aug-08

3317

Sep-08

4187

Oct-08

2737

Nov-08

2448

Dec-08

2319

Red Kidney Beans

Figure 5.48: Forecasting model for seasonality & trend for red kidney beans.

It can clearly be seen in figure 5.48 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 2291 and 2674, respectively.

Page | 374

Red Kidney Beans

Figure 5.49: Forecasted demand for red kidney beans.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 2.778 Mean Square Error = 18.937

Page | 375

25. Red Kidney Beans with Chili Table 5.25: The data of Avg. MA (12) and Ct for red kidney beans with chili.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

58

Feb-06

56

Mar-06

53

Apr-06

57

May-06

54

Jun-06

59

Jul-06

64

62.741

1.016

Aug-06

77

63.216

1.222

Sep-06

98

63.669

1.531

Oct-06

64

64.125

0.994

Nov-06

57

64.588

0.883

Dec-06

54

65.059

0.830

Jan-07

64

65.572

0.969

Feb-07

62

66.159

0.935

Mar-07

58

66.888

0.863

Apr-07

63

67.559

0.928

May-07

59

68.063

0.873

Jun-07

65

68.525

0.951

Jul-07

70

68.991

1.016

Aug-07

85

69.466

1.223

Sep-07

107

69.919

1.534

Oct-07

70

70.375

0.996

Nov-07

63

70.838

0.885

Dec-07

59

71.309

0.833

Page | 376

Jan-08

69

71.822

0.965

Feb-08

68

72.409

0.932

Mar-08

63

73.138

0.861

Apr-08

68

73.809

0.927

May-08

65

74.313

0.872

Jun-08

71

74.775

0.951

Jul-08

77

Aug-08

93

Sep-08

117

Oct-08

77

Nov-08

68

Dec-08

65

Red Kidney Beans with Chili

Figure 5.50: Forecasting model for seasonality & trend for red kidney beans with chili.

It can clearly be seen in figure 5.50 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 64 and 75, respectively.

Page | 377

Red Kidney Beans with Chili

Figure 5.51: Forecasted demand for red kidney beans with chili.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 0.078 Mean Square Error = 0.015

Page | 378

26. Sweet Corn Table 5.26: The data of Avg. MA (12) and Ct for sweet corn.

Demand (Dt)

Avg.MA(12)

Index (Ct)

1863

1833.532

1.016

2258

1847.413

1.222

2849

1860.656

1.531

1863

1873.989

0.994

1666

1887.505

0.883

1578

1901.295

0.830

1856

1916.272

0.969

1808

1933.442

0.935

1688

1954.720

0.863

1832

1974.355

0.928

1736

1989.059

0.873

1905

2002.575

0.951

2049

2016.182

1.016

2483

2030.063

1.223

3134

2043.306

1.534

2049

2056.639

0.996

1832

2070.155

0.885

1736

2083.945

0.833

1688 1644 1534 1666 1578 1732

Page | 379

2025

2098.922

0.965

1973

2116.092

0.932

1841

2137.370

0.861

1999

2157.005

0.927

1894

2171.709

0.872

2078

2185.225

0.951

2236 2709 3419 2236 1999 1894

Sweet Corn

Figure 5.52: Forecasting model for seasonality & trend for sweet corn.

It can clearly be seen in figure 5.52 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 1871 and 2184, respectively

Page | 380

Sweet Corn

Figure 5.53: Forecasted demand for sweet corn.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 2.269 Mean Square Error = 12.630

Page | 381

27. White Beans Table 5.27: The data of Avg. MA (12) and Ct for white beans.

Time

Demand (Dt)

Avg.MA(12)

Index (Ct)

Jan-06

346

Feb-06

337

Mar-06

314

Apr-06

341

May-06

323

Jun-06

355

Jul-06

382

374.333

1.020

Aug-06

463

374.333

1.236

Sep-06

584

374.333

1.560

Oct-06

382

374.333

1.020

Nov-06

341

374.333

0.912

Dec-06

323

374.333

0.864

Jan-07

346

374.333

0.924

Feb-07

337

374.333

0.900

Mar-07

314

374.333

0.840

Apr-07

341

374.333

0.912

May-07

323

374.333

0.864

Jun-07

355

374.333

0.948

Jul-07

382

377.216

1.012

Aug-07

463

382.906

1.208

Sep-07

584

388.333

1.504

Oct-07

382

393.799

0.970

Nov-07

341

399.339

0.855

Dec-07

323

404.991

0.799

Page | 382

Jan-08

415

411.130

1.010

Feb-08

404

418.168

0.967

Mar-08

377

426.890

0.884

Apr-08

410

434.938

0.942

May-08

388

440.965

0.880

Jun-08

426

446.505

0.954

Jul-08

458

Aug-08

555

Sep-08

701

Oct-08

458

Nov-08

410

Dec-08

388

White Beans

Figure 5.54: Forecasting model for seasonality & trend for white beans.

It can clearly be seen in figure 5.54 above, that the trend line passes through points D10* and D30*. The values that correspond to D10* and D30* are 384 and 448,

respectively

Page | 383

White Beans

Figure 5.55: Forecasted demand for white beans.

After applying Holt's Method, the following results were obtained: Mean Absolute Deviation = 4.607 Mean Square Error = 119.103

Page | 384

Five Year Forecasts The following charts show the forecasted demands for the next five years for each type of product.

Baked Beans Baked Beans

Figure 5.56: Forecasted demand for baked beans.

2. Black Eye Beans

Black Eye Beans

Figure 5.57: Forecasted demand for black eye beans.

Page | 385

3. Broad Beans Broad Beans

Figure 5.58: Forecasted demand for broad beans.

4. Chick Peas Chick Peas

Figure 5.59: Forecasted demand for chick peas.

Page | 386

5. Chick Peas 10mm

Chick Peas 10mm

Figure 5.60: Forecasted demand for chick peas 10mm.

6. Chick Peas with Chili

Chick Peas with Chili

Figure 5.61: Forecasted demand for chick peas with chili.

Page | 387

7. Fava Beans

Fava Beans

Figure 5.62: Forecasted demand for fava beans.

8. Fava Beans with Chili

Fava Beans with Chili

Figure 5.63: Forecasted demand for fava beans with chili.

Page | 388

9. Egyptian Foul Medames Egyptian Foul Medames

Figure 5.64: Forecasted demand for Egyptian foul medames.

10. Saudi Foul Medames

Saudi Foul Medames

Figure 5.65: Forecasted demand for Saudi foul medames.

Page | 389

11. Lebanese Fould Medames

Lebanese Foul Medames

Figure 5.66: Forecasted demand for Lebanese foul medames.

12. Green Peas Green Peas

Figure 5.67: Forecasted demand for Green Peas.

Page | 390

13. Hummus Tahineh - Chick Peas 7mm Hummus Tahineh – Chick Peas 7mm

Figure 5.68: Forecasted demand for hummus tahineh – chick peas 7mm.

14. Hummus Tahineh with Garlic

Hummus Tahineh with Garlic

Figure 5.69: Forecasted demand for hummus tahineh with garlic.

Page | 391

15. Hotdog Sausage Hotdog Sausage

Figure 5.70: Forecasted demand for hotdog sausage.

16. Frankfurter Sausage Frankfurter Sausage

Figure 5.71: Forecasted demand for frankfurter sausage.

Page | 392

17. Cocktail Sausage

Cocktail Sausage

Figure 5.72: Forecasted demand for cocktail sausage.

18. Lima Beans Lima Beans

Figure 5.73: Forecasted demand for lima beans.

Page | 393

19. Mixed Vegetables Mixed Vegetables

Figure 5.74: Forecasted demand for mixed vegetables.

20. Mushroom Pieces and Stems

Mushroom Pieces and Stems

Figure 5.75: Forecasted demand for mushroom pieces and stems.

Page | 394

21. Whole Mushrooms Whole Mushrooms

Figure 5.76: Forecasted demand for whole mushrooms.

22. Peas and carrots

Peas and Carrots

Figure 5.77: Forecasted demand for peas and carrots.

Page | 395

23. Peeled Fava Beans with Chili Peeled Fava Beans with Chili

Figure 5.78: Forecasted demand for peeled fava beans with chili.

24. Red Kidney Beans

Red Kidney Beans

Figure 5.79: Forecasted demand for red kidney beans.

Page | 396

25. Red Kidney Beans with Chili Red Kidney Beans with Chili

Figure 5.80: Forecasted demand for red kidney beans with chili.

26. Sweet Corn

Sweet Corn

Figure 5.81: Forecasted demand for sweet corn.

Page | 397

27. White Beans White Beans

Figure 5.82: Forecasted demand for white beans.

Page | 398

Economic Order Quantity (EOQ) for Production Planning The EOQ is essentially an accounting formula that determines the point at which the combination of order costs and inventory carrying costs are the least. The result is the most cost effective quantity to order. In purchasing, this is known as the order quantity, whilst in manufacturing it is known as the production lot size. While EOQ may not apply to every inventory situation, most organizations will find it beneficial in at least some aspect of their operation.

Parameters: Q = Order quantity. Q * = Optimal order quantity. D = Annual demand quantity of the product (average demand for three years was used). P = Purchase cost per unit. C = A = Fixed cost per order. H= ht = total annual holding cost per unit (also known as carrying cost)/

Equations: TC = H Q/2 + A D/Q TC*= √ (2ADH) Q* = √ (2AD/H)

Page | 399

Table 5.28: The current and optimal quantities for the can plant.

Item

Unit

Q

Q*

Labels

CTN

2000

1474

Cooper Wire

K.G

4250

812

Lids

CTN

2000

1266

Tin-sheet

CTN

1000

847

Cartoon

CTN

1500

1030

Shrink Film

PCS

30000

26857

Glue

K.G

6751

3071

Lacquer

K.G

6179

1593

The optimal quantities (Q*) for each item of the can plant’s raw materials are less than the current quantities in the system. Table 5.29: The difference between the current total cost and the optimal total cost of the can plant.

Item

Unit

TC

TC*

TC-TC*

Labels

CTN

112

106

6

Cooper Wire

K.G

124

45

79

Lids

CTN

20

18

2

Tin-sheet

CTN

8

7

0

Cartoon

CTN

10

9

1

Shrink Film

PCS

8

7

0

Glue

K.G

60

44

16

Lacquer

K.G

75

36

39

Sum

143.58

After applying the EOQ model to the can plant raw materials, it was found that the optimal quantity saves a total of 143.58 KD/year.

Page | 400

Table 5.30: The current and optimal quantities for the spices.

Item

Unit

Q

Q*

Tomato Pasta

K.G

6000

3537

Lemon Juice

Ltr

500

339

Green Color

K.G

1000

405

Edta

K.G

1000

775

Citric Acid

K.G

3000

1960

Camon Powder

K.G

1000

596

Chick Peas Powder

K.G

2000

1695

Spices

K.G

1000

548

Whole Red Chili

K.G

500

381

Onion Powder

K.G

2000

706

Powder Red Chili

K.G

1200

1014

The optimal quantities (Q*) for each item of the spices raw material are less than the current quantities in the system.

Page | 401

Table 5.31: The difference between the current total cost and the optimal total cost of the spices.

Item

Unit

TC

TC*

TC-TC*

Tomato Pasta

K.G

40.0

34.49

5.51

Lemon Juice

Ltr

16.0

14.75

1.25

Green Color

K.G

48.0

33.37

14.63

Edta

K.G

12.0

11.62

0.38

Citric Acid

K.G

28.0

25.51

2.49

Camon Powder

K.G

16.0

13.42

2.58

Chick Peas Powder

K.G

17.0

16.52

0.48

Spices

K.G

20.0

16.43

3.57

Whole Red Chili

K.G

10.0

9.44

0.56

Onion Powder

K.G

38.0

23.81

14.14

Powder Red Chili

K.G

15.0

14.44

0.56

Sum=

46.1

After applying the EOQ model to the spices, it was found that the optimal quantities would save a total of 46.1 KD/year.

Page | 402

Table 5.32: The current and optimal quantities for the beans.

Item

Unit

Q

Q*

Black Eye Beans

K.G

10553

2096

Broad Beans

K.G

132234

17287

Chick Peas 8mm

K.G

101930

18899

Chick Peas 7mm

K.G

27500

4633

Chick Peas 10mm

K.G

46309

6619

Whole Mushrooms

K.G

18750

2972

Mushroom Stems and Pieces

K.G

18750

2412

Green Peas

K.G

61291

2419

Mixed Vegetables

K.G

25811

2013

Navy Beans

K.G

53905

5228

White Beans

K.G

18766

2499

Peeled Fava Beans

K.G

65000

11185

Fava Beans

K.G

71153

7396

Red Kidney

K.G

33869

2070

Sweet Corn

K.G

33572

5806

Lima Beans

K.G

19184

4229

Carrots

K.G

12000

4883

The optimal quantities (Q*) for each item of the beans raw material are less than the current quantities in the system.

Page | 403

Table 5.33: The difference between the current total cost and the optimal total cost of the beans.

Item

Unit

TC

TC*

TC-TC*

Black Eye Beans

K.G

75.44

42.44

33.01

Broad Beans

K.G

813.74

311.17

502.58

Chick Peas 8mm

K.G

643.12

340.18

302.93

Chick Peas 7mm

K.G

243.70

118.15

125.56

Chick Peas 10mm

K.G

322.16

134.04

188.12

Whole Mushrooms

K.G

291.85

133.72

158.12

Mushroom Stems and Pieces

K.G

288.23

108.53

179.70

Green Peas

K.G

261.10

30.84

230.26

Mixed Vegetables

K.G

240.93

55.85

185.08

Navy Beans

K.G

116.02

33.29

82.74

White Beans

K.G

269.71

104.95

164.76

Peeled Foul

K.G

594.01

293.61

300.41

Fava Beans

K.G

397.68

122.04

275.65

Red Kidney

K.G

263.42

48.03

215.39

Sweet Corn

K.G

289.39

143.69

145.71

Lima Beans

K.G

34.95

21.54

13.41

Carrots

K.G

13.96

13.65

0.31

Sum=

3101.73

After applying the EOQ model to the beans, the optimal quantity (Q*) for each was found to save a total of 3101.73 KD/year.

Page | 404

Economic Production Quantity (EPQ) for Production Planning The EPQ is a method used to determine the optimal procedure for producing multiple items in one system, to minimize the holding and the setup costs. This procedure helps to avoid stock outs in a production cycle.

Parameters: If (n) products are to be produced on a single machine: λi = Demand rate for product i. Pi = Production rate for product i. ht,i = Total holding cost per unit time of product i. Ki = Cost of setting up the production line to produce product i. K: Setup Cost = setup time *production rate*selling price The setup time for the 28 products is equal to 30 minutes each. Four workers conduct the setup but the cost of their labor was not considered because it is already considered in the selling price of each can. Assumptions required for satisfying the demand with current capacity: Feasibility: ∑λi/Pi ≤ 1. Utilization of the rotation cycle so that in each cycle, there is exactly one setup for each product. The products are produced in the same sequence in each production cycle. T = cycle time = √ ((2 ∑ Ki) / (hi * λi)) The setup time for each production type is not significant which will ensure that T ≥ (∑Si / 1- ∑ (λi/Pi)) = Tmin

Page | 405

The production rate 160 cans/min which is the maximum production rate The factory works 26 days per month and 12 hours per day to meet the customers demand which includes the overtime shifts. Table 5.34: Total holding cost.

Capital Cost (h0 = rv)

Storage (h1)

Insurance (h2)

Security (h3)

Total holding Cost (hT)

0.00219

0.005

0.003

0.004

0.01419

0.00208

0.005

0.003

0.004

0.01408

0.00169

0.005

0.003

0.004

0.01369

0.00141

0.005

0.003

0.004

0.01341

0.00186

0.005

0.003

0.004

0.01386

0.00242

0.005

0.003

0.004

0.01442

0.00146

0.005

0.003

0.004

0.01346

0.00169

0.005

0.003

0.004

0.01369

0.00276

0.005

0.003

0.004

0.01476

0.00219

0.005

0.003

0.004

0.01419

0.00264

0.005

0.003

0.004

0.01464

0.00129

0.005

0.003

0.004

0.01329

0.00242

0.005

0.003

0.004

0.01442

0.00283

0.005

0.003

0.004

0.01483

0.00416

0.005

0.003

0.004

0.01616

0.00630

0.005

0.003

0.004

0.01830

0.00450

0.005

0.003

0.004

0.01650

0.00495

0.005

0.003

0.004

0.01695

0.00276

0.005

0.003

0.004

0.01476

0.00585

0.005

0.003

0.004

0.01785 Page | 406

0.00585

0.005

0.003

0.004

0.01785

0.00321

0.005

0.003

0.004

0.01521

0.00203

0.005

0.003

0.004

0.01403

0.00225

0.005

0.003

0.004

0.01425

0.00180

0.005

0.003

0.004

0.01380

0.00327

0.005

0.003

0.004

0.01527

0.00420

0.005

0.003

0.004

0.01620

The set up cost (K) for all products is equal to 30 r is equal to 0.015 for all products Cycle time (T) is equal to 1.0524 for all products Tmin is equal to 0.8844 for all products

Page | 407

P/year

∆

h'=∆hT

λh'

T

Tmin

EPQ (Q*)

λ/P

Q

Tj

(Q*)

TVC(Q) TVC(Q*)

Q*-Q

Tj (Hrs)

Tj (Min)

TVC

Demand (λ)

1

13,154

166,400

0.9209

0.0131

171.95

0.91

0.3931

11,975

111

0.0791

3,500

136

0.072

24

8,475

29.94

1796

2

1,866

166,400

0.9888

0.0139

25.98

0.91

0.3931

1,698

45

0.0112

1,000

63

0.0102

18

698

4.25

255

3

18,660

166,400

0.8879

0.0122

226.77

0.91

0.3931

16,987

136

0.1121

3,500

181

0.1021

45

13,487

42.47

2548

4

24,809

166,400

0.8509

0.0114

283.01

0.91

0.3931

22,584

162

0.1491

3,500

233

0.1357

71

19,084

56.46

3388

5

24,809

166,400

0.8509

0.0118

292.51

0.91

0.3931

22,584

166

0.1491

3,500

233

0.1357

67

19,084

56.46

3388

6

174

166,400

0.999

0.0144

2.5

0.91

0.3931

158

34

0.001

500

14

0.001

20-

342-

0.4

24

7

19,922

166,400

0.8803

0.0119

236.09

0.91

0.3931

18,135

140

0.1197

3,500

191

0.109

51

14,635

45.34

2720

8

252

166,400

0.9985

0.0137

3.45

0.91

0.3931

230

35

0.0015

500

19

0.0014

16-

270-

0.57

34

9

2,377

166,400

0.9857

0.0145

34.58

0.91

0.3931

2,164

49

0.0143

1,000

79

0.013

30

1,164

5.41

325

10

142

166,400

0.9991

0.0142

2.01

0.91

0.3931

129

34

0.0009

100

43

0.0008

9

29

0.32

19

11

761

166,400

0.9954

0.0146

11.1

0.91

0.3931

693

38

0.0046

500

49

0.0042

11

193

1.73

104

12

27,416

166,400

0.8352

0.0111

304.42

0.91

0.3931

24,958

172

0.1648

3,500

254

0.15

83

21,458

62.39

3744

13

14,796

166,400

0.9111

0.0131

194.37

0.91

0.3931

13,469

121

0.0889

3,500

150

0.0809

28

9,969

33.67

2020

14

102

166,400

0.9994

0.0148

1.51

0.91

0.3931

93

34

0.0006

100

31

0.0006

2-

7-

0.23

14

15

88

52,000

0.9983

0.0161

1.42

0.91

0.3931

80

34

0.0017

100

27

0.0015

6-

20-

0.64

39

16

145

52,000

0.9972

0.0182

2.65

0.91

0.3931

132

34

0.0028

100

44

0.0025

10

32

1.06

63

(Q)

Product

TVC

Table 5.35: shows the EPQ Model for the current demand in CTN.

Page | 408

17

399

52,000

0.9923

0.0164

6.53

0.91

0.3931

363

36

0.0077

500

28

0.007

8-

137-

2.91

174

18

356

166,400

0.9979

0.0169

6.02

0.91

0.3931

324

36

0.0021

500

26

0.0019

10-

176-

0.81

49

19

1,327

166,400

0.992

0.0146

19.43

0.91

0.3931

1,208

42

0.008

1,000

47

0.0073

5

208

3.02

181

20

688

52,000

0.9868

0.0176

12.12

0.91

0.3931

626

38

0.0132

500

46

0.012

7

126

5.01

301

21

885

166,400

0.9947

0.0178

15.72

0.91

0.3931

806

40

0.0053

1,000

35

0.0048

5-

194-

2.01

121

22

193

166,400

0.9988

0.0152

2.94

0.91

0.3931

176

34

0.0012

500

15

0.0011

19-

324-

0.44

26

23

871

166,400

0.9948

0.014

12.14

0.91

0.3931

792

38

0.0052

1,000

33

0.0048

5-

208-

1.98

119

24

2,914

166,400

0.9825

0.014

40.79

0.91

0.3931

2,652

52

0.0175

2,000

58

0.0159

6

652

6.63

398

25

81

166,400

0.9995

0.0138

1.12

0.91

0.3931

74

33

0.0005

100

25

0.0004

8-

26-

0.19

11

26

2,380

166,400

0.9857

0.0151

35.82

0.91

0.3931

2,166

49

0.0143

1,000

79

0.013

30

1,166

5.42

325

27

493

166,400

0.997

0.0162

7.97

0.91

0.3931

449

37

0.003

500

34

0.0027

3-

51-

1.12

67

1,780

0.9793 Since <1

Sum

160,061

4,035,200

Since T>Tmin we will choose T*=T

2,174

394

Note: The product numbers are in the same order as they appear in the previous sections. h’ represents the modified holding cost

Page | 409

P/year

∆

h'

λh'

T

Tmin

EPQ (Q*)

λ/P

Q

(Q*)

Tj

TVC(Q) TVC(Q*)

Q*-Q

Tj (Hrs)

Tj (Min)

TVC

Demand (λ)

1

13,154

166,400

0.92

0.013

171.95

0.9103

0.393

11,975

111

0.0791

3,500

136

0.07

24

8,475

29.94

1796

2

1,866

166,400

0.99

0.014

25.98

0.9103

0.393

1,698

45

0.0112

1,000

63

0.01

18

698

4.25

255

3

18,660

166,400

0.89

0.012

226.77

0.9103

0.393

16,987

136

0.1121

3,500

181

0.1

45

13,487

42.47

2548

4

24,809

166,400

0.85

0.011

283.01

0.9103

0.393

22,584

162

0.1491

3,500

233

0.14

71

19,084

56.46

3388

5

24,809

166,400

0.85

0.012

292.51

0.9103

0.393

22,584

166

0.1491

3,500

233

0.14

67

19,084

56.46

3388

6

174

166,400

1

0.014

2.5

0.9103

0.393

158

34

0.001

500

14

0

20-

342-

0.4

24

7

19,922

166,400

0.88

0.012

236.09

0.9103

0.393

18,135

140

0.1197

3,500

191

0.11

51

14,635

45.34

2720

8

252

166,400

1

0.014

3.45

0.9103

0.393

230

35

0.0015

500

19

0

16-

270-

0.57

34

9

2,377

166,400

0.99

0.015

34.58

0.9103

0.393

2,164

49

0.0143

1,000

79

0.01

30

1,164

5.41

325

10

142

166,400

1

0.014

2.01

0.9103

0.393

129

34

0.0009

100

43

0

9

29

0.32

19

11

761

166,400

1

0.015

11.1

0.9103

0.393

693

38

0.0046

500

49

0

11

193

1.73

104

12

27,416

166,400

0.84

0.011

304.42

0.9103

0.393

24,958

172

0.1648

3,500

254

0.15

83

21,458

62.39

3744

13

14,796

166,400

0.91

0.013

194.37

0.9103

0.393

13,469

121

0.0889

3,500

150

0.08

28

9,969

33.67

2020

14

102

166,400

1

0.015

1.51

0.9103

0.393

93

34

0.0006

100

31

0

2-

7-

0.23

14

15

88

52,000

1

0.016

1.42

0.9103

0.393

80

34

0.0017

100

27

0

6-

20-

0.64

39

16

145

52,000

1

0.018

2.65

0.9103

0.393

132

34

0.0028

100

44

0

10

32

1.06

63

17

399

52,000

0.99

0.016

6.53

0.9103

0.393

363

36

0.0077

500

28

0.01

8-

137-

2.91

174

18

356

166,400

1

0.017

6.02

0.9103

0.393

324

36

0.0021

500

26

0

10-

176-

0.81

49

19

1,327

166,400

0.99

0.015

19.43

0.9103

0.393

1,208

42

0.008

1,000

47

0.01

5

208

3.02

181

(Q)

Description

TVC

Table 5.36: shows the EPQ Model for the forecasted demand of year 2009.

Page | 410

20

688

52,000

0.99

0.018

12.12

0.9103

0.393

626

38

0.0132

500

46

0.01

7

126

5.01

301

21

885

166,400

0.99

0.018

15.72

0.9103

0.393

806

40

0.0053

1,000

35

0

5-

194-

2.01

121

22

193

166,400

1

0.015

2.94

0.9103

0.393

176

34

0.0012

500

15

0

19-

324-

0.44

26

23

871

166,400

0.99

0.014

12.14

0.9103

0.393

792

38

0.0052

1,000

33

0

5-

208-

1.98

119

24

2,914

166,400

0.98

0.014

40.79

0.9103

0.393

2,652

52

0.0175

2,000

58

0.02

6

652

6.63

398

25

81

166,400

1

0.014

1.12

0.9103

0.393

74

33

0.0005

100

25

0

8-

26-

0.19

11

26

2,380

166,400

0.99

0.015

35.82

0.9103

0.393

2,166

49

0.0143

1,000

79

0.01

30

1,166

5.42

325

27

493

166,400

1

0.016

7.97

0.9103

0.393

449

37

0.003

500

34

0

3-

51-

1.12

67

1,780

0.9793 Since <1

Sum

160061

4035200

Since T>Tmin we will choose T*=T

2,174

394

Page | 411

Service Level The service level expresses the probability that a certain level of safety stock will not lead to a stock-out. Naturally, when safety stocks are increased, the service level increases as well. Three scenarios of service level percentages were applied to the average demand of the raw materials in order to evaluate the safety stock for each item. If the company applies one of the scenarios, it will consider the safety stock and the total cost for it. Assumptions: The labels, cartons and the spices are locally provided, but the other raw materials are provided from different countries. The local raw materials have an average lead time of one week, while the other materials have an average lead time of three months. The three different service levels tested were 90%, 95%, and 99%. All raw materials follow a normal distribution. Parameters: D: Average demand. Q: Order quantity. L: Lead time. DL: Demand during lead time. µ: Mean. σ: Standard deviation.

Page | 412

Equations: TC(SS) = TC(Q) + h (SS) 𝑧=

𝑥−𝜇 𝜎

The mean and the standard deviation are obtained from the Arena input analyzer.

Page | 413

Table 5.37: Service levels of can plant.

Description

Average Demand

SS Unit

mean

stand.dev

Q

TVC(Q)

h

For 90%

TC (SS) 90%

SS For 95%

TC (SS) 95%

SS For 99%

TC (SS) 99%

Black Eye Beans

88936

K.G

1853

794

10553

75.44

0.02

2869

132.82

3154

138.53

3694

149.32

Broad Beans

768446

K.G

15328

7133

132234

813.74

0.018

24458

1253.99

27026

1300.20

31876

1387.51

Chick Peas 8mm

1168924

K.G

17262

16829

101930

643.12

0.018

38802

1341.56

44860

1450.60

56304

1656.58

Chick Peas 7mm

608219

K.G

12671

5429

27500

243.70

0.026

19620

753.82

21574

804.63

25265

900.60

Chick Peas 10mm

161314

K.G

3361

1440

46309

322.16

0.02

5204

426.24

5722

436.61

6702

456.19

Whole Mushrooms

132455

K.G

3356

7122

18750

291.85

0.045

12471

853.04

15035

968.41

19877

1186.33

109071

K.G

2272

974

18750

288.23

0.045

3518

446.56

3869

462.33

4531

492.12

Green Peas

24859

K.G

7392

5614

61291

261.10

0.013

14577

450.60

16598

476.87

20415

526.49

Mixed Vegetables

37465

K.G

1873

1824

25811

241

0.028

4208

358.75

4865

377.14

6105

411.87

Navy Beans

24859

K.G

3760

9203

53905

116

0.006

15540

209.26

18853

229.14

25111

266.69

White Beans

37465

K.G

518

222

18766

270

0.042

802

303.41

882

306.76

1033

313.10

Peeled Foul

820995

K.G

780.5

334.5

65000

594

0.026

1209

625.44

1329

628.57

1557

634.48

Mushroom Stems and Pieces

Page | 414

Fava Beans

128946

K.G

15709

8098

71153

398

0.017

26075

840.95

28990

890.51

34497

984.13

Red Kidney

24859

K.G

3096

7061

33869

263

0.023

12134

542.51

14676

600.97

19478

711.40

Sweet Corn

208549

K.G

4345

1861.5

33572

289

0.025

6727

457.58

7398

474.33

8663

505.98

Lima Beans

26026

K.G

542.3

232.5

19184

35

0.005

840

39.15

924

39.57

1082

40.36

Carrots

16664

K.G

347.3

149

12000

14

0.003

538

15.57

592

15.73

693

16.04

Sum =

5159.43

9051.23

9600.91

10639.20

After applying the three scenarios for the can plant, it was found that the 90% service level gives the least total cost, which is equal to 728.72 KD/year, according to the safety stock. And the total cost of the current order quantity is equal to 416 KD/year.

Page | 415

Table 5.38: Service levels of beans.

SS Average Description

Demand

Unit

mean

stand.dev

Q

TVC(Q)

h

For

TC (SS)

SS For

TC (SS)

SS For

TC (SS)

90%

90%

95%

95%

99%

99%

Black Eye Beans

88936

K.G

1853

794

10553

75.44

0.02

2869

132.82

3154

138.53

3694

149.32

Broad Beans

768446

K.G

15328

7133

132234

813.74

0.018

24458

1253.99

27026

1300.20

31876

1387.51

Chick Peas 8mm

1168924

K.G

17262

16829

101930

643.12

0.018

38802

1341.56

44860

1450.60

56304

1656.58

Chick Peas 7mm

608219

K.G

12671

5429

27500

243.70

0.026

19620

753.82

21574

804.63

25265

900.60

Chick Peas 10mm

161314

K.G

3361

1440

46309

322.16

0.02

5204

426.24

5722

436.61

6702

456.19

Whole Mushrooms

132455

K.G

3356

7122

18750

291.85

0.045

12471

853.04

15035

968.41

19877

1186.33

Pieces

109071

K.G

2272

974

18750

288.23

0.045

3518

446.56

3869

462.33

4531

492.12

Green Peas

24859

K.G

7392

5614

61291

261.10

0.013

14577

450.60

16598

476.87

20415

526.49

Mixed Vegetables

37465

K.G

1873

1824

25811

241

0.028

4208

358.75

4865

377.14

6105

411.87

Navy Beans

24859

K.G

3760

9203

53905

116

0.006

15540

209.26

18853

229.14

25111

266.69

White Beans

37465

K.G

518

222

18766

270

0.042

802

303.41

882

306.76

1033

313.10

Peeled Foul

820995

K.G

780.5

334.5

65000

594

0.026

1209

625.44

1329

628.57

1557

634.48

Mushroom Stems and

Page | 416

Fava Beans

128946

K.G

15709

8098

71153

398

0.017

26075

840.95

28990

890.51

34497

984.13

Red Kidney

24859

K.G

3096

7061

33869

263

0.023

12134

542.51

14676

600.97

19478

711.40

Sweet Corn

208549

K.G

4345

1861.5

33572

289

0.025

6727

457.58

7398

474.33

8663

505.98

Lima Beans

26026

K.G

542.3

232.5

19184

35

0.005

840

39.15

924

39.57

1082

40.36

Carrots

16664

K.G

347.3

149

12000

14

0.003

538

15.57

592

15.73

693

16.04

Sum =

5159.43

9051.23

9600.91

10639.20

After applying the three scenarios of the service levels for the beans, it was found that the 90% service level once again gives the least total cost, which is equal to 9051.23 KD/year, according to the safety stock. And the total cost of the current order quantity is equal to 5159.43 KD/year.

Page | 417

5.3 Conclusion

Were the EOQ model applied for the last three years, it would have reduced the cost of the company’s total inventory by 3,291 KD/year. Were the EPQ model applied for the last three years, it would have reduced the cost of the company’s total inventory by 12,744 KD/year If the EPQ Model is applied for the year of 2009, the total inventory cost will be reduced by 4,728 KD/year From the three different scenarios, the 90% service level minimized the company’s total inventory costs.

Table 5.38: Total costs for the different service levels.

Service Level

TC(KD/yr)

Scenario 1: 90% 10,079 Scenario 2: 95% 10,664 Scenario 3: 99% 11,768

Page | 418

6. Supply Chain Management

Page | 419

Page | 420

6.1 Introduction

A supply chain consists of all parties involved directly or indirectly in fulfilling a customer request. It is dynamic and involves the constant flow of information, product and funds between different stages. The value a supply chain generates is the difference between what the final product is worth to the customer and the effort the supply chain expends in filling the customer request.

Figure 6.16: Supply chain stages.

The National Canned Food Production and Trading Co.'s supply chain can be classified as a pull system when it comes to meeting demand from its overseas and gulf region customers; it orders its raw materials from its suppliers and manufactures to meet the required demand. For its local customers, based on historical demand from co-ops, wholesalers and small stores, the company keeps an inventory to satisfy it. The company uses two modes of transportation to fulfill its customer's orders; truck loads for transportation by land and ship containers by sea with a capacity of 2100 and 1650 cartons, respectively.

Page | 421

Typical Supply Chain and its Cycles

Manufacturer markets product

Customer places orders

Manufacturer receives orders

Manufacturer order supplies

Supplier fulfill the order

Manufacturer fulfill customer’s order

Manufacturer sends final products to the customer

Figure 6.17: A typical supply chain.

Customer Order Cycle Occurs at the customer/distributor interface and includes all processes directly involved in receiving and filling customer's order. 

Customer arrives.



Customer Places Order.



Order is fulfilled.



Order is received.

Page | 422

Manufacturing Cycle Occurs at the distributor/manufacturer interface; related to production scheduling and includes all processes involved in replenishing inventory triggered by: 

Customer order.



Replenishment orders.



Forecast of customer demand.

Procurement Cycle Occurs at the manufacturer/supplier interface and includes all processes necessary to ensure that materials are available for all manufacturing to occur according to schedule.

Figure 6.18 - Supply chain cycles.

Cycles are very useful when considering operational decisions because it specifies the roles and responsibilities of each member of the chain. Push/Pull view is very useful when considering the strategic decisions relating to supply chain design.

Page | 423

Warehouses' Locations

There are two warehouses which belong to the National Canned Food Production and Trading Co. One is located at Sabhan and is used for storing final products and only the material/equipment needed for near production. The other warehouse is located in Kabd and is used for storing the packing material until it is needed.

Figure 6.19: Warehouses' location on Kuwait map.

Page | 424

Distribution Network

The National Canned Food Production and Trading Co. distributes its final product, by land, to a local distributor who is then in charge of delivering to the coops, wholesalers and small stores, to six Gulf Countries and ships to two countries in Africa and to Houston, TX. The imported packing and raw materials arrive at Shuwaikh Port. The packing material is then transported to Kabd, and the raw materials to Sabhan. When the packing material is needed, it is then sent to Sabhan. The company manufactures for other gulf countries and the overseas customers based on customer request, but does keep inventory for its local customers.

Figure 6.20: Customers in the Gulf region.

Page | 425

Figure 6.21: Overseas customers.

Page | 426

Figure 6.22: Supply chain network.

Page | 427

Current Average Demand and Costs

Based on historical data, table 6.1 was derived. Note that every truck (sometimes called trailer) has a capacity of 2100 cartons (every carton holds 24 cans) and every container which is used for shipping modes has a capacity of 1650 cartons. Table 6.44: Average demand and transportations costs for all customers.

Avg. Demand

Capacity

Cost

(transporter/month)

of

(KD/transporter)

Total Cost (KD/month)

transporter (carton) Local

28

2100

0

0

KSA (Dammam)

6

2100

200

1200

UAE

5

2100

300

1500

Bahrain

4

2100

290

1160

Qatar

3

2100

300

900

Oman

3

2100

400

1200

Iraq

3

2100

150

450

Tunisia

2

1650

815

1630

USA

3

1650

980

2940

Kenya

3

1650

1300

3900

Totals

122400 cartons/month

14880

Page | 428

Problem Statement

The National Canned Food Production and Trading Co. have to keep the production line running overtime due to the large demand for their products. They are incapable of satisfying demand with their official scheduled working hours. The overtime includes working throughout nights, early mornings and during weekends. The company is at risk of being unable to satisfy the current demand even with overtime production. The company produces 4,000 cartons daily on average (without considering overtime hours), which is equal to 104,000 cartons per month. The total monthly demand on average is equal to 122,400 cartons. This means that the factory produces almost 15% of the demand during overtime. Overtime hours do not come free of charge, however. It costs the company, on average, 1,750 KD every month which is considered as an extra, unnecessary expense for the company and it is a work overload on the workers at the company! The system is thereby risky and expensive.

Page | 429

Solution Approach

After studying the current supply chain of the company, Linear Programming (was used to study the profitability of opening a new factory in 2 potential sites (KSA Dammam and Kuwait), the profitability of using a new mode of transportation, and the profitability of increasing the capacity of the existing factory by replacing the bottleneck machines.

The aim from this study is to raise the company's awareness of the necessity of increasing the company's production capacity and look further into it.

6.2 Analysis and Studies

Assumptions

1. The establishing and fixed costs for the two alternatives are the same. 2. Any regulations regarding establishing a new factory in KSA were overlooked. 3. Costs of transportation from KSA are estimated using the obtained data for transportation from/in Kuwait. 4. Sabhan (Kuwait) will remain to produce for the overseas markets and therefore will not be included in the modeling. 5. The average monthly capacity is 50 truckloads. Since the overseas markets will not be considered, their demand will be deducted from the total monthly capacity. Therefore, the monthly capacity will be 42 cartons.

Page | 430

Table 6.45: Input data.

Demand City (j) Transportation Cost (Cij) per 2100 cartons (KD) (i)

Kuwait - Existing

Monthly Capacity (x2100 cartons)

Kuwait

KSA

UAE

Bahrain

Qatar

Oman

Iraq

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0

200

300

290

300

400

150

42

0

200

300

290

300

400

150

90

200

0

100

90

100

200

350

90

28

6

5

4

3

3

3

Total Demand

(Ki)

(1) Kuwait - Potential (2) KSA - Potential (3) Monthly Demand (Dj) (x2100 cartons)

54

Page | 431

Study 1: Establishing a New Factory

The potential sites for establishing a new factory are Kuwait and KSA - Dammam. Dammam is considered one of the most industrial cities in KSA. It is an easily accessible city. Also, the distributor is located in Dammam, so the cost estimates are valid. The annual maintenance cost of the existing factory in Kuwait is 77,500 KD. The annual equivalent of preventive maintenance costs is 10,800 KD and the annual equivalent of the setup cost was estimated to be 53,070 KD: Setup cost = 300,000 KD A= P (A/P, i =12%, n=10) = 53,070 KD Therefore, the annual equivalent of setup and maintenance costs either in Kuwait or KSA is 63,870 KD. Model Input: Cij : Cost of transporting one truck from i to j. Dj : Demand of j. Ki : Capacity of i. Ai : Annual equivalent of running/establishing factory. Decision Variables: Yij : Whether j is covered by i or not. Si : Whether a factory exists or is established at i or not. Objective Function: Min

1≤ 𝑖 ≤ 3 CijDjYij 1<𝑗 <7

+

1≤ 𝑖 ≤ 3 AiSi 1<𝑗 <7

Page | 432

Constraints: 3 𝑖=1 Yij

= 1

j = 1, 2, … ,7

Ensures that the demand of every market is supplied by one factory. Yij ≤ Si

i = 1, 2, 3 and j = 1, 2, … ,7

Ensures that a factory can only cover a market’s demand if it exists or is established. 7 𝑗 =1 DjYij

≤ KiSi

i = 1, 2, 3

Ensures that the demand supplied by a factory does not exceed its capacity. 3 i=2 Si

= 1

Ensures that only one new factory is opened in either KSA or Kuwait. S1 = 1 Ensures that Kuwait Plant Exists. Yij = {0,1} Whether a market i is supplied by a factory j or not. Si = {0,1}

i = 2,3

Whether a factory is established at KSA or Kuwait min 0Y11 + 1200Y12 + 1500Y13 + 1160Y14 + 900Y15 + 1200Y16 + 450Y17 + 0Y21 + 1200Y22 + 1500Y23 + 1160Y24 + 900Y25 + 1200Y26 + 450Y27 + 5600Y31 + 0Y32 + 500Y33 + 360Y34 + 300Y35 + 600Y36 + 1050Y37 + 6458S1 + 5323S2 + 5323S3 st Y11 + Y21 + Y31 = 1 Y12 + Y22 + Y32 = 1 Y13 + Y23 + Y33 = 1 Y14 + Y24 + Y34 = 1 Y15 + Y25 + Y35 = 1

Page | 433

Y16 + Y26 + Y36 = 1 Y17 + Y27 + Y37 = 1 Y21 - S2 <= 0 Y22 - S2 <= 0 Y23 - S2 <= 0 Y24 - S2 <= 0 Y25 - S2 <= 0 Y26 - S2 <= 0 Y27 - S2 <= 0 Y31 - S3 <= 0 Y32 - S3 <= 0 Y33 - S3 <= 0 Y34 - S3 <= 0 Y35 - S3 <= 0 Y36 - S3 <= 0 Y37 - S3 <= 0 S1= 1 S2 + S3 = 1 28Y11 + 6Y12 + 5Y13 + 4Y14 + 3Y15 + 3Y16 + 3Y17 - 42S1<= 0 28Y21 + 6Y22 + 5Y23 + 4Y24 + 3Y25 + 3Y26 + 3Y27 - 90S2 <= 0 28Y31 + 6Y32 + 5Y33 + 4Y34 + 3Y35 + 3Y36 + 3Y37 - 90S3 <= 0 end int Y11 int Y12 int Y13

Page | 434

int Y14 int Y15 int Y16 int Y17 int Y21 int Y22 int Y23 int Y24 int Y25 int Y26 int Y27 int Y31 int Y32 int Y33 int Y34 int Y35 int Y36 int Y37 int S2 int S3

Page | 435

Output Results showed that a new factory should be established in KSA and the distribution plan is as follows. Table 6.46: Model 1 output.

DjYij

Kuwait

KSA

UAE

Bahrain

Qatar

Oman

Iraq

Total Truck loads

Kuwait

28

0

0

0

0

0

3

31

KSA

0

6

5

4

3

3

0

21

Total Cost = 13991 KD/month

S2 = 0 S3 = 1 *For more details refer to Appendix O for the Lindo output.

Page | 436

Study 2: Using New Trucks

KGL sends trucks with a capacity of 67.7 m3, to two of the existing customers. Thus, the capacity of the new truck is 4130 cartons. We will study if using these trucks as a mode of transportation from Kuwait to KSA - Dammam and UAE will help reduce transportation costs in comparison to establishing a new factory. Table 6.47: Price quotation from KGL.

KSA - Dammam

UAE

300

450

3

3

Cost from Kuwait (KD/truck) Average Demand (truck/month)

Model Input: Cij : Cost of transporting one truck from i to j. Dj : Demand of j. Ki : Capacity of i. Decision Variables: Yij : Whether j is covered by i or not. Si : Whether a factory exists or is established at i or not. Tij : Whether the new trucks are used to transport from i to j. Objective Function: Min

1≤ 𝑖 ≤ 3 CijDjYij 1<𝑗 <7

+

3 𝑖=1 AiSi

+

1≤ 𝑖 ≤ 3 CijDjTij 1<𝑗 <7

Page | 437

Constraints: 3 𝑖=1 Yij

+ Tij = 1

j = 2, 3

Ensures that the demand of every market is supplied by one factory using one mode of transportation. 3 𝑖=1 Yij

= 1

j = 1, 4, 5, 6, 7

Ensures that the demand of every market is supplied by one factory. Yij ≤ Si

i = 1, 2, 3 and j = 1, 2, … , 7

Ensures that a factory can only cover a market's demand if it exists or is established. 7 𝑗 =1 DjYij

+ DjTij ≤ KiSi

i = 1, 2, 3

Ensures that the demand supplied by a factory by one mode of transportation does not exceed its capacity. 3 i=2 Si

= 1

Ensures that only one new factory is opened at either KSA or Kuwait. S1 = 1 Ensures that Kuwait Plant Exists. Yij = {0,1} Whether a market i is supplied by a factory j or not. Si = {0,1}

i = 2,3

Whether a factory is established at KSA or Kuwait

Page | 438

min 0Y11 + 1200Y12 + 900T12 + 1500Y13 + 1125T13 + 1160Y14 + 900Y15 + 1200Y16 + 450Y17 + 0Y21 + 1200Y22 + 1500Y23 + 1160Y24 + 900Y25 + 1200Y26 + 450Y27 + 5600Y31 + 0Y32 + 500Y33 + 360Y34 + 300Y35 + 600Y36 + 1050Y37 + 6458S1 + 5323S2 + 5323S3 st Y11 + Y21 + Y31 = 1 Y12 + Y22 + Y32 + T12 = 1 Y13 + Y23 + Y33 + T13 = 1 Y14 + Y24 + Y34 = 1 Y15 + Y25 + Y35 = 1 Y16 + Y26 + Y36 = 1 Y17 + Y27 + Y37 = 1 Y21 - S2 <= 0 Y22 - S2 <= 0 Y23 - S2 <= 0 Y24 - S2 <= 0 Y25 - S2 <= 0 Y26 - S2 <= 0 Y27 - S2 <= 0 Y31 - S3 <= 0 Y32 - S3 <= 0 Y33 - S3 <= 0 Y34 - S3 <= 0 Y35 - S3 <= 0 Y36 - S3 <= 0 Y37 - S3 <= 0

Page | 439

S1= 1 S2 + S3 = 1 28Y11 + 6Y12 + 3t12+ 3t13 + 5Y13 + 4Y14 + 3Y15 + 3Y16 + 3Y17 - 42S1 <= 0 28Y21 + 6Y22 + 5Y23 + 4Y24 + 3Y25 + 3Y26 + 3Y27 - 90S2 <= 0 28Y31 + 6Y32 + 5Y33 + 4Y34 + 3Y35 + 3Y36 + 3Y37 - 90S3 <= 0 end int Y11 int Y12 int Y13 int Y14 int Y15 int Y16 int Y17 int Y21 int Y22 int Y23 int Y24 int Y25 int Y26 int Y27 int Y31 int Y32 int Y33 int Y34 int Y35

Page | 440

int Y36 int Y37 int S2 int S3 int T12 int T13

Output Results showed that the best option is establishing a new factory in KSA again. Table 6.48: Model 2 output.

DjYij

Kuwait

KSA

UAE

Bahrain

Qatar

Oman

Iraq

Total Truck loads

Kuwait

28

0

0

0

0

0

3

31

KSA

0

6

5

4

3

3

0

21

Total Cost = 13991 KD/month S2 = 0 S3 = 1 T12 = 0 T13 = 0 *For more details refer to Appendix O for the Lindo output.

Page | 441

Justifications for Study 1 and Study 2

Current Situation N.B. The following data was used to estimate the costs and was obtained from the Cost Analysis Group. Table 6.49: Annual costs.

Cost (KD/year) Overtime

21,000

Maintenance

77,500

Operation Costs

178,560

Transportation

145,812

These are the costs considered when opening the new factory. A cash flow diagram was developed to calculate the present worth of the current existing factory in Kuwait. The interest rate used was 12% and calculated over a period of 10 years.

PW = 2,389,322 KD

Page | 442

Current (Kuwait) Factory in New Situation Maintenance costs remain the same because the machines are untouched. The transportation costs include only the costs involved in the new distribution plan. The operation costs are equal to 65% of the current operation costs because the current factory in the new situation will be responsible for producing only 65% of its current production.

PW = 1,578,209 KD

Page | 443

New Factory Since it is a new factory, no corrective maintenance should be applied in normal conditions. However, the preventive maintenance will be carried on the same schedule as the current factory which will result in constant costs. The new factory will be shipping to KSA, Bahrain, UAE, Qatar and Oman. These locations demand 35% of the current production and operation costs are calculated based on that.

PW = 768,715 KD Therefore, the Total Present Worth was calculated for the company by summing the PW for the current factory in the new situation and that of the new factory. PW = 1,578,209 + 768,715 = 2,346,924 KD Total Cost Savings = ((2,389,322 - 2,346,924)/ 2,389,322) x 100 = 1.77 %

Page | 444

Study 3: Increasing Capacity of Existing Factory The capacity of the existing factory in Kuwait could be increased if the bottle neck machines were replaced. In the following model this option was included in addition to the previous two alternatives and also relaxing the constraint so that more than one alternative could be feasible. The new average production speed would equal about 290 - 300 cans/min after replacing the bottleneck machines. Therefore, the average monthly capacity is 90 truckloads. Using average cost values obtained from Elmar, an industry leader in the manufacturing and design of a wide variety of machines (http://www.nov.com/elmar/), the annual equivalent of expanding the capacity cost was estimated to be KD 11,522. Model Input: Cij : Cost of transporting one truck from i to j. Dj : Demand of j. Ki : Capacity of i. Ui : Increase in capacity of i. Decision Variables: Yij : Whether j is covered by i or not. Si : Whether a factory exists or is established at i or not. Tij : Whether the new trucks are used to transport from I to j. Qi : Whether the capacity of factory i is increased or not. Objective Function: Min

1≤ 𝑖 ≤ 3 CijDjYij 1<𝑗 <7

+

3 𝑖=1 AiSi

+

1≤ 𝑖 ≤ 3 CijDjTij 1<𝑗 <7

+

1 i=1 AiQi

Page | 445

Constraints: 3 𝑖=1 Yij

+ Tij = 1

j = 2, 3

Ensures that the demand of every market is supplied by one factory using one mode of transportation. 3 𝑖=1 Yij

= 1

j = 1, 4, 5, 6, 7

Ensures that the demand of every market is supplied by one factory. Yij ≤ Si

i = 1, 2, 3 and j = 1, 2, … , 7

Ensures that a factory can only cover a market's demand if it exists or is established. 7 𝑗 =1 DjYij

+ DjTij ≤ KiSi + QiUi

i = 1, 2, 3

Ensures that the demand supplied by a factory by one mode of transportation does not exceed its capacity. 3 i=2 Si

= 1

Ensures that only one new factory is opened at either KSA or Kuwait. S1 = 1 Ensures that Kuwait Plant Exists. Yij = {0,1} Whether a market i is supplied by a factory j or not. Si = {0,1}

i = 2,3

Whether a factory is established at KSA or Kuwait

Page | 446

min 0Y11 + 1200Y12 + 900T12 + 1500Y13 + 1125T13 + 1160Y14 + 900Y15 + 1200Y16 + 450y17 + 0Y21 + 1200Y22 + 1500Y23 + 1160Y24 + 900Y25 + 1200Y26 + 450Y27 + 5600Y31 + 0Y32 + 500Y33 + 360Y34 + 300Y35 + 600Y36 + 1050Y37 + 6458S1 + 5323S2 + 5323S3 + 960Q1 st Y11 + Y21 + Y31 = 1 Y12 + Y22 + Y32 + T12 = 1 Y13 + Y23 + Y33 + T13 = 1 Y14 + Y24 + Y34 = 1 Y15 + Y25 + Y35 = 1 Y16 + Y26 + Y36 = 1 Y17 + Y27 + Y37 = 1 Y21 - S2 <= 0 Y22 - S2 <= 0 Y23 - S2 <= 0 Y24 - S2 <= 0 Y25 - S2 <= 0 Y26 - S2 <= 0 Y27 - S2 <= 0 Y31 - S3 <= 0 Y32 - S3 <= 0 Y33 - S3 <= 0 Y34 - S3 <= 0 Y35 - S3 <= 0 Y36 - S3 <= 0 Y37 - S3 <= 0

Page | 447

S1= 1 28Y11 + 6Y12 + 3T12+ 3T13 + 5Y13 + 4Y14 + 3Y15 + 3Y16 + 3Y17 – 42S1 – 48Q1 <= 0 28Y21 + 6Y22 + 5Y23 + 4Y24 + 3Y25 + 3Y26 + 3Y27 – 90S2 <= 0 28Y31 + 6Y32 + 5Y33 + 4Y34 + 3Y35 + 3Y36 + 3Y37 – 90S3 <= 0 end int Y11 int Y12 int Y13 int Y14 int Y15 int Y16 int Y17 int Y21 int Y22 int Y23 int Y24 int Y25 int Y26 int Y27 int Y31 int Y32 int Y33 int Y34 int Y35 int Y36

Page | 448

int Y37 int S2 int S3 int T12 int T13 int Q1

Output Results showed that increasing the capacity of the existing plant in Kuwait is the best option alongside using the new modes of transport. Table 6.50: Model 3 output.

DjYij

Kuwait

KSA

UAE

Bahrain

Qatar

Oman

Iraq

Total Truck loads

Kuwait (old truck)

28

0

0

4

3

3

3

41

Kuwait

0

3

3

0

0

0

0

6

(new truck) Total Cost = 13153 KD/month S2 = 0 S3 = 0 Q1 = 1 T12 = 1 T13 = 1 *For more details refer to Appendix O for the Lindo output. Page | 449

Study 4: Demand Increase

In the likely case of an increase in demand, decisions may change. Using the demand forecasted for the next 5 years by the inventory control group, an average monthly demand was calculated and the following results were obtained. Using the same model as study 3, results were obtained in order to develop a distribution plan in order to meet the forecasted demand.

Page | 450

Table 6.51: Forecasted average demand.

Demand City (j) Transportation Cost (Cij) per 2100 cartons (KD) (i)

Kuwait - Existing

Monthly Capacity (x2100 cartons)

Kuwait

KSA

UAE

Bahrain

Qatar

Oman

Iraq

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0

200

300

290

300

400

150

42

0

200

300

290

300

400

150

90

200

0

100

90

100

200

350

90

33

8

7

5

4

4

7

Total Demand

(Ki)

(1) Kuwait - Potential (2) KSA - Potential (3) FORECASTEDMonthly Demand (Di) (x2100 cartons)

68

Page | 451

min 0Y11 + 1600Y12 + 1500T12 + 2100Y13 + 2250T13 + 1450Y14 + 1200Y15 + 1600Y16 + 1050Y17 + 0Y21 + 1600Y22 + 2100Y23 + 1450Y24 + 1200Y25 + 1600Y26 + 1050Y27 + 6600Y31 + 0Y32 + 700Y33 + 450Y34 + 400Y35 + 800Y36 + 2450Y37 + 6458S1 +

5323S2

+ 5323S3 + 960Q1 st Y11 + Y21 + Y31 = 1 Y12 + Y22 + Y32 + T12 = 1 Y13 + Y23 + Y33 + T13 = 1 Y14 + Y24 + Y34 = 1 Y15 + Y25 + Y35 = 1 Y16 + Y26 + Y36 = 1 Y17 + Y27 + Y37 = 1 Y21 - S2 <= 0 Y22 - S2 <= 0 Y23 - S2 <= 0 Y24 - S2 <= 0 Y25 - S2 <= 0 Y26 - S2 <= 0 Y27 - S2 <= 0 Y31 - S3 <= 0 Y32 - S3 <= 0 Y33 - S3 <= 0 Y34 - S3 <= 0 Y35 - S3 <= 0 Y36 - S3 <= 0

Page | 452

Y37 - S3 <= 0 S1= 1 33Y11 + 8Y12 + 5T12 + 5T13 + 7Y13 + 5Y14 + 4Y15 + 4Y16 + 7Y17 – 42S1 – 48Q1 <= 0 33Y21 + 8Y22 + 7Y23 + 5Y24 + 4Y25 + 4Y26 + 7Y27 – 90S2 <= 0 33Y31 + 8Y32 + 7Y33 + 5Y34 + 4Y35 + 4Y36 + 7Y37 – 90S3 <= 0 end int Y11 int Y12 int Y13 int Y14 int Y15 int Y16 int Y17 int Y21 int Y22 int Y23 int Y24 int Y25 int Y26 int Y27 int Y31 int Y32 int Y33 int Y34

Page | 453

int Y35 int Y36 int Y37 int S2 int S3 int T12 int T13 int Q1

Output Results showed that establishing a factory in KSA would be the most feasible solution in the case of an increase in demand. Table 6.52: Model 4 output.

DjYij

Kuwait

KSA

UAE

Bahrain

Qatar

Oman

Iraq

Total Truck loads

Kuwait

33

0

0

0

0

0

7

60

0

8

7

5

4

4

0

5

Existing KSA Potential Total Cost = 15181.00 KD/month S2 = 0 S3 = 1 Q1 = 0

Page | 454

T12 = 0 T13 = 0 *For more details refer to Appendix O for the Lindo output.

6.3 Conclusion

Throughout this analysis, alternatives were studied in order to overcome the problem regarding the production capacity of the factory. The alternatives studied were whether to increase the capacity of the current factory, establish a new factory, and also, to reduce shipping costs, new modes of transportation were introduced where the unit shipping cost is less than for the existing modes. With the current average demand, it is suggested to increase the capacity of the existing Kuwait factory and use the new modes of transportation introduced. The initial associated transportation costs were 14880 KD/month; the cost resulting from the suggested distribution plan is 13153 KD/month, resulting in savings of 11.6%. Since the National Canned Food Production and Trading CO. is becoming more and more known throughout the region and internationally, there is an expected increase in demand, which the company may not be able to satisfy with their current production capacity. It is safe to assume so because of the fact that they are already working overtime to satisfy the current demand. Therefore, it would seem necessary for the company to increase their production capacity in order to be able to satisfy the future forecasted demand.

Page | 455

Page | 456

7. Safety & Human Factors

Page | 457

Page | 458

7.1 Introduction

The working conditions inside the factory were examined and it was determined whether they are safe. It was attempted to remove all hazards from the workplace and to try to minimize the chances of workers sustaining significant injuries. By applying multiple human factors tools as RULA and the NIOSH lifting equation, the aim was to eradicate any unhealthy postures during work or activities that cause too much fatigue to the workers. Also, the company was educated on the important role that safety and human factors engineers can play in ensuring the safety of their workers and avoiding any expensive accidents from occurring.

Problem Description By observing the factory, it was noticed that there is no significant attention paid to the safety and human factors aspects of the work being done. There were wet floors, crammed machines, and no signs instructing workers to wear protective equipment. Furthermore, many of the work activities were not ergonomically sound.

Page | 459

Objectives It was immediately noticed that there are major opportunities for improvement in the environment of the factory. The workers’ body positions as well as other areas that safety and human factors can cover were studied with the aim to: 

Improve operational performance.



Enhance effectiveness and efficiency.



Ensure the work environment can be used conveniently.



Make workers comfortable in their surrounding environment.



Reduce human errors.



Increase productivity.



Improve safety.



Reduce fatigue and stress.



Get workers’ acceptance.



Increase job satisfaction.



Improve the quality of life.

Note that, achieving the objectives above leads to a reduction in the number of accidents which will go towards eliminating the direct and indirect costs of an accident.

Solution Approach Safety and human factors tools such as RULA and NIOSH were used to evaluate

all

work

activities.

When

activities

were

found

to

be

unsafe,

recommendations to modify them were suggested.

Page | 460

7.2 Safety and Human Factors

Even though technology is advancing at an exponential rate, there are still work activities with manual handling of material, supplies, and tools often requiring workers to expend moderate to high level of physical energy to perform them. Engineers must make sure that products, workplaces, environments, buildings, vehicles and systems are safe since they affect the way a worker may act, and may eventually cause an accident. The Domino Theory states that an accident sequence is like a series of five dominos standing on end, one can knock the others over. The five dominos in reverse sequence are injuries caused by an action which, in turn, is caused by an unsafe act or condition, caused by undesirable traits (nervousness, violent temper, lack of knowledge,…etc.), that are developed because of unsafe environment.

Undesirable Traits

INJURY

Unsafe Environment

Accident

Unsafe Act

Figure 7.1: Dominos theory.

At the same time, engineers work in an economic system that requires businesses and enterprises to be competitive. Safety and human factors make ergonomic sense as well as moral and legal sense.

Page | 461

So to achieve safety through engineering, engineers need to understand: 

The duties and responsibilities for which they are accountable.



The hazards and engineering controls for them.



Human behavior, capabilities and limitations.



How to identify hazards and present the need for controls to the managers.

Engineers work mainly on the preventive side of safety, where they must identify the hazards during design and eliminate or reduce them. They also prevent unsafe behavior by designing the product, workplace and environment in a way that unsafe behaviors are not likely to occur. Industrial engineers work mainly on fitting the job to people and designing work methods to improve the fit between people and their equipment, environment, system, workplace or information, to improve workers performance and safety. Safety engineering is the application of scientific and engineering principals and methods to the elimination and control of hazards. Also it is the state of being free from harm, danger, injury or damage. Human factors is a term that covers: 

The science of understanding the properties of human capability (Human Factors Science).



The application of this understanding to the design and development of systems and services (Human Factors Engineering).



The art of ensuring successful application of Human Factors Engineering to a program.

Page | 462

7.3 Hazard Categories

A hazard is a situation which poses a level of threat to life, health, property or environment. Most hazards are dormant or potential, with only a theoretical risk of harm. However, once a hazard becomes 'active', it can create an emergency situation.

1. Biological Hazards include bacteria, viruses, insects, plants, birds, animals, and humans. These sources can cause a variety of health effects ranging from skin irritation and allergies to infections (e.g., tuberculosis, AIDS), cancer and so on.

2. Chemical hazards are present when a worker is exposed to any chemical preparation in the workplace in any form (solid, liquid or gas). Some are safer than others, but to some workers who are more sensitive to chemicals, even common solutions can cause illness, skin irritation or breathing problems. Beware of: 

Liquids, such as cleaning products, paints, acids, solvents especially chemicals in an unlabelled container.



Vapors and fumes, for instance those that come from welding or exposure to solvents.



Gases like acetylene, propane, carbon monoxide and helium.



Flammable materials like gasoline, solvents and explosive chemicals.

Page | 463

3. Ergonomic Hazards occur when the type of work, body position and working conditions put strain on your body. They are the hardest to spot since the strain on the body and the harm they pose are immediately noticeable. Shortterm exposure may result in "sore muscles" the next day or in the days following exposure, but long term exposure can result in serious long-term injuries. Ergonomic hazards include: 

Poor lighting.



Improperly adjusted workstations and chairs.



Frequent lifting.



Poor posture.



Awkward movements, especially if they are repetitive.



Repeating the same movements over and over.



Having to use too much force, especially if repeated frequently.

4. Physical Hazards are the most common and will be present in most workplaces at one time or another. They include unsafe conditions that can cause injury, illness and death. They are typically easiest to spot but often overlooked because of familiarity, lack of knowledge, resistance to spending time or money to make necessary improvements or simply delays in making changes to remove the hazards. None of these are acceptable reasons for workers to be exposed to physical hazards. Examples of physical hazards include: 

Electrical hazards such as frayed cords, missing ground pins, improper wiring.



Unguarded machinery and moving machinery parts, guards removed or moving parts that a worker can accidentally touch.



Constant loud noise.



High exposure to sunlight/ultraviolet rays, heat or cold.



Working from heights, including ladders, scaffolds, roofs, or any raised work area.



Working with mobile equipment such as forklifts since they require significant additional training and experience. Page | 464

7.4 Worker interaction with machine and material

The areas where the workers interact with the machine, raw materials, or final product through the production process are discussed below. Can Production Line: 1. Slitting: In the slitting process, a worker standing that feeds the tin sheets into the slitting machine. 2. Blanks are manually fed by the same worker to the welder. 3. Welding: In this step there is a welding test applied by a single worker. 4. Seaming: A worker manually feeds the seaming machine with the lids. Filling Line: 1. Soaking: Tanks are manually filled by a worker. 2. Inspection belt: The solid material is sorted manually by 4-6 workers to remove any dark or broken pieces. 3. Crate loading: 700 cans are put on a crate manually and are taken to the sterilizing stage by a trolley. 4. Sterilizing: The crates are pushed into the sterilizing machine manually. 5. Crate unloading: The cans are unloaded from the crate to the labeler manually. 6. Label inspection: Checking the quality of the labels is done manually by a specialized worker. 7. Every 20 cartons are put in a pallet by two workers and one fork lift.

Page | 465

7.5 Data Collection and Findings

To collect information accurately and easily identify the hazards around the factory, steps were taken to summarize the findings to make it easier to improve the system and reduce the hazards.  Safety Checklists: A checklist is used as an aid to memory. It helps to ensure consistency and completeness in carrying out a task. A more advanced checklist would be a schedule, which lays out tasks to be done according to time of day or other factors.  Safety and Human factors Survey Table: A survey table is a technique used to gather the findings and summarize them into categories.

Safety and Human Factors Checklists1: Applying a number of safety and human factors checklists covered a large part of the workplace which led to general conclusions regarding to safety hazards: a. Work Environment:  The factory has a ventilation system but does not have an air conditioning system which causes

an

increase

in

temperature

and

humidity in summer, adversely affecting worker performance.  The noise level in the factory was very high. Figure 7.2: Ventilation system.

1

For more details, Check Appendix (P)

Page | 466

 The lighting of the factory was deemed acceptable is the roof of the factory allows the sun light through (which provides natural lighting in addition to the electrical lighting system in the factory). However, some areas need some enhancement in the lightning system because the illumination is not enough

Figure 7.3: Lighting system.

or there are glare issues.  The poor machine layout and the unorganized raw material and final products storage area cause some workers to face some difficulties in moving from one machine to another.  Since the factory deals with the production of caned food which involves the use of a massive amount of fluids in the process line, the ground is always wet, causing slipping accidents. b. Fire Protection: Figure 7.4: Wet ground.

The factory has an automatic fire fighting and detection system that is sensitive to smoke and fire. There are 4 fire hose reels distributed around the factory plant and 8 fire extinguishers. c. Emergency Exits:

Figure 7.5: Fire extinguishers.

The factory has 7 emergency exits distributed in several places around the factory plant. Some emergency exits are difficult to reach or access because of the presence of obstacles in the way. Stockpiles of raw material also hinder the passage of workers.

Figure 7.6: Blocked emergency exit.

Page | 467

d. Safety Signs: There are no information and warning signs that remind the workers of the importance of wearing protective gear (for example boots, gloves, eye protectors, coats, helmets and earmuffs).

Uncomfortable Body Postures1:

Figure 7.7: Instruction boards.

The design of the machines and the working tasks forced the workers to adopt uncomfortable postures that require further study by applying Human Factors methods.

Safety and Human Factors Survey Forming a safety and human factors survey table that contains all the findings that were recognized when studying the factory made it easier to identify the type of hazard and the way to remove or reduce it. The survey table contains the number of findings, type, date, location (Fig.#), description, and data available. The information gathered will be then used in:  Quick-Win Improvement Table contains the findings that can be easily solved and the number of findings. The findings that can be solved by the same recommendation are grouped together to faciliate their solution.  Long Term Improvement Table contains findings that need further studying by applying human factors and safety tools where the findings can not be solved easily and need further investigation.

1

For pictures, check Appendix (Q)

Page | 468

Location Layout

Figure 7.8: Location of hazard layout.

Page | 469

Safety and Human Factors Survey Table 1

Table 7.53: Safety and human factors survey .

Finding

Hazard Type

Date

Location

Description

Data

1

Chemical

ET

Out Doors

H2S Gas.

Video

2

Physical

ET

EW

Wet floor everywhere, except storage areas.

V+P

3

Physical

ET

EW

Very high noise level.

Video

4

Safety

ET

EW

No safety signs.

Picture

5

Ergonomic

ET

L2

Workers are sorting beans to remove any dark or broken pieces.

Video

6

Ergonomic

ET

L2

Workers standing/sitting for long periods of time.

Picture

7

Ergonomic

ET

L2

Uncomfortable chairs.

Picture

8

Ergonomic

ET

L3

Operators standing all the time.

Video

9

Ergonomic

ET

L3

Hard to move and a need to bend under machines to pass.

V+P

10

Ergonomic

ET

L4

Empty crates are pulled from the empty crate area to the crate loading machine.

Video

11

Ergonomic

ET

L4

700 cans are put on a crate.

Video

12

Ergonomic

ET

L4

Pushing full crate to sterilizing machine.

Video

13

Ergonomic

ET

L5

Push full basket into retort.

Video

1

ET: Every time, EW: Everywhere, Data: Represents the available data about the finding, Pictures: for more details, see Appendix (Q), Video: For more details, Check attached CD. V+P: Video and Pictures are available

Page | 470

Table 7.54: Cont. safety and human factors survey.

Finding

Hazard Type

Date

Location

Description

Data

14

Ergonomic

ET

L5

Pull full basket from retort.

-

15

Chemical

ET

L5

Facing hot steam from sterilizing machine

Video

16

Ergonomic

ET

L5

Push full basket to unloading machine.

Video

17

Ergonomic

ET

L6

Pull & push to unload from basket to labeling machine.

Video

18

Ergonomic

ET

L6

Pull & Push empty basket back to empty crate area.

Video

19

Ergonomic

ET

L7

Labels are manually inspected by a single worker.

V+P

20

Ergonomic

ET

L8

Stacking product on pallets.

Video

21

Ergonomic

ET

L8

Pulling empty pallet.

V+P

22

Physical

ET

L9

Very high noise level next to the welding machine.

Video

23

Ergonomic

ET

L9

Loading welding machine with 5 to 10 kg group of blanks.

V+P

24

Ergonomic

ET

L9

Feeding slitting machine with tin sheets.

Video

25

Ergonomic

8\11

L9

Applying welding test on welded blanks.

Video

26

Ergonomic

8\11

L9

Using old and heavy tools to apply test.

Video

27

Ergonomic

8\11

L9

Operators setting up the seaming machine.

Video

28

Physical

17\11

EW

High temperature & humidity levels.

-

29

Physical

17\11

EW

Glare on instruction boards.

Picture

Page | 471

Table 7.55: Cont. safety and human factors survey.

Finding

Hazard Type

Date

Location

Description

Data

30

Safety

17\11

L2

Emergency exit was blocked.

video

31

Ergonomic

17\11

L2

Filling machine from heavy oil drums.

V+P

32

Physical

17\11

L5

Unstable pressure gauge.

Video

33

Ergonomic

17\11

L5

Operator setting up sterilizing machine.

Picture

34

Ergonomic

17\11

L7

Operator setting up labeling machine.

V+P

35

Safety

26\11

L1

Emergency exit was blocked.

Picture

36

Safety

26\11

L1

Lifting worker on a forklift

Video

37

Ergonomic

26\11

L3

Manual can filling.

Video

38

Safety

26\11

L3

Emergency exit was blocked.

V+P

39

Safety

26\11

L9

Emergency exit was blocked.

Picture

40

Safety

26\11

L 10

Emergency exit not obvious and hard to reach.

Video

41

Ergonomic

26\11

L 10

Control buttons are not classified.

Picture

42

Safety

26\11

L 11

Emergency exit was blocked and located next to the main door.

Picture

43

Safety

26\11

L 11

Lifting worker on a forklift.

Video

44

Ergonomic

28\11

L1

Workers lifting 50 kg beans bags to fill tanks.

Video

45

Safety

5\12

L8

Forklift bumps into worker.

Video

Page | 472

7.6 Quick-win Improvements Table 7.56: Quick win Improvement.

No.

Finding #

Hazard Description

1

2

Slippery floor

2

3,22

High noise level

3

4

No safety signs

4

5,11,19,20,24,37

Repetitive motion

5

7

Uncomfortable chairs.

6

8

Standing all the time.

7

15

Hot steam.

8

26

Old, heavy, and unergonomically designed tools.

9

28

High temperature and humidity level.

10

29

Glare on instruction board.

11

30,35,38,39,40,42

Blocked emergency exits.

12

32

Unstable pressure gauge.

13

36,43

Lifting workers on a forklift.

14

41

Control buttons without instructions.

Recommendations



Try as much as possible to minimize the amount of water while cleaning the factory.  Wear boots.

 

Wear ear muffs.

Add instruction board that contains safety signs.



Educate workers on the importance of changing their body posture every once in a while.  Change worker every so often.



Use chairs that are ergonomically designed.



Provide workers with chairs so that they can rest every once in a while.

 

Replace old tools with light, ergonomically designed tools.

  

Add fans to the factory to reduce the temperature and humidity levels.

Change the material of the board to a type that does not reflect light. Change the position of the board to reduce the glare effect.



Educate workers to the importance of clearing the area around the emergency exit.

 

Wear protective masks.

Replace with new one.

Educate workers to the risks of their action.



Add instructions to show their use.

Page | 473

Figure 7.9: Safety instruction board

7.7 Long-term Improvement Table 7.57: Long term improvement.

No.

Finding #

Tool Used

Hazard Description

Recommendations

1

1

-

H2S Gas.

The government should provide a sewage system.

2

9

-

Not easy to move from one machine to another.

Rearrange machine layout.

3

5,6,11,19,20,23,2 4,25,27,31,33,34, 44

RULA

Uncomfortable\awkward body posture with repetitive motion.

1

4

10,12,17,18,21

SNOOK tables

Push\pull heavy items (Create \Pallet).

*

5

20,23,44

NIOSH

Repetitive lifting with body twisting.

*

6

5,11,13,19,20, 23

RRM

Long working hours.

*

*

* Note that recommendations will be explained separately for each case in the next section.

Page | 474

Methodologies Human factors tools were applied on the findings introduced in the long term improvement table, to rank the findings and determine whether to change it.

RULA RULA is a quick survey method for use in ergonomic investigations of workplaces where muscular skeletal disorders are reported. It is a screening tool that assesses biomechanical and postural loading on the whole body. RULA scores indicate the level of intervention required to reduce MSD (Muscular skeletal disorders) risks. Furthermore, it compliments other ergonomic methods. RULA can be applied manually, through a program from the following site “http:\\www.rula.co.uk\survey.html”, or through Job Hazard Pro1. Most of the postures have been assessed manually except for a posture that has two different scores, one for the right hand and one for the left. The score was found using the program as an example. Print screen of the final outcome is available below. For the grand score “C” of the posture assessment:  A score of one or two shows an acceptable posture.  A score of three or four indicates further investigation is needed and changes may be required.  A score of five or six indicates investigation and changes are required soon.  A score of seven or more indicates investigation and changes are required immediately.

1

It includes five major risk assessment tools, which are recognized and recommended by OSHA.

Page | 475

By using RULA software the final score, action, and action level for each location in the factory were obtained.1 Filling Line: Case description: In this case female workers repetitively separate the beans from dark or broken ones. 

Final RULA score: 4

 Action: Investigate further. Figure 7.10

Recommendation: Use ergonomically designed chairs with back rest, and lower the chair height so that the worker does not need to bend.

Case description: A male worker is filling a machine with oil. The process takes more than one minute in the same body posture.  Final RULA score: 7  Action: Investigate and change immediately. Figure 7.11

Recommendation: Place the oil tank in a high place and use an alternative method for filling.

1

For more details, see Appendix (R)

Page | 476

Case Description: Male worker is repetitively loading cans into a crate, with 700 cans fitting into one crate. 

Final RULA score: 7



Action: Investigate and change immediately.

Recommendation: Use an automated loading machine where the worker only has to operate it and not apply too much muscular force to load the cans into the crate. Figure 7.12

Case Description: Operator is setting up the labeling machine 

Final RULA score: 3



Action: Investigate further.

Recommendation: Educate the worker on the importance of changing his body posture while setting up the machine; for example, bending his knees rather than his back. Figure 7.13

Page | 477

Case Description: Male worker inspects lables. 

Final RULA score: 4



Action: Investigate further.

Recommendation:  Educate the worker on the importance of changing his body posture every once in a while.  Train different workers to do the same job to break the repetitive sequence. Figure 7.14

Case Description: Male worker is stacking the final product which in a box that contains 24 cans, weighing 400g each. 

Final RULA score: 7

 Action: Investigate and change immediately. Figure 7.11

Recommendation: Introduce an automated machine that stacks the boxes instead of the worker. The worker would only

Figure 7.15

have to operate it rather than repetitively lift the boxes.

Page | 478

Can Line: Case Description: Male worker is applying welding test on a welded can to check the quality of the weld. 

Final RULA score: 7

 Action: Investigate and change immediately. Recommendation:

Figure 7.16

Change the tool into an ergonomically designed one to make testing easier.

Page | 479

NIOSH National Institute for Occupational Safety and Health have developed an “occupational lifting” formula to compute recommended weight limits. This has great influence on the health of the carrier. There are certain assumptions related to applying the NIOSH equation such as the temperature being favorable for lifting, smooth lifting and so on. The measurements required are shown from figure 7.17 the calculations are done for the origin and destination of a certain act. One could be safe, the other harmful. NIOSH can be applied manually or through a program from the following site “http:\\www.emcins.com\lc\niosh.htm”.

Figure 7.17: Diagram showing all the distances required to substitute into the equation.

Page | 480

The Recommended weight limit is calculated from the following equation: RWL = LC * HM * VM * DM * AM * FM * CM LI = W \ RWL

Where,  RWL: Recommended weight limit



AM: Asymmetric multiplier

 LC: Load constant



FM: Frequency multiplier

 HM: Horizontal multiplier



CM: Coupling multiplier

 VM: Vertical multiplier



LI: Lifting index

 DM: Distance multiplier



W: Load weight

Note that, If the lifting index is less than one then the posture is fine for most workers. If greater than one then the job has to be redesigned and finally if it is greater than 3 then it poses a significant risk.

Figure 7.18: All the factors in the equation, and how each multiplier is calculated from the real data

Page | 481

Case description: Male worker is repetitively lifting 5-10 kg metal blanks from the slitting machine to the wilding machine.

Origin

Destination

Figure 7.19 Table 7.58: Multipliers.

Hand location

Origin

Angle

Vert.

Dest.

Freq.

Dist.

Origin

Dest.

Lifts /min

H

V

H

V

D

A

A

F

36

112

66

176

64

0

135

9

Time

hours

Object coupling

C 10

poor

RWL = 23 * (25/36) * (1- (0.003*|110-75| ) * (0.82 + (4.5/64)) *0.57 * 0.15*0.9 = 23 * (0.695) * (0.895) * (0.891) * (0.77) = 0.9815 ~ 1 Kg W (actual weight of object) = 5 kg LI = W/RWL = 5 / .9815

LI = W/RWL = 10 / .9815 = 10.188 > 3 (significant risk)

= 5.094 > 3 (significant risk) W (actual weight of object) = 10 kg Page | 482

Recommendation: Reccomendation: Join the slitting machine with the welding machine by a conveyor to eliminate the lifting operation.

SNOOK Tables Snook tables19 were originally published by Snook in 1978 and by Snook and Ciriello in 1991. Snook tables are used for lowering, lifting, pushing and pulling efforts. Snook tables are less precise than NIOSH since they are based on psychophysical measures rather than biomechanical. Data required include the type of effort, whether the job is carried out by a male or female, the distance moved, and the frequency. Appropriate tables are then used in order to reach the maximum acceptable force. Can Loading: Case Discreption: A male worker pulling a 30kg create filled whith 700 cans, each can weighing 400g, for 10 meters. The height of his hand is 1.3 m, and he repeats this process every 30 minutes. • Result: maximum acceptable weight is 28 kg. From Snook pull table results, it was concluded that the worker exceeded the weight limit. It is recommended a hoist is added to carry the crates from the loading machine to the sterilizing machine.

19

Figure 7.20

For more details about Snook tables, see Appendix (T)

483

Case Discreption: A male worker pulling a 32kg pallet for 3 meters, where the height of his hand is 0.7m, and he aproximatly does this process every 30 min. • Result:

maximum

acceptable

weight is 37 kg. From Snook pull table results; we can conclude that the worker did not exceed the weight limit. Figure 7.21

Page | 484

Rest Required in Minutes To find the rest required in minutes we use the equation: R = T [(W-S)/(W-1.5)] Where; • T: Total work time in min. • W: Average energy consumption of work in kcal/min. • S: Recommended average energy expenditure (4 or 5 kcal/min). Working hours in the factory: 10 hrs/day = 600 min 55 min/day break Total time = 600-55 = 545min. The rest required for the beans inspection belt workers: W = 1.6 kcal/min. R=545[(1.6 - 4)/(1.6 -1.5)] = 22.71 minutes < 55 minutes. Therefore, the rest time is acceptable. The rest required for the label inspector: W = 3.75 kcal/min. R=545[(3.75 - 4)/(3.75 -1.5)] = 60.5 minutes > 55 minutes. Therefore, the worker needs more breaks. Calculating the rest required for the final product stacker: W = 8.75 kcal/min. R=545[(8.75 - 4)/(8.75 -1.5)] = 350 minutes >> 55 minutes. Therefore, the job is very risky and the worker needs more rest. Page | 485

Discomfort Survey A survey20 was distributed amongst seventy workers to record the level of discomfort for each body part. It contained questions about the type of discomfort that they suffer and to which part of the body.

Discomfort Survey 21

20

11 7

7 5

5 3

3

3

2

2

2

R

ig ht Le low ft er lo le Le wer g M ft l id sh eg lo old w R er er ig b Le ht s ac ft ho k up ld pe er R ra ig ht rm t R ig Le high ht ft up thi p g Le er h ft arm f U or a p R per rm ig ht bac fo k R ra ig rm ht w Bu ris t to t ck s

3

Figure 7.22

The Pareto Chart results show that most of the workers are complaining from their right and left lower leg. Suggested tips to minimize injury risk during standing work: 1. Remember to move around. 2. Take breaks and stretch. 3. Watch your posture.

20

For more details, check Appendix (U)

Page | 486

7.6 Management Control

After analyzing the results of the checklist and the survey table, it was suggested a new, specialized department to the management system, which consists of:  Departmental Safety officer (DSO).  Safety Supervisor (SS). DSO responsibilities: 1. Apply and update OSHA regulations. 2. Develop training and refresher courses about safety and ergonomics. These courses include: 

Instructions about using personal protective equipment.



Instructions about doing the job in a safe way.

3. Develop a monthly journal which will be distributed to the workers. These journals contain: 

The accidents that occurred in the previous month, as well as the causes of the accidents, the suggested corrective actions and the suggested preventive actions.



An honors list, containing the names of the workers who are following the safety rules.



Useful safety and ergonomics information that benefits the workers.



Workers comments and answers to workers questions.

4. Develop Safety Manual 5. Organize occupational safety and health committee which consists of the supervisors of the factory shops. Also prepare regular committee meetings to monitor the workers’ safety performance. 6. Develop yearly safety reports to monitor the safety performance in both the filling line, and the can production line. 7. Develop monthly injury records.

Page | 487

Safety Supervisor responsibilities: 1. Investigate the factory using workplace safety checklists21. 2. Observe workers safety performance during working hours. 3. Apply training and refreshing courses to the workers. 4. Apply safety and ergonomics tools and analyze the results. In order to do their job properly, it is imperative for the DSO and SS to communicate with the other departments and workers regularly, to keep them informed of what is expected. These departments are:  Management: 1. Approval on training courses. 2. Funding. 3. Assessment of staff requirements. 4. Reactive response to existing problems. 5. Funds for modifying existing equipment.  Engineering department: 1. Evaluation

of

basic

workstation

design

and

making

appropriate

modifications to reduce or eliminate physical stress.  Line supervisor: 1. Record important information, such as high risk jobs. 2. Identify production trends. 3. Supervise workers and eliminate any risky actions.  Operators: 1. Attentive, open to new ideas, and asking questions. 2. Suggest improvements that might control the jobs’ physical stress. 3. Follow the company’s procedures for reporting an accident.

21

Provided in Appendix (P)

Page | 488

 Purchasing department: 1. Purchase appropriate ergonomics equipment and tools.  Maintenance department: 1. Maintain factory machines. 2. Maintain safety and ergonomic equipment and tools. Injury and Accident Record It is important for the company to have a well recorded medical injury and accident record because it helps in understanding what happened in an accident and why it occurred, which can lead to preventive actions in similar situations. Record keeping steps after an accident or an injury occur include: 1. Investigate the accident. 2. Compile data in a report. 3. Analyze the report. 4. Take preventive actions so that further accidents of the same type will not occur again. Keeping records will make it easier to point to the direct and indirect costs of an accident.. 

Direct Costs: o Medical expenses. o Replacement of damaged items. o Compensation paid to an injured employee. o … Etc.

Page | 489



Indirect Costs: o Lost time of injured employees. o Time lost on investigation, and preparing reports. o Damage to tools, equipment, materials or property. o Losses resulting from reduced productivity of injured workers upon return to work. o Loss of profit because of lost work time and idle machines. o Overhead costs that continue during lost work.

Also, laws and regulations that require record keeping and reporting injuries are other reasons for keeping records. At the same time, records help in identifying hazards, are used in establishing or adjusting insurance rates, and to assign legal penalties.

Page | 490

7.7 Conclusion In this project, the working conditions inside the factory were assessed. When possible, hazards were removed from the workplace to try and minimize the chances of workers sustaining significant injuries. This was done by applying multiple human factors tools as RULA and Snook pull/push tables, to eradicate any unhealthy postures during work or activities that cause too much fatigue to the workers. Of the 45 problems identified, 61% were ergonomic, 22% safety, 13% physical, and 4% were chemical hazards. It was found that 45% of the findings have exceeded the maximum acceptable lifting weight, body posture score, or maximum acceptable pulling weight. It is hoped that the company has been educated as to the important role that safety and human factors engineers can play in ensuring the safety of their workers and avoiding any expensive accidents from occurring.

Page | 491

References 

Safety and Health for engineers, Roger L.Brauer (1994)



Human Factors in engineering and design, Sanders and McCormick- Seventh Edition



http:\\ google.com



http:\\en.wikipedia.org



http:\\www.emcins.com\lc\niosh.htm



http:\\www.cdc.gov\niosh\



http:\\www.rula.co.uk\



http:\\www.osha.gov\



https:\\www.ekginc.com\?p=services_ergonomics



http:\\www.ccohs.ca\oshanswers\safety_haz\materials_handling\



http:\\libertymmhtables.libertymutual.com\CM_LMTablesWeb\taskSelection.do?action=initTas kSelection



http:\\www.minerals.csiro.au\safety\physhaz.htm



http:\\www.saftek.com\osha\checklists.html



http:\\www.ccohs.ca\oshanswers\safety_haz\forklift\checks.html?print



http:\\www.labour.gov.on.ca\english\hs\guidelines\lifttrucks\index.html



http:\\www.labour.gov.on.ca\english\hs\alerts\i10.html



http:\\www.worksmartontario.gov.on.ca\scripts\default.asp?contentID=2-61&mcategory=health#H2



http:\\www.stayingalive.ca\fire_checklist.html



http:\\www.stanford.edu\dept\EHS\prod\training\checklist\index_inspection.html



http:\\www.safety.uwa.edu.au\forms\workplace_safety_checklist



http:\\www.worksafesask.ca\topics\hazards.html



http:\\www.safety.uwa.edu.au\policies#physical



http:\\www.ccohs.ca\oshanswers\



http:\\www.cdc.gov\niosh\docs\2004-101\default.html



http:\\www.managementsuLort.com\factorytoolbox.htm



http:\\bfa.sdsu.edu\ehs\index.htm (A

Page | 492

8. Facilities Planning

Page | 493

Page | 494

8.1 Introduction

Facilities' planning determines how an activity’s fixed assets best support achieving the facility objectives. In general, 20%-50% of total operating expenses are attributed to material handling. With effective facilities planning, the material handling costs can be reduced by at least 30%. In this project the layout of the National Canned Food Production and Trading Co. was studied with the aim of achieving the facility’s objectives, in order to best be able to manufacture its products and deliver them to its customers by analyzing the existing problems and if possible finding appropriate solutions. Enhancing the satisfaction of the objectives and relationships of the fourteen major departments was attempted. The function of each department and its relationship with the others was studied. The flow of raw materials, semi-finished products and final products between the departments was focused on.

Problem Statment

The following are the problems that were noticed regarding the current layout of the factory: 

The machines are too crammed.



Pathways are obstructed.



Inventory spread throughout the factory.



Wasted Space.



Floor area not clearly visible.

Throughout this study, the feasibility of eliminating these problems was studied.

Page | 495

Objectives

The following objectives are what were aimed to be achieved throughout this study of the facility layout and the relationship and interactions that exists between the departments. A.

Minimize the cost of distance traveled.

B.

Smooth intradepartmental flow.

C.

Improve the overall aesthetics of the layout.

D.

Utilize space more efficiently.

Solution Approach

The current layout of the facility was studied and new layouts were proposed by using the RDM and CRAFT software. Both layouts were scored based on their ability to meet the criteria set in the objectives of the study, and the one that best met the criteria was chosen. The costs of adopting the new layout were justified by means of cash flow analysis.

Page | 496

8.2 Current Layout

Departments

1. Can Production; The can production department includes all the machines used to make the empty cans. After they are produced, an overhead conveyor is used to move the empty cans to the empty can storage department.

Figure 8.23: Seaming machine (part of the can production department).

Page | 497

2. Empty Storage Can; The produced empty cans arrive to this area by the overhead conveyors; they are palletized and kept until they are needed.

Figure 8.24: Empty cans in storage.

3. Storage and Mixing tanks; In the storage and mixing department, the beans are brought from the cold storage area and are soaked in the tanks with the all the additives necessary until they are ready to be taken to the hoppers in the raw material preparation department.

Page | 498

4. Raw Material Preparation; In this department, the beans are brought from the storage and mixing tanks and are left to soak until the beans are soft enough, and are then washed in the real washer and manually inspected for any defective beans.

Figure 8.25: Workers manually inspect the beans.

Page | 499

5. Can Filling and Coding; In this department, the empty cans are filled using a solid filler machine with the beans that come from the raw material preparation, and with brine using the liquid filler machine. The cans are then coded with the production and expiration dates by the coding machine.

Figure 8.26: Codes showing production and expiry dates.

Page | 500

6. Can Sterilizing; After the cans have been filled and coded, they are taken to the sterilizing department by crates. There, the cans are put in four rotaries which use steam to cook and sterilize the can.

Figure 8.27: Can Sterilizing Machine.

Page | 501

7. Labeling and Packaging; After the cans have been through the sterilizing department, they are moved by crates to the labeling and packaging department where the cans are manually transported, from the crate to the conveyor, by a worker. The cans go through the labeling machine, then each 12 cans are wrapped together and placed on a small box that the packaging machine makes. Every two boxes are placed on top of each other to form a carton.

Figure 8.28: Packed cartons wrapped in plastic.

Page | 502

8. Filled Cans Inventory Store: After the cans have been packaged into cartons of 24 cans, they are palletized and taken to the filled cans inventory store by a forklift.

Figure 8.29: Inventory Storage.

9. Labels Storage; The labels storage department is a small space where the boxes of empty labels are stored until they are ready to be used by the labeling machine. When needed, boxes of labels are transported to the labeling machine by a worker using a crate.

Figure 8.30: Labels moved from storage by crates.

Page | 503

10. Cold Storage Area; The beans are stored in the cold storage area until they are needed for production and are taken to the storage and mixing tanks.

11. Office; There is one office for one employee inside the can plant. It’s very small and is currently located next to the raw material preparation department.

12. Maintenance Room; The maintenance room is the room where all the maintenance tools and equipment are kept.

13. Water Treatment Room; The water treatment room is where the water that is to be used in the production line is cleaned and purified. It also supplies the water needed through pipes.

14. Vinegar Production Line; The National Canned Food Production and Trading Co. also produce vinegar. The vinegar production line is inside the can plant, and occupies a very small area.

Page | 504

Blue Print of Factory

Figure 8.9: Blue Print of the Factory.

505

As-Is Layout #5 Can Filling #10 Cold

#6 Can Sterilizing

and

Storage Coding

#13

Treat-

#8 Filled

#14 Vinegar

Line

Water

Cans

ment Room

Inventor

#4 Raw

y

Material

Storage

Preparatio n

#3 Storage

#1 Can Production 1.

#7 Labeling and

and Mixing

Packaging

Tanks

#2 Empty Cans

12.25 m

Storage #11

#12

Offic.

Main t.

Figure 8.10: As-Is Layout of the Factory.

#9 Labels inventor y Page | 506

As-Is Layout with dimensions

Figure 8.11: As-Is Layout of the Factory with dimensions.

Page | 507

As-Is Layout showing flow between departments

Figure 8.12: As-Is Layout of the Factory with dimensions.

Page | 508

Department Areas Table 8.59: Departments' Areas.

#

Department name

Area (m2)

1

Can Production

254.8

2

Empty Can Storage

107.8

3

Storage and Mixing Tanks

75.14

4

Raw Material Preparations

169.6

5

Can Filling and Coding

65

6

Can Sterilizing

147.6

7

Labeling and Packaging

171.15

8

Filled Cans Inventory Store

183.52

9

Labels Storage

43.4

10

Cold Storage Area

64.24

11

Office

6.25

12

Maintenance Room

6.25

13

Water Treatment Room

30.15

14

Vinegar Production Line

7.2

Total

1332.1

509

Grid Layout For the grid layout, all areas were rounded to the nearest 25m 2 Departments 10, 11 and 14 were ignored because they are not involved in can production/filling, and they are small. Table 8.60: Number of Grids.

#

Area (m2) Rounded # Grids

1

254.8

250

10

2

107.8

100

4

3

75.14

75

3

4

169.6

175

7

5

65

75

3

6

147.6

150

6

7

171.15

175

7

8

183.52

175

7

9

43.4

50

2

10

64.24

75

3

11

6.25

0

0

12

6.25

0

0

13

30.15

25

1

14

7.2

0

0

510

Each Grid represents 25m2

10

5

10

10

5

6

6

6

6

6

6

5

4

3

1

1

7

7

8

13

4

3

1

1

7

7

8

8

2

4

3

1

1

7

7

8

8

2

4

4

1

1

7

8

8

2

4

4

1

1

9

9

2 Figure 8.13: Grid Blocks representing the As-Is Layout.

Page | 511

8.3 Material Handling

The following are the material handling modes that were considered. They represent the way material andcans are moved from one department to the other. The material handling modes are described in more detail in section 4. Empty Can's Overhead Conveyors

Figure 8.14: Overhead Conveyor.



Conveyor

Figure 8.15: Conveyor linking raw material preparation department and can filling department.

Page | 512



Forklifts

Figure 8.16: Forklifts.



Crates

Figure 8.17: Crate.



Pipes: Flow through pipes was neglected because its cost represents a negligible proportion of the total costs.

Page | 513

Forklifts Material Handling Modes between Departments

Crates Overhead Conveyor Conveyor Can Making Flow

1

2

Can

Can Inventory

Production

Storage

Water Pipes Can Filling Flow 9

13

Label

Water

Storage

Treatment Room

10

3

4

5

Cold

Storage and

Raw

Can Filling

Storage

Mixing

Material

and Coding

Tanks

Prep.

11

12

14

Office

Maintenanc

Vinegar

e

Line

6 Can Sterilizing

7 Labeling and Packaging

8 Filled Can Inventory Storage

514

Table 8.3-Material Handling Modes.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1

2

3

4

5

6

7

8

9

10

11

12

13

14

―

conveyor

0

0

0

0

0

0

0

0

0

0

0

0

―

0

0

conveyor

0

0

0

0

0

0

0

0

0

―

forklifts

0

0

0

0

0

0

0

0

pipes

0

―

conveyor

0

0

0

0

forklifts

0

0

pipes

0

―

crates

0

0

0

0

0

0

0

0

―

crates

0

0

0

0

0

pipes

0

―

forklifts

crates

0

0

0

0

0

―

0

0

0

0

0

0

―

0

0

0

0

0

―

0

0

0

0

―

0

0

0

―

0

0

―

0 ―

515

Table 8.61: Average Number of trips or units per day.

1 1 2 3 4 5 6 7 8 9 10 11

―

2 96000 ―

(1)

3

4

5

0

0

0

0 ―

0 (3)

20

―

84000

(2)

0 84000 ―

(2)

6

7

8

9

10

11

12

13

14

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

(3)

0

0

0

20

0

0

0

0

120

0

0

0

0

0

0

0

0

―

120

0

0

0

0

0

0

0

(6)

0

0

0

0

0

0

0

0

0

0

0

―

0

0

0

0

0

―

0

0

0

0

―

0

0

0

―

0

0

―

0

(4)

(4)

―

(5)

39

―

1

12 13 14

―

N.B. (n) denotes that the data point will be explained in the Data Collection and Calculations section.

516

Table 8.62: Average Cost (KD) per trip or unit.

1 1 2 3 4 5 6 7 8 9 10 11 12 13 14

―

2 (7)

3.3E-06 ―

3

4

5

0

0

0

0 ―

(7)

0 0.0944 ―

3.3E-06 (8)

0 (9)

2.7E-05 ―

6

7

8

9

10

11

12

13

14

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

―

0

0

0

0

0

―

0

0

0

0

―

0

0

0

―

0

0

―

0

0 (10)

0.03068 ―

0

0

0

0

0

0

(10)

0.03068 ―

0

0.0944

0 (8)

0.0944 ―

(8)

(10)

0.03068

―

Page | 517

Table 8.63: Average Cost(KD) per day.

1 2 3 4 5 6 7

1

2

3

4

5

6

7

8

9

10

11

12

13

14

―

0.32043

0

0

0

0

0

0

0

0

0

0

0

0

―

0

0

0.28037

0

0

0

0

0

0

0

0

0

―

1.89E+00

0

0

0

0

0

0

0

0

0

0

―

0.45125

0

0

0

0

1.89E+00

0

0

0

0

―

3.68208

0

0

0

0

0

0

0

0

―

3.68208

0

0

0

0

0

0

0

―

3.68E+00

3.07E-

0

0

0

0

0

0

0

0

0

0

0

―

0

0

0

0

0

―

0

0

0

0

―

0

0

0

―

0

0

―

0

02 8 9 10 11 12 13 14

―

―

Page | 518

Data Collection and Calculations

Avg. Number of trips/units (1) 96,000 empty cans are produced in the can production department, and are moved by overhead conveyors to the empty can storage area. (2) 84,000 empty cans are moved to join the can filling and coding department through overhead conveyors. (3) 20 forklift trips are needed to move the raw materials needed from the storage and mixing tanks to the raw material preparation department. (4) 120 crate trips are needed to move the filled cans from the can filling and coding department to the can sterilizing department and from there to the labeling and packaging department. (5) 39 forklift trips are needed to move the cans to the final inventory storage. (6) 1 crate load trip is needed to move the required labels from the labels storage to the labeling and packaging department. Avg. Cost/trip or Avg. Cost/unit (7) Conveyor costs 0.3204 KD/day ; 3.33778E-06 KD/can. (8) Forklifts' drivers' average salary is KD 95.735 /month; (÷ 26 days/month) = 3.682 KD/day; (÷ 39 trips/day) = 0.094414 KD/trip. (9) Conveyor Costs 2.256 KD/day; 2.686E-05 KD/can. (10) Worker's (pushing crate) salary is 3.682 KD/day; (÷ 120 trips/day) = 0.030684 KD/trip.

519

8.4 Method 1: Relationship Diagramming (RDM) Method

The Relationship Diagramming Method is a procedure applied in many layout algorithms. It involves creating a relationship chart which identifies the priority of the presence of one department next to the other by using letters. Table 8.64: REL Key.

Letter

Relation

A

Absolutely Important

E

Essential

I

Important

O

Ordinary

U

Unimportant

X

Undesirable

The following REL chart was created by studying the flow between the departments and asking factory employees and management about the necessity of the proximity between each department and the others.

520

REL Chart Table 8.65: Deparment Relationships.

14. Vinegar

production line

13. Water treatment

room

12. Maintenance

room

11. Office

10. Cold storage

area

9. Labels storage

14. Vinegar production line

8. Filled cans

13. Water treatment room

inventory store

12. Maintenance room

7. Labeling and

11. Office

packaging

10. Cold storage area

6. Can sterilizing

9. Labels storage

5. Can filling and

8. Filled cans inventory store

Coding

7. Labeling and packaging

4. Raw material

6. Can sterilizing

preparations

5. Can filling and Coding

3. Storage and

4. Raw material preparations

Mixing tanks

3. Storage and Mixing tanks

2. Empty can

2. Empty can storage

-

storage

1. Can production

1. Can production

E

O

O

E

O

O

U

U

U

X

U

I

U

-

I

O

E

U

U

E

U

U

U

U

O

U

-

E

O

O

O

U

U

E

X

U

I

U

-

A

I

I

I

U

E

O

U

E

U

-

A

I

I

U

U

X

U

I

U

-

A

I

U

U

X

U

E

U

-

A

E

U

O

U

O

U

-

O

U

U

U

O

U

-

U

U

U

O

U

-

U

U

O

U

-

U

O

U

-

O

U

-

U -

521

REL Diagram

Figure 8.18: REL Diagram.

522

Relationship Diagramming Worksheet Table 8.66:REL Diagramming Worksheet.

1

2

3

A E

2,5

1,5,8

4,10

4

5

6

7

8

5

4,6

5,7

7,8

3,10,1

1,2

13

9

2

9

7

10

11

12

3,4

13

14

4,6

3 I

13

O 3,4,6,

3

2,13

6,7,8

7,8,13

4,8

4,5

4,5

4,13

5,6,7

1,2,11

3

1,3

1,3,11,1

9,13

7

5 8,13

13

4,7,13

13

3

2,7,8,9 10,11,1 2

U 8,9,1

6,7,9,1

8,9,1

9,12,1

9,10,1

2,9,1

2,10,12

1,3,10

1,2,3,4,

1,2,5,6

2,8,9,1

1,2,3,

0

0

2

4

2

0

14

11,12,1

5

7,8,9,1

0

4

5

14

11,12,1

14

14

12,1

4

6,10,11

1

12,14

5,6,7,

6,7,8,9

12,14

12,14

8

10,11,1

9,10,1

2

1

13

4

4

14

1,2,3,4,

14 X

11

11

11

11

1,3,5,6

523

Iteration 1 Start with department #5 since it’s one of the departments with the highest number of “A” relationships and it has the largest E relationships.

5

Figure 8.19: Iteration 1.

Iteration 2 Place department #6 because it has the highest number of “A” relationships with department 5.

6

5

Figure 8.20: Iteration 2.

The next iterations are based on the following ranking hierarchy: “AA”, “AE”, “AI”, “EE”, “EI”, “E*”, “II”, “I*”. Where * corresponds to “O” and “U”.

Page | 524

Iteration 3 From the table below, department #4 was selected. Table 8.67: Iteration 3.

Dept. 1 E5*6

Dept. 9 *5*6

Dept. 2 E5*6

Dept.

*5*6

10 Dept. 3 *5*6

Dept.

*5*6

11 Dept. 4 A5I6

Dept.

*5*6

12 Dept. 7 I5

Dept.

E6I5

13 Dept. 8 I5

Dept.

*5*6

14

4 6

5

Figure 8.21: Iteration 3.

Page | 525

Iteration 4 From the table below, department #13 was selected. Table 8.68: Iteration 4.

Dept. 1 E5*4*6

Dept. 10

E4*5*6

Dept. 2 E5*4*6

Dept. 11

*4*5*6

Dept. 3 E4*5*6

Dept. 12

*5*6*4

Dept. 7 I4I5

Dept. 13

E6E4I5

Dept. 8 I4I5

Dept. 14

*4*5*6

Dept. 9 *4*5*6

4

13

6

5

Figure 8.22: Iteration 4.

Page | 526

Iteration 5 From the table below, department #1 was selected. Table 8.69: Iteration 5.

Dept. 1 E5I13*4*6

Dept. 9 *4*5*6*13

Dept. 2 E5*13*4*6 Dept.

E4*5*6*13

10 Dept. 3 E4I13*5*6

Dept.

*4*5*6*13

11 Dept. 7 I4I5*13

Dept.

*5*6*4*13

12 Dept. 8 I4I5*13

Dept.

*4*5*6*13

14

4

13

6

5

1

Figure 8.23: Iteration 5.

Page | 527

Iteration 6 From the table below, department #2 was selected. Table 8.70: Iteration 6.

Dept. 2 E1E5*13*4*6 Dept.

E4*1*5*6*13

10 Dept. 3 E4I13*5*6

Dept.

*4*5*6*13

11 Dept. 7 I4I5*1*13

Dept.

*1*5*6*4*13

12 Dept. 8 I4I5*1*13

Dept.

*1*4*5*6*13

14 Dept. 9 *1*4*5*6*13

4

13

2

6

5

1

Figure 8.24: Iteration 6.

Page | 528

Iteration 7 From the table below, department #10 was selected. Table 8.71: Iteration 7.

Dept. 3 E4I2I13*5*6

Dept.

E2E4*1*5*6*13

10 Dept. 7 I4I5*1*2*13

Dept.

*4*5*6*13

11 Dept. 8 I4I5*2*9*13

Dept.

*1*2*5*6*4*13

12 Dept. 9 *1*2*4*5*6*13 Dept.

*1*2*4*5*6*13

14

10 4

13

2

6

5

1

Figure 8.25: Iteration 7.

Page | 529

Iteration 8 From the table below, department #3 was selected. Table 8.72: Iteration 8.

Dept. 3 E4E10I2I13*5*6

Dept.

*4*5*6*10*13

11 Dept. 7 I4I5*1*10*2*13

Dept.

*1*2*4*5*6*10*13

12 Dept. 8 I4I5*2*9*10*13

Dept.

*1*2*4*5*6*10*13

14 Dept. 9 *1*2*4*5*6*10*13

3

10

4

13

2

6

5

1

Figure 8.26: Iteration 8.

Page | 530

Iteration 9 From the table below, department #7 was selected. Table 8.73: Iteration 9.

Dept. 7 I4I5*1*3*10*2*13

Dept.

*4*5*6*10*13

11 Dept. 8 I4I5*2*3*1*10*13

Dept.

*1*2*3*4*5*6*10*13

12 Dept. 9 *1*2*3*4*5*6*10*13 Dept.

*1*2*3*4*5*6*10*13

14

7

3

10

4

13

2

6

5

1

Figure 8.27: Iteration 9.

Page | 531

Iteration 10 From the table below, department #9 was selected. Table 8.74: Iteration 10.

Dept. 8 I4I5*2*3*9*10*13

Dept.

*1*2*3*4*5*6*7*10*13

12 Dept. 9 E7*1*2*3*4*5*6*10*13 Dept.

*1*2*3*4*5*6*7*10*13

14 Dept.

*4*5*6*7*10*13

11

3

10

7

4

13

2

9

6

5

1

Figure 8.28: Iteration 10.

Page | 532

Iteration 11 From the table below, department #8 was selected. Table 8.75: Iteration 11.

Dept. 8 I4I5*2*3*1*9*10*13

Dept. 12

*1*2*3*4*5*9*6*7*10*13

Dept.

Dept. 14

*1*2*3*4*5*6*7*9*10*13

*4*5*6*7*10*9*13

11

3

10

7

4

13

2

9

6

5

1

8

Figure 8.29: Iteration 11.

Page | 533

Iteration 12 All other departments were randomly assigned since they have the same ranking code and they are not necessary in the can production/filling line. 3

10

7

4

13

2

9

6

5

1

14

11

8

12

Figure 8.30: Iteration 12.

Page | 534

8.5 Method 2: CRAFT

CRAFT (computerized Relative Allocation of Facilities Technique) is the first computer aided layout algorithm. It was introduced by Armour and Buffa in 1963. The input data is represented in the form of an initial block layout and flow and cost matrices. The main objective behind CRAFT is to minimize total transportation cost. CRAFT uses the input data and calculates the centroid of each department and the rectilinear distances between the centroids, then stores them in a matrix. It then determines the initial layout score by multiplying the from-to-chart i.e. the flow matrix, by the distance and cost matrices. Next, CRAFT aims to improve the layout by performing all-possible two-way exchanges, which involve switching the place of two departments, and three-way exchanges, which involve changing three. It selects the interchange that results in the least cost at each iteration, unless no further reduction in cost is possible. CRAFT was used to develop a layout alternative for the factory's current layout, if possible, resulting in lower material handling costs.

Page | 535

CRAFT Output Initial Layout

Figure 8.31: CRAFT Initial Layout.

Initial MH Cost (KD/day)

124.1587

CRAFT Alternatives

Figure 8.32: 2-way Exchange.

2-way Exchange MH Cost (KD/day)

94.50639

Page | 536

Figure 8.33: 3-way Exchange.

3-way Exchange MH Cost (KD/day)

110.7229

Figure 8.34: 2-way followed by 3-way Exchange.

2-way followed by 3-way Exchange MH Cost (KD/day)

94.50639

Figure 8.35: 3-way followed by 2-way Exchange.

2-way followed by 3-way Exchange MH Cost (KD/day)

74.18327

Page | 537

The best layout developed by CRAFT was using the 3-way followed by 2-way exchange method. This layout alternative was massaged and compared with the layout developed by the RDM method.

8.6 Comparison of Method 1 and Method 2: Massaged Layouts

A: CRAFT Alternative 10

2

2

2

1

1

1

12

10

2

5

5

1

1

1

10

13

4

5

6

1

1

3

4

4

4

6

1

1

8

8

3

4

4

4

6

6

11

8

8

3

7

7

7

6

6

14

8

8

7

7

7

7

9

9

8

Figure 8.36: Grid Blocks representing the CRAFT Alternative Layout.

\

Page | 538

B: RDM Alternative 2

2

9

9

6

7

7

2

2

3

5

6

7

7

8

1

1

3

5

6

7

7

8

8

1

1

3

5

6

7

8

8

1

1

4

4

6

10

8

8

1

1

4

4

6

10

1

1

4

4

13

10

14

11

12

Figure 8.37: Grid Blocks representing the RDM Alternative Layout.

After massaging both alternatives, the layouts were input into CRAFT to display the actual MH cost associated with our massaged layouts.

Page | 539

A: CRAFT Layout

Figure 8.38: CRAFT Alternative Layout.

CRAFT Layout MH Cost

50.52348

B: RDM Layout

Figure 8.39: RDM Alternative Layout.

RDM Layout MH Cost

79.45359

Page | 540

Prioritization Matrix The following evaluation criteria were selected to be used in comparing both layout alternatives in order to determine which is better. Each criterion was then compared to the other and a score was given based on how important each criterion was with respect to the other. Table 8.76: Weights used to compare the importance of each pair.

Weight 1 5 10 1/5 1/10

Meaning Equally Important Significantly more important Extremely more important Significantly less important Extremely less important

Evaluation Criteria: A. Minimize the cost of distance traveled. B. Smooth intradepartmental flow. C. Improve the overall aesthetics of the layout. D. Space utilization.

NB. Relative Weight = Row Totals/Total. Table 8.77: Prioritization Matrix.

A

B

C

D 5

Row Totals 21

Relative Weight 0.57

A

1

5

10

B

1/5

1

5

1

7.2

0.20

C

1/10

1/5

1

1/5

1.5

0.04

D

1/5

1

5

1

7.2

0.20

Column Total

1.5

7.2

21

7.2

36.9

1.00

Page | 541

A: Minimize Cost of Distance Traveled Table 8.78: Criterion A.

A

CRAFT

RDM 5

Row Totals 6

Relative Weight 0.83

CRAFT

1

RDM

1/5

1

1 1/5

0.17

Column Totals

1.2

6

7.2

1

A lower MH cost was associated with the CRAFT alternative.

B: Smooth Intradepartmental Flow Table 8.79: Criterion B.

B

CRAFT

RDM 10

Row Totals 11

Relative Weight 1.53

CRAFT

1

RDM

1/10

1

1 1/10

0.15

Column Totals

1.1

11

12.1

1.68

Based on the study of the flow between the departments, the alternative developed by CRAFT had a smooth flow, resulting in fewer overlapping flows.

Page | 542

C: Improve Overall Aesthetics of the Layout Table 8.80: Criterion C.

C

CRAFT

RDM 1/5

Row Totals 1.2

Relative Weight 0.17

CRAFT

1

RDM

5

1

6

0.83

Column Totals

6

1 1/5

7.2

1

The alternative developed by the RDM had more regular shaped departments than the alternative developed by CRAFT, therefore it was deemed to look better than the alternative developed by CRAFT.

D: Space Utilization Table 8.81: Criterion D.

D

CRAFT

RDM 1/5

Row Totals 1.2

Relative Weight 0.17

CRAFT

1

RDM

5

1

6

0.83

Column Totals

6

1 1/5

7.2

1

The RDM layout gathered all the originally wasted space into one area which the factory could then use parts of as storage instead of having to randomly store items throughout the factory.

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Ranking Alternatives Based on Scores Table 8.82: Final Ranking of Alternatives.

A

B

C

D 0.03

Row Totals 0.81

Relative Weight 0.72

CRAFT

0.47

0.30

0.01

RDM

0.09

0.03

0.03

0.16

0.32

0.28

Column Totals

0.57

0.33

0.04

0.20

1.13

1.00

Table 8.83: Alternative Scores.

Alternative

Score

A

72%

B

28%

Based on the final score, the CRAFT alternative was considered to be the better choice as the new layout.

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8.7 Proposed Layout #9 Labels inventory

#6 Can Sterilizing

#2 Empty Cans Storage

#7 Labeling and Packaging

#5 Can Filling and

#3 storage and Mixing Tanks

#8 Filled

Coding

Cans Inventor y Storage

#1 Can Production #4 Raw Material Prep. #13 Water

Room

Storage Area

#14 Vinegar

ment

#10 Cold

Line

Treat-

#12

#11

Main

Offic.

t.

Figure 8.40: Proposed Factory Layout.

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8.8 Savings in Cost Table 8.84: Summary of Costs and Savings.

Material handling cost in initial condition

124.1587 KD/day

Material handling cost in proposed layout

50.52348 KD/day

Savings

59.3 %

Annual Savings

22,975 KD



Average Annual profit = 775911.15 KD.



Average Daily profit = 2487 KD (assuming 12 months, 26 working days).

Therefore, the average daily loss in production, for every day the factory has to stop working in order to change the layout will equal the average daily profit. Assuming it would take approximately 14 – 21 days to change the factory layout, it would cause a 34,818 - 52,227 KD loss in production, on average. Also, assuming productive labor are hired to do the job at an average cost of 2000 KD to change the layout, the total cost is between 37,000 – 55,000 KD.

22,975 KD 22,975 KD

0

1

2

22,975 KD

3

22,975 KD 22,975 KD 22,975 KD 22,975 KD

4

5

6

7

P= 37,000~55,000 KD

Figure 8.41: Cash Flow Diagram.

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Taken P = 37,000 KD •

P = P + A(P/A, i=12%, n=2) = - 37,000 + 22,975(1.69) = 1,827 KD.

Taken P = 55,000 KD •

P = P + A(P/A, i=12%, n=3) = - 55,000 + 22,975(2.40) = 140 KD.

This change in layout is profitable in almost 2 years if 37,000 KD was invested in changing the layout and is profitable in almost 3 years if 55,000 KD was invested.

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8.9 Conclusion

Facilities planning techniques were used throughout this study in order to propose a new layout that would minimize material handling costs, improve space utilization, allow a smoother interdepartmental flow, and improve the overall aesthetics of the layout. The factory was split into 14 departments while keeping in mind that every department contained a part of the production line that was inseparable.

Two methods were used to propose new, better layouts. To apply those two methods it was necessary to develop a relationship chart, which explains the importance of the existence of every department with respect to the other, to be used in the relationship diagramming method. The flow and cost of the flow between departments and the material handling modes, was also collected and used in CRAFT software.

The layouts developed by both methods were compared based on selected criteria and the best layout was chosen and massaged. The costs of changing the layout were justified showing it would be profitable in a couple of years.

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9. Conclusion

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General Conclusion

By applying IMSE tools on the problems faced at the factory, many improvements were achieved. To start off, the over filling of cans was eliminated. Proper quality control procedures including adequate documentation and statistically reliable raw material sampling plans were also introduced. Also, a safer, more ergonomic work environment was provided for the employees, in order to avoid significant injuries in the workplace and thereby minimize any compensation or repair costs associated with major accidents. By breaking down and analyzing all the costs of the company, areas of waste such as over filling of cans and disparately high transportation costs for some markets, were highlighted and minimized. Furthermore, after studying the current maintenance policies, new plans were proposed. By using Arena simulation software, it was proven that these new plans minimize the maintenance costs whilst increasing daily production. In addition, specialized inventory models, including the EOQ and EPQ, were introduced to optimize the company’s production plans and help meet the demand forecasted for the near future. Having noticed that the transportation costs were high, and that the company is struggling to meet demand, burdened by excessive overtime, distribution plans were developed in order to reduce transportation costs and increase production capacity. Finally, a new proposed layout was introduced to minimize material handling costs, utilize space more efficiently, and improve the overall aesthetics of the factory.

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