Annexes Mihir Baxi
This document was uploaded by user and they confirmed that they have the permission to share it. If you are author or own the copyright of this book, please report to us by using this DMCA report form. Report DMCA
Overview
Download & View Annexes Mihir Baxi as PDF for free.
More details
- Words: 4,202
- Pages: 45
DEVELOPING A MODEL TO ANALYZE THE IMPACTS OF SELF-SERVICE AND WEB CHECK-IN AT AIRPORTS ANNEXURE A Results of Statistical Analysis for Processing Time
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports A-1 Annexure A- Results of Statistical Analysis for Processing Time
ANOVA T EST The processing times where observed at three airports and it was felt necessary to analyze if the data at all three airports has significant variations in the data due to the change in the group size or number of bags per passenger. Thus, statistical analysis was carried out to confirm the same. The single factor Analysis of Variance was performed for all the check-in modes. The results of the test are shown in Table 1, Table 2 and Table 3. It was concluded that there is no significant variations in the data collected at three airports and all the samples can be pooled together. SUMMARY OF TEST FOR CHECK-IN COUNTERS Groups LCY MAN
Count 78 149
Sum 1:43:18 3:40:30
ANOVA Source of Variation Between Groups Within Groups
SS 00:00 00:07
Df 1 225
Total
00:07
226
Average Variance 01:19 00:00 01:29 00:00
MS 00:00 00:00
F 07:03
P-value 0.186607
F crit 11:42
Table 1 - Results of ANOVA test for Check-in Counters
SUMMARY OF TEST FOR SELF-SERVICE KIOSKS Groups LCY LHR MAN
Count 157 190 37
Sum Average Variance 5:11:46 01:59 00:00 6:22:34 02:01 00:00 0:45:37 01:14 00:00
ANOVA Source of Variation Between Groups Within Groups
SS 00:01 00:14
Df 2 381
Total
00:15
383
MS 00:00 00:00
F 21:49
P-value 2.15E-05
F crit 27:57
Table 2 - Results of ANOVA test for Self-Service Kiosks
CRANFIELD UNIVERSITY Department of Air Transport
A-2 Developing a Model to analyze the impacts of Self-service and Web Check-in at airports Annexure A- Results of Statistical Analysis for Processing Time SUMMARY Groups
Count 32 28 101
LCY MAN LHR
Sum 1:01:21 0:31:45 2:28:35
Average 01:55 01:08 01:28
Variance 00:00 00:00 00:00
MS 00:00 00:00
F 02:17
ANOVA Source of Variation Between Groups Within Groups
SS 00:00 00:08
Df 2 158
Total
00:09
160
P-value 0.0246
F crit 16:41
Table 3 - Results of ANOVA test for Self-Service Kiosks
P ROCESSING T IME
FOR
M ODEL
The ANOVA test shows that there is no significant variation in the data observed at three airports thus the all the samples were pooled together. The pooled samples were than analyzed for the probability distributions and the results of the analysis are shown in Table 4 Table 5 & Table 6 and histograms are shown in Figure 1, Figure 2 & Figure 3. Check-in Counters Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Largest(4) Smallest(4) Confidence Level (95.0%)
01:26 00:03 01:15 01:00 00:50 00:00 31:57 07:39 05:05 00:24 05:29 227 04:19 00:27 00:07
Table 4 - Statistical data of pooled samples for Check-in Counters
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports A-3 Annexure A- Results of Statistical Analysis for Processing Time
Self-Service Kiosks Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Largest(4) Smallest(4) Confidence Level (95.0%)
01:56 00:03 01:47 02:00 00:58 00:00 46:40 20:15 06:05 00:10 06:15 384 05:49 00:19 00:06
Table 5 - Statistical data of pooled samples for Self-Service Kiosks
Bag Drop-off Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Largest(4) Smallest(4) Confidence Level (95.0%)
01:31 00:05 01:13 01:00 01:08 00:00 56:40 18:54 08:56 00:14 09:10 160 05:05 00:21 00:11
Table 6 - Statistical data of pooled samples for Bag Drop-off
CRANFIELD UNIVERSITY Department of Air Transport
A-4 Developing a Model to analyze the impacts of Self-service and Web Check-in at airports Annexure A- Results of Statistical Analysis for Processing Time
Processing Time- Check-In 90 80
Frequency
70 60 50 40 30 20 10 0
Processing Time Figure 1 - Histogram of Processing Time for Check-in Counters
Processing Time- Kiosks 110 100
Frequency
90 80 70 60 50 40 30 20 10 0
Processing Time
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports A-5 Annexure A- Results of Statistical Analysis for Processing Time Figure 2 - Histogram of Processing Time for Self-Service Kiosks
Processing Times - Bag Drop-off 50 45 40 Frequency
35 30 25 20 15 10 5 0
Processing Time Figure 3 - Histogram of Processing Time for Bag Drop-off
CRANFIELD UNIVERSITY Department of Air Transport
DEVELOPING A MODEL TO ANALYZE THE IMPACTS OF SELF-SERVICE AND WEB CHECK-IN AT AIRPORTS ANNEXURE B Simulation Model: Description and Users Guide
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-1 Annexure B - Simulation Model: Description and Users Guide
INTRODUCTION The Simulation model is designed in MS Office Excel 2007. The simulation model simulates the check-in process at airport with self-service and web check-in. It is designed to estimate the requirements for a two step self service check-in, which includes kiosks and bag drop-off at the airport. The model enables the users to analyze the impacts of the changes made in resources and estimate the requirements of check-in counters, kiosks and bag drop-off. The structure of the model, the inputs required and interpretation of results have been explained to enable the user to get the maximum utility from the model. The model has been kept as simple as possible and there is no additional software or macros required to run the simulation. The model is limited to the size of the inputs. The model can only compute for maximum 2500 Peak hour passengers or maximum resources of 25 in each mode. 1. Structure of Model The model is designed in Excel2007. To use the model and give inputs, it is first necessary to understand the structure of the model. The data is arranged in number of sheets. Every sheet handles different aspects of the model and has been assigned a color code for ease of orientation of the users. The color codes are as shown below Inputs Results Intermediate Results Calculations
Modify Cannot be Modified by user Cannot be Modified by user Cannot be Modified by user
The user can only modify the input sheets. The Sheets for Results, Intermediate Results and Calculations cannot be modified by the user. The modification of these sheets may result in incorrect answers. The Sheets for calculations have been kept hidden to prevent any accidental change in the formulas. The layout of the sheets can be seen in the Figure 1. Further to this, there are some cells in the input sheets which cannot be modified as they contain formulas and the user can only give inputs in the specific cells. To make it easy for the user to identify such cells, the input cells have been given different colors from the cells which could be not be modified. The color codes are shown below User Inputs Results from the calculations
Modify Cannot be Modified by User
CRANFIELD UNIVERSITY Department of Air Transport
B-2 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
Figure 1 - Layout of the Excel Worksheet
The sheets are arranged in order of the importance to user. The first two sheets “Inputs” and “Arrival profiles” are the input sheets where the user has to input the key values and assumptions for the model. The next four sheets “Queuing Times”, “Server Usage”, “Wait Time Graphs” and “Process Time Graphs” are results that will interest the user and will be useful to draw the conclusions about the resources required. The next set of the sheets are intermediate results and calculations. The calculation sheets are of no direct importance to the user and have been kept hidden. The intermediate results could be interesting to observe for determining the efficiency of the system but as explained cannot be modified. If the user needs to save any of the results from the simulation he/she needs to save all the results sheet in a new workbook as Excel will recalculate all the formulas while opening and closing the file. Thus if the user needs to compare the results between each scenario or each simulation run, it is required to save the results from the result sheet in different workbook. The best way to save graphs is to save them as images so that they can be compared later.
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-3 Annexure B - Simulation Model: Description and Users Guide
2. Inputs in the Model To run the model it is necessary to give correct inputs. Each input parameter is discussed and the key characteristics and limitations are discussed in this section to enable the user to give correct inputs. The input sheet is as shown in the Figure 2.
Figure 2 Check-in SImulation Input Sheet
2.1. Peak Hour Passengers These are number of passengers for the departure in the peak hour at the airport being modeled. This number is limited to 2500 in the model. This is only departure and not the total peak hour passengers at the airport. 2.2. Passenger Mix Profile This is the most important input. There are three default profiles available, which could be selected from the drop down list. The three profiles are Business, Leisure and Mix. The user can select the appropriate profile as per the airport being modeled. This selection dictates the arrival profile of the passengers at the airport. If the user is aware of the arrival profile at the airport being modeled he/she can use that as input, which has been discussed in paragraph 2.8.
CRANFIELD UNIVERSITY Department of Air Transport
B-4 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
2.3. Check-in Open Times This is the opening time for the all the counters, kiosks and bag drop-off. The time has to be given in minutes. It is necessary to decide the check-in open times as that will govern the number of passengers arriving at the airport. This input cannot be Zero. 2.4. Check-in Close Times before STD It is time in minutes for closing of check-in counters before departure time of the flight. If assumed zero, the model considers the full duration between check-in open times and departure. 2.5. Passengers by each Check-in Mode This is expected breakup of the passengers using each mode to check-in in percentages. The user needs to provide the information such that the expected break up totals 100%. The numbers for each mode is calculated by the model and shown in the next cell to it, which cannot be modified by the user. If any of the modes are not available at the airport the user can input zero for the mode, but the total of all three cells should be 100. The user can modify these proportions to analyze the impacts on the available resources. 2.6. Resources Required These cells are the required resources for processing the passengers. These cells cannot have input more than 25 units in each mode. For the existing airport, the actual number of resources can be used as an input and modify to see the impacts on the efficiency. For a proposed new airport the user can start assuming the number of resources at the airport and keep on modifying until the expected results are available or service standards are achieved. 2.7. Passengers Using Bag Drop-off The user needs to assume the number of the passengers using bag drop-off. The bag drop-off is only used by a proportion of passengers from the web check-in and self service kiosks. Thus the user needs to estimate these proportions as per the characteristic at the airport. The 100% from self service kiosks will imply that all the passengers using kiosks will use bag drop-off and same for web check-in. 2.8. Arrival Profile As arrival profile is a crucial factor that affects the queuing and operations of airport it is necessary to adjust it to the airport being modeled. The user can input the arrival profile at the airport in sheet “Arrival Profile”. The arrival profile in the model is defined as cumulative passengers arriving at the airport for each given interval of time. The total time is derived from the subtracting check-in close times from check-in open CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-5 Annexure B - Simulation Model: Description and Users Guide
times and is to be given by user and thus has been converted to percentages. So the user has to first convert the total time in percentage and passengers arriving at each 10% time interval should be calculated and used as input in one of the three columns B, C & D as see in the Figure 3.
Figure 3 - Input for Arrival Profile
The user should not change the heading of the profiles and will be able to input only three different profiles at a time. The rest of the sheet cannot be modified by the user. 3. Interpretation of the Results The results from the simulation are arranged in number of sheets. The results of the simulation are arranged in green sheets. Each sheet is explained here in this section and the subsequent titles represent the names of the sheet in Excel. 3.1. Queuing Times This sheet is summary for the passenger wait times and total processing times for each mode. The user can estimate the number of passengers waiting more than zero, 5 minutes, 10 Minutes, 15 minutes and 20 minutes. The proportions is cumulative so the number of people not waiting at all can be obtained by subtracting passenger waiting more than zero from 100%.
CRANFIELD UNIVERSITY Department of Air Transport
B-6 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
Figure 4 - Queuing Times and Other details
The table also summarizes the Maximum Wait Time and Average Wait Time for each mode. The average Total process times is also indicated in the last column. The total process time includes the waiting time and processing time. 3.2. Server Usage This sheet shows how many passengers were processed by each server in proportion and in numbers. The table enables the user to understand usage of server. The user can compare the performance of each server and decide if he/she wants to reduce one server which is underutilized.
Figure 5 Server Usage
3.3. Waiting Time Graphs This sheet shows the wait time graphs for each passenger by each mode. The graph enables the user to see which passengers have to wait and when the queuing does takes place. It can be seen if the queues are built up at the start or at end of the process. These graphs enable user to decide the increase or decrease in resources for that particular time of the process. The Figure 6 shows the graphs as seen in excel. It will be necessary for user to save these graphs as images for each scenario they need to compare as they are dynamic and will change every time, when file is saved or closed.
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-7 Annexure B - Simulation Model: Description and Users Guide
Figure 6 Wait Time Graphs
3.4. Process Time Graphs This sheet similar to wait time graphs shows the total processing time for each passenger for each mode. The processing times is waiting time plus processing time. The user can thus identify which passengers had taken more time for processing. 3.5. Intermediate Result Sheets There are three sheets for the intermediate results as mentioned earlier they are yellow in color. This sheets actually show the process and the user can see details for each passenger in each mode. The user can see the arrival time, processing time and wait time for each passenger in these sheets. In addition to these, user can also see which passenger was served by which server.
CRANFIELD UNIVERSITY Department of Air Transport
B-8 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
Figure 7 Process Time Graphs
CRANFIELD UNIVERSITY Department of Air Transport
DEVELOPING A MODEL TO ANALYZE IMPACTS OF SELF-SERVICE AND WEB CHECK-IN AT AIRPORTS ANNEXURE C Results from Simulation Model: Scenario 1-Scenario 5
As-is Model
Figure -1 Wait Times, Check-in Counters - As-is Model
Figure -2 Process Times, Check-in Counters - As-is Model Cranfield University | Department of Air Transport
C-1 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
As-is Model
Figure -3 Wait Times, Bag Drop-off - As-is Model
Figure -4 Process Times, Bag Drop-off - As-is Model Cranfield University | Department of Air Transport
C-2 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 1
Figure -5 Wait Times, Check-in Counters - Scenario 1
Figure -6 Process Time, Check-in Counters - Scenario 1 Cranfield University | Department of Air Transport
C-3 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 1
Figure -7 Wait Times, Self Service Kiosk - Scenario 1
Figure -8 Process Times, Self Service Kiosk - Scenario 1 Cranfield University | Department of Air Transport
C-4 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 1
Figure -9 Wait Times, Bag Drop-off - Scenario 1
Figure -10 Process Times, Bag Drop-off - Scenario 1 Cranfield University | Department of Air Transport
C-5 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 2
Figure -11 Wait Times, Check-in Counters - Scenario 2
Figure -12 Process Times, Check-in Counters - Scenario 2
Cranfield University | Department of Air Transport
C-6 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 2
Figure -13 Wait Times, Self Service Kiosk - Scenario 2
Figure -14 Process Times, Self Service Kiosks - Scenario 2 Cranfield University | Department of Air Transport
C-7 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 2
Figure -15 Wait Times, Bag Drop-off - Scenario 2
Figure -16 Process Times, Bag Drop-off - Scenario 2 Cranfield University | Department of Air Transport
C-8 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 3
Figure -17 Wait Times, Check-in Counters - Scenario 3
Figure -18 Process Times, Check-in Counters - Scenario 3 Cranfield University | Department of Air Transport
C-9 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 3
Figure -19 Wait Times, Self Service Kiosks - Scenario 3
Figure -20 Process Times, Self Service Kiosks - Scenario 3 Cranfield University | Department of Air Transport
C-10 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 3
Figure -21 Wait Times, Bag Drop-off - Scenario 3
Figure -22 Process Times, Bag Drop-off - Scenario 3
Cranfield University | Department of Air Transport
C-11 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4A
Figure -23 Wait Times, Check-In Counters - Scenario 4A
Figure -24 Process Times, Check-In Counters - Scenario 4A Cranfield University | Department of Air Transport
C-12 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4A
Figure -25 Wait Times, Self Service Kiosks - Scenario 4A
Figure -26 Process Times, Self Service Kiosks - Scenario 4A Cranfield University | Department of Air Transport
C-13 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4A
Figure -27 Wait Times, Bag Drop-off - Scenario 4A
Figure -28 Process Times, Bag Drop-off - Scenario 4A
Cranfield University | Department of Air Transport
C-14 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4B
Figure -29 Wait Times, Check-In Counters - Scenario 4B
Figure -30 Process Times, Check-In Counters - Scenario 4B
Cranfield University | Department of Air Transport
C-15 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4B
Figure -31 Wait Times, Self Service Kiosks - Scenario 4B
Figure -32 Process Times, Self Service Kiosks - Scenario 4B Cranfield University | Department of Air Transport
C-16 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4B
Figure -33 Wait Times, Bag Drop-off - Scenario 4B
Figure -34 Process Times, Bag Drop-off - Scenario 4B Cranfield University | Department of Air Transport
C-17 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4C
Figure -35 Wait Times, Check-In Counters - Scenario 4c
Figure -36 Process Times, Check-In Counters - Scenario 4C Cranfield University | Department of Air Transport
C-18 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4C
Figure -37 Wait Times, Self Service Kiosks - Scenario 4C
Figure -38 Process Times, Self Service Kiosks - Scenario 4C Cranfield University | Department of Air Transport
C-19 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4C
Figure -39 Wait Times, Bag Drop-off - Scenario 4C
Figure -40 Process Times, Bag Drop-off - Scenario 4C Cranfield University | Department of Air Transport
C-20 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5A
Figure -41 Wait Times, Check-In Counters - Scenario 5A
Figure -42 Process Times, Check-In Counters - Scenario 5A Cranfield University | Department of Air Transport
C-21 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5A
Figure -43 Wait Times, Self Service Kiosks - Scenario 5A
Figure -44 Process Times, Self Service Kiosks - Scenario 5A Cranfield University | Department of Air Transport
C-22 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5A
Figure -45 Wait Times, Bag Drop-off - Scenario 5A
Figure -46 Process Times, Bag Drop-off – Scenario 5A Cranfield University | Department of Air Transport
C-23 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5B
Figure -47 Wait Times, Check-In Counters - Scenario 5B
Figure -48 Process Times, Check-In Counters - Scenario 5B Cranfield University | Department of Air Transport
C-24 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5B
Figure -49 Wait Times, Self Service Kiosks - Scenario 5B
Figure -50 Process Times, Self Service Kiosks - Scenario 5B Cranfield University | Department of Air Transport
C-25 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5B
Figure -51 Wait Times, Bag Drop-off - Scenario 5B
Figure -52 Process Times, Bag Drop-off - Scenario 5B Cranfield University | Department of Air Transport
C-26 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5C
Figure -53 Wait Times, Check-In Counters - Scenario 5C
Figure -54 Process Times, Check-In Counters - Scenario 5C Cranfield University | Department of Air Transport
C-27 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5C
Figure -55 Wait Times, Self Service Kiosks - Scenario 5C
Figure -56 Process Times, Self Service Kiosks - Scenario 5C Cranfield University | Department of Air Transport
C-28 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5C
Figure -57 Wait Times, Bag Drop-off - Scenario 5C
Figure- 58 Process Times, Bag Drop-off - Scenario 5C Cranfield University | Department of Air Transport
C-29 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports A-1 Annexure A- Results of Statistical Analysis for Processing Time
ANOVA T EST The processing times where observed at three airports and it was felt necessary to analyze if the data at all three airports has significant variations in the data due to the change in the group size or number of bags per passenger. Thus, statistical analysis was carried out to confirm the same. The single factor Analysis of Variance was performed for all the check-in modes. The results of the test are shown in Table 1, Table 2 and Table 3. It was concluded that there is no significant variations in the data collected at three airports and all the samples can be pooled together. SUMMARY OF TEST FOR CHECK-IN COUNTERS Groups LCY MAN
Count 78 149
Sum 1:43:18 3:40:30
ANOVA Source of Variation Between Groups Within Groups
SS 00:00 00:07
Df 1 225
Total
00:07
226
Average Variance 01:19 00:00 01:29 00:00
MS 00:00 00:00
F 07:03
P-value 0.186607
F crit 11:42
Table 1 - Results of ANOVA test for Check-in Counters
SUMMARY OF TEST FOR SELF-SERVICE KIOSKS Groups LCY LHR MAN
Count 157 190 37
Sum Average Variance 5:11:46 01:59 00:00 6:22:34 02:01 00:00 0:45:37 01:14 00:00
ANOVA Source of Variation Between Groups Within Groups
SS 00:01 00:14
Df 2 381
Total
00:15
383
MS 00:00 00:00
F 21:49
P-value 2.15E-05
F crit 27:57
Table 2 - Results of ANOVA test for Self-Service Kiosks
CRANFIELD UNIVERSITY Department of Air Transport
A-2 Developing a Model to analyze the impacts of Self-service and Web Check-in at airports Annexure A- Results of Statistical Analysis for Processing Time SUMMARY Groups
Count 32 28 101
LCY MAN LHR
Sum 1:01:21 0:31:45 2:28:35
Average 01:55 01:08 01:28
Variance 00:00 00:00 00:00
MS 00:00 00:00
F 02:17
ANOVA Source of Variation Between Groups Within Groups
SS 00:00 00:08
Df 2 158
Total
00:09
160
P-value 0.0246
F crit 16:41
Table 3 - Results of ANOVA test for Self-Service Kiosks
P ROCESSING T IME
FOR
M ODEL
The ANOVA test shows that there is no significant variation in the data observed at three airports thus the all the samples were pooled together. The pooled samples were than analyzed for the probability distributions and the results of the analysis are shown in Table 4 Table 5 & Table 6 and histograms are shown in Figure 1, Figure 2 & Figure 3. Check-in Counters Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Largest(4) Smallest(4) Confidence Level (95.0%)
01:26 00:03 01:15 01:00 00:50 00:00 31:57 07:39 05:05 00:24 05:29 227 04:19 00:27 00:07
Table 4 - Statistical data of pooled samples for Check-in Counters
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports A-3 Annexure A- Results of Statistical Analysis for Processing Time
Self-Service Kiosks Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Largest(4) Smallest(4) Confidence Level (95.0%)
01:56 00:03 01:47 02:00 00:58 00:00 46:40 20:15 06:05 00:10 06:15 384 05:49 00:19 00:06
Table 5 - Statistical data of pooled samples for Self-Service Kiosks
Bag Drop-off Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Count Largest(4) Smallest(4) Confidence Level (95.0%)
01:31 00:05 01:13 01:00 01:08 00:00 56:40 18:54 08:56 00:14 09:10 160 05:05 00:21 00:11
Table 6 - Statistical data of pooled samples for Bag Drop-off
CRANFIELD UNIVERSITY Department of Air Transport
A-4 Developing a Model to analyze the impacts of Self-service and Web Check-in at airports Annexure A- Results of Statistical Analysis for Processing Time
Processing Time- Check-In 90 80
Frequency
70 60 50 40 30 20 10 0
Processing Time Figure 1 - Histogram of Processing Time for Check-in Counters
Processing Time- Kiosks 110 100
Frequency
90 80 70 60 50 40 30 20 10 0
Processing Time
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports A-5 Annexure A- Results of Statistical Analysis for Processing Time Figure 2 - Histogram of Processing Time for Self-Service Kiosks
Processing Times - Bag Drop-off 50 45 40 Frequency
35 30 25 20 15 10 5 0
Processing Time Figure 3 - Histogram of Processing Time for Bag Drop-off
CRANFIELD UNIVERSITY Department of Air Transport
DEVELOPING A MODEL TO ANALYZE THE IMPACTS OF SELF-SERVICE AND WEB CHECK-IN AT AIRPORTS ANNEXURE B Simulation Model: Description and Users Guide
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-1 Annexure B - Simulation Model: Description and Users Guide
INTRODUCTION The Simulation model is designed in MS Office Excel 2007. The simulation model simulates the check-in process at airport with self-service and web check-in. It is designed to estimate the requirements for a two step self service check-in, which includes kiosks and bag drop-off at the airport. The model enables the users to analyze the impacts of the changes made in resources and estimate the requirements of check-in counters, kiosks and bag drop-off. The structure of the model, the inputs required and interpretation of results have been explained to enable the user to get the maximum utility from the model. The model has been kept as simple as possible and there is no additional software or macros required to run the simulation. The model is limited to the size of the inputs. The model can only compute for maximum 2500 Peak hour passengers or maximum resources of 25 in each mode. 1. Structure of Model The model is designed in Excel2007. To use the model and give inputs, it is first necessary to understand the structure of the model. The data is arranged in number of sheets. Every sheet handles different aspects of the model and has been assigned a color code for ease of orientation of the users. The color codes are as shown below Inputs Results Intermediate Results Calculations
Modify Cannot be Modified by user Cannot be Modified by user Cannot be Modified by user
The user can only modify the input sheets. The Sheets for Results, Intermediate Results and Calculations cannot be modified by the user. The modification of these sheets may result in incorrect answers. The Sheets for calculations have been kept hidden to prevent any accidental change in the formulas. The layout of the sheets can be seen in the Figure 1. Further to this, there are some cells in the input sheets which cannot be modified as they contain formulas and the user can only give inputs in the specific cells. To make it easy for the user to identify such cells, the input cells have been given different colors from the cells which could be not be modified. The color codes are shown below User Inputs Results from the calculations
Modify Cannot be Modified by User
CRANFIELD UNIVERSITY Department of Air Transport
B-2 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
Figure 1 - Layout of the Excel Worksheet
The sheets are arranged in order of the importance to user. The first two sheets “Inputs” and “Arrival profiles” are the input sheets where the user has to input the key values and assumptions for the model. The next four sheets “Queuing Times”, “Server Usage”, “Wait Time Graphs” and “Process Time Graphs” are results that will interest the user and will be useful to draw the conclusions about the resources required. The next set of the sheets are intermediate results and calculations. The calculation sheets are of no direct importance to the user and have been kept hidden. The intermediate results could be interesting to observe for determining the efficiency of the system but as explained cannot be modified. If the user needs to save any of the results from the simulation he/she needs to save all the results sheet in a new workbook as Excel will recalculate all the formulas while opening and closing the file. Thus if the user needs to compare the results between each scenario or each simulation run, it is required to save the results from the result sheet in different workbook. The best way to save graphs is to save them as images so that they can be compared later.
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-3 Annexure B - Simulation Model: Description and Users Guide
2. Inputs in the Model To run the model it is necessary to give correct inputs. Each input parameter is discussed and the key characteristics and limitations are discussed in this section to enable the user to give correct inputs. The input sheet is as shown in the Figure 2.
Figure 2 Check-in SImulation Input Sheet
2.1. Peak Hour Passengers These are number of passengers for the departure in the peak hour at the airport being modeled. This number is limited to 2500 in the model. This is only departure and not the total peak hour passengers at the airport. 2.2. Passenger Mix Profile This is the most important input. There are three default profiles available, which could be selected from the drop down list. The three profiles are Business, Leisure and Mix. The user can select the appropriate profile as per the airport being modeled. This selection dictates the arrival profile of the passengers at the airport. If the user is aware of the arrival profile at the airport being modeled he/she can use that as input, which has been discussed in paragraph 2.8.
CRANFIELD UNIVERSITY Department of Air Transport
B-4 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
2.3. Check-in Open Times This is the opening time for the all the counters, kiosks and bag drop-off. The time has to be given in minutes. It is necessary to decide the check-in open times as that will govern the number of passengers arriving at the airport. This input cannot be Zero. 2.4. Check-in Close Times before STD It is time in minutes for closing of check-in counters before departure time of the flight. If assumed zero, the model considers the full duration between check-in open times and departure. 2.5. Passengers by each Check-in Mode This is expected breakup of the passengers using each mode to check-in in percentages. The user needs to provide the information such that the expected break up totals 100%. The numbers for each mode is calculated by the model and shown in the next cell to it, which cannot be modified by the user. If any of the modes are not available at the airport the user can input zero for the mode, but the total of all three cells should be 100. The user can modify these proportions to analyze the impacts on the available resources. 2.6. Resources Required These cells are the required resources for processing the passengers. These cells cannot have input more than 25 units in each mode. For the existing airport, the actual number of resources can be used as an input and modify to see the impacts on the efficiency. For a proposed new airport the user can start assuming the number of resources at the airport and keep on modifying until the expected results are available or service standards are achieved. 2.7. Passengers Using Bag Drop-off The user needs to assume the number of the passengers using bag drop-off. The bag drop-off is only used by a proportion of passengers from the web check-in and self service kiosks. Thus the user needs to estimate these proportions as per the characteristic at the airport. The 100% from self service kiosks will imply that all the passengers using kiosks will use bag drop-off and same for web check-in. 2.8. Arrival Profile As arrival profile is a crucial factor that affects the queuing and operations of airport it is necessary to adjust it to the airport being modeled. The user can input the arrival profile at the airport in sheet “Arrival Profile”. The arrival profile in the model is defined as cumulative passengers arriving at the airport for each given interval of time. The total time is derived from the subtracting check-in close times from check-in open CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-5 Annexure B - Simulation Model: Description and Users Guide
times and is to be given by user and thus has been converted to percentages. So the user has to first convert the total time in percentage and passengers arriving at each 10% time interval should be calculated and used as input in one of the three columns B, C & D as see in the Figure 3.
Figure 3 - Input for Arrival Profile
The user should not change the heading of the profiles and will be able to input only three different profiles at a time. The rest of the sheet cannot be modified by the user. 3. Interpretation of the Results The results from the simulation are arranged in number of sheets. The results of the simulation are arranged in green sheets. Each sheet is explained here in this section and the subsequent titles represent the names of the sheet in Excel. 3.1. Queuing Times This sheet is summary for the passenger wait times and total processing times for each mode. The user can estimate the number of passengers waiting more than zero, 5 minutes, 10 Minutes, 15 minutes and 20 minutes. The proportions is cumulative so the number of people not waiting at all can be obtained by subtracting passenger waiting more than zero from 100%.
CRANFIELD UNIVERSITY Department of Air Transport
B-6 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
Figure 4 - Queuing Times and Other details
The table also summarizes the Maximum Wait Time and Average Wait Time for each mode. The average Total process times is also indicated in the last column. The total process time includes the waiting time and processing time. 3.2. Server Usage This sheet shows how many passengers were processed by each server in proportion and in numbers. The table enables the user to understand usage of server. The user can compare the performance of each server and decide if he/she wants to reduce one server which is underutilized.
Figure 5 Server Usage
3.3. Waiting Time Graphs This sheet shows the wait time graphs for each passenger by each mode. The graph enables the user to see which passengers have to wait and when the queuing does takes place. It can be seen if the queues are built up at the start or at end of the process. These graphs enable user to decide the increase or decrease in resources for that particular time of the process. The Figure 6 shows the graphs as seen in excel. It will be necessary for user to save these graphs as images for each scenario they need to compare as they are dynamic and will change every time, when file is saved or closed.
CRANFIELD UNIVERSITY Department of Air Transport
Developing a Model to analyze the impacts of Self-service and Web Check-in at airports B-7 Annexure B - Simulation Model: Description and Users Guide
Figure 6 Wait Time Graphs
3.4. Process Time Graphs This sheet similar to wait time graphs shows the total processing time for each passenger for each mode. The processing times is waiting time plus processing time. The user can thus identify which passengers had taken more time for processing. 3.5. Intermediate Result Sheets There are three sheets for the intermediate results as mentioned earlier they are yellow in color. This sheets actually show the process and the user can see details for each passenger in each mode. The user can see the arrival time, processing time and wait time for each passenger in these sheets. In addition to these, user can also see which passenger was served by which server.
CRANFIELD UNIVERSITY Department of Air Transport
B-8 Developing of Model to analyze the impacts of Self-service and Web Check-in at airports Annexure B – Simulation Model: Description and Users Guide
Figure 7 Process Time Graphs
CRANFIELD UNIVERSITY Department of Air Transport
DEVELOPING A MODEL TO ANALYZE IMPACTS OF SELF-SERVICE AND WEB CHECK-IN AT AIRPORTS ANNEXURE C Results from Simulation Model: Scenario 1-Scenario 5
As-is Model
Figure -1 Wait Times, Check-in Counters - As-is Model
Figure -2 Process Times, Check-in Counters - As-is Model Cranfield University | Department of Air Transport
C-1 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
As-is Model
Figure -3 Wait Times, Bag Drop-off - As-is Model
Figure -4 Process Times, Bag Drop-off - As-is Model Cranfield University | Department of Air Transport
C-2 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 1
Figure -5 Wait Times, Check-in Counters - Scenario 1
Figure -6 Process Time, Check-in Counters - Scenario 1 Cranfield University | Department of Air Transport
C-3 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 1
Figure -7 Wait Times, Self Service Kiosk - Scenario 1
Figure -8 Process Times, Self Service Kiosk - Scenario 1 Cranfield University | Department of Air Transport
C-4 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 1
Figure -9 Wait Times, Bag Drop-off - Scenario 1
Figure -10 Process Times, Bag Drop-off - Scenario 1 Cranfield University | Department of Air Transport
C-5 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 2
Figure -11 Wait Times, Check-in Counters - Scenario 2
Figure -12 Process Times, Check-in Counters - Scenario 2
Cranfield University | Department of Air Transport
C-6 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 2
Figure -13 Wait Times, Self Service Kiosk - Scenario 2
Figure -14 Process Times, Self Service Kiosks - Scenario 2 Cranfield University | Department of Air Transport
C-7 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 2
Figure -15 Wait Times, Bag Drop-off - Scenario 2
Figure -16 Process Times, Bag Drop-off - Scenario 2 Cranfield University | Department of Air Transport
C-8 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 3
Figure -17 Wait Times, Check-in Counters - Scenario 3
Figure -18 Process Times, Check-in Counters - Scenario 3 Cranfield University | Department of Air Transport
C-9 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 3
Figure -19 Wait Times, Self Service Kiosks - Scenario 3
Figure -20 Process Times, Self Service Kiosks - Scenario 3 Cranfield University | Department of Air Transport
C-10 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 3
Figure -21 Wait Times, Bag Drop-off - Scenario 3
Figure -22 Process Times, Bag Drop-off - Scenario 3
Cranfield University | Department of Air Transport
C-11 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4A
Figure -23 Wait Times, Check-In Counters - Scenario 4A
Figure -24 Process Times, Check-In Counters - Scenario 4A Cranfield University | Department of Air Transport
C-12 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4A
Figure -25 Wait Times, Self Service Kiosks - Scenario 4A
Figure -26 Process Times, Self Service Kiosks - Scenario 4A Cranfield University | Department of Air Transport
C-13 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4A
Figure -27 Wait Times, Bag Drop-off - Scenario 4A
Figure -28 Process Times, Bag Drop-off - Scenario 4A
Cranfield University | Department of Air Transport
C-14 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4B
Figure -29 Wait Times, Check-In Counters - Scenario 4B
Figure -30 Process Times, Check-In Counters - Scenario 4B
Cranfield University | Department of Air Transport
C-15 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4B
Figure -31 Wait Times, Self Service Kiosks - Scenario 4B
Figure -32 Process Times, Self Service Kiosks - Scenario 4B Cranfield University | Department of Air Transport
C-16 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4B
Figure -33 Wait Times, Bag Drop-off - Scenario 4B
Figure -34 Process Times, Bag Drop-off - Scenario 4B Cranfield University | Department of Air Transport
C-17 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4C
Figure -35 Wait Times, Check-In Counters - Scenario 4c
Figure -36 Process Times, Check-In Counters - Scenario 4C Cranfield University | Department of Air Transport
C-18 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4C
Figure -37 Wait Times, Self Service Kiosks - Scenario 4C
Figure -38 Process Times, Self Service Kiosks - Scenario 4C Cranfield University | Department of Air Transport
C-19 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 4C
Figure -39 Wait Times, Bag Drop-off - Scenario 4C
Figure -40 Process Times, Bag Drop-off - Scenario 4C Cranfield University | Department of Air Transport
C-20 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5A
Figure -41 Wait Times, Check-In Counters - Scenario 5A
Figure -42 Process Times, Check-In Counters - Scenario 5A Cranfield University | Department of Air Transport
C-21 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5A
Figure -43 Wait Times, Self Service Kiosks - Scenario 5A
Figure -44 Process Times, Self Service Kiosks - Scenario 5A Cranfield University | Department of Air Transport
C-22 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5A
Figure -45 Wait Times, Bag Drop-off - Scenario 5A
Figure -46 Process Times, Bag Drop-off – Scenario 5A Cranfield University | Department of Air Transport
C-23 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5B
Figure -47 Wait Times, Check-In Counters - Scenario 5B
Figure -48 Process Times, Check-In Counters - Scenario 5B Cranfield University | Department of Air Transport
C-24 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5B
Figure -49 Wait Times, Self Service Kiosks - Scenario 5B
Figure -50 Process Times, Self Service Kiosks - Scenario 5B Cranfield University | Department of Air Transport
C-25 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5B
Figure -51 Wait Times, Bag Drop-off - Scenario 5B
Figure -52 Process Times, Bag Drop-off - Scenario 5B Cranfield University | Department of Air Transport
C-26 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5C
Figure -53 Wait Times, Check-In Counters - Scenario 5C
Figure -54 Process Times, Check-In Counters - Scenario 5C Cranfield University | Department of Air Transport
C-27 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5C
Figure -55 Wait Times, Self Service Kiosks - Scenario 5C
Figure -56 Process Times, Self Service Kiosks - Scenario 5C Cranfield University | Department of Air Transport
C-28 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Scenario 5C
Figure -57 Wait Times, Bag Drop-off - Scenario 5C
Figure- 58 Process Times, Bag Drop-off - Scenario 5C Cranfield University | Department of Air Transport
C-29 Developing a Model to Analyze the impacts of Self-service and Web Check-in at Airports Annexure C- Results from Simulation Model
Related Documents
Annexes Mihir Baxi
December 2019 7
Annexes
June 2020 22
Seminar Baxi
November 2019 9
Mihir Resume1
November 2019 7
Mule Mihir
May 2020 7