7qc Tools.pdf

7 QC TOOLS

Let’s do an exercise!

You have to cut down your

house expenditure by 20% per month How will you do it ?

Objective

OBJECTIVE: After the completion of this training programme: You will be able to choose the appropriate QC tool to solve problems in your work area.

Types

7QC tools Check Sheet

Pareto Diagram Cause & Effect diagram

7 QC Tools

Graph &Control charts Histogram

Stratification Scatter Diagram

Check sheet

Check sheet

Check sheet

What is a check sheet?

Why is a check sheet necessary?

Check sheet

Example:

Check sheet

Check sheet Check sheets are forms used for

•checking results of work • verifying and collecting data

Check sheet

Types of Check Sheet Discrete value such as no. Of recording errors, no. of Item sold & Rejections etc.

Indiscrete value such as height, weight, length, time & temp., Etc.

Measured Data

Point Scale Data

1 Point, 2 Point … etc.

Counted Data

Check Sheet Ordered Data 1st, 2nd Order … Very Good, Good, No Good … - Type

Primary Data

YES / NO or  / X - Type

Check sheet

Examples for various types of check sheet Ordered data check sheet Example:

Quality of painting in paint shop can be classified as good, very good, bad etc.

Check sheet

Example: CHECK SHEET FOR PAINT QUALITY (Max 100) Sl No

Time of check

Quality

1

10.00

Good

2

11.00

Very Good

3

12.00

Good

4

13.00

Good

5

14.00

Very Good

6

15.00

Bad

7

16.00

Good

8

17.00

Good

Check sheet

Point scale data check sheet Example: Star ratings of motorcycles

Check sheet

Example: CHECK SHEET FOR STAR RATINGS (Two Wheelers) Sl No

Model

1

TVS VICTOR

2

HERO HONDA SPLENDOR

3

LML FREEDOM

4

BAJAJ BOXER

5

KINETIC CHALLENGER

6

YAMAHA CRUX

Rating

Check sheet

Measured data check sheet Example

Data of bush diameters (Cylindrical grinding) in 3rd Shift Sl. No. Diameter Range No. of components 25.010 – 25.015 1 02 25.015 – 25.020 2 05 25.020 – 25.025 3 08 25.025 – 25.030 4 10 25.030 –25.035 5 19 25.035 – 25.040 6 29 25.040 – 25.045 7 33 25.045 – 25.050 8 36 25.050 – 25.055 9 40 25.055 – 25.060 10 48 25.060 – 25.065 11 57 25.065 – 25.070 12 42 25.070 – 25.075 13 29 25.075 – 25.080 14 15 25.080 – 25.085 15 11 25.085 – 25.090 16 09 25.090 – 25.095 17 05 25.095 – 25.100 18 02 Total 400

Check sheet

Counted data check sheet Example Sales of TVS two wheelers Sl. No. Year

No. of Units

1

1994 – 95

287500

2

1995 – 96

410800

3

1996 – 97

513400

4

1997 – 98

568700

5

1998 – 99

603000

6

1999 – 00

668700

7

2000 – 01

863600

8

2001 – 02

866240

9

2002 – 03

1062000

Check sheet

Example: Primary data check sheet A check sheet with yes or no type data Quality Control Department

Sl. No.

Employee Name

TEIAN award

1

Saravanan

Yes

2

Srinivasan

No

3

Krishnan

Yes

4

Venkatraman

No

5

Subramanian

Yes

6

Padmanabhan

Yes

7

Velu

Yes

8

Senthil

Yes

Check sheet

Check points for check sheets preparation Below items can be added , as necessary 1. The purpose of the checks

2. The items being checked 3. The methods of the checks 4. The dates and times of the checks 5. The person to perform the checks 6. The results

Check sheet

Example of check sheet Defect check sheet Month ,day Component

4/1

2

1 2 3 4 5 6 7 8 9 10

No. of defects

3

4

Check sheet

Example of check sheet Data collection sheet

Check sheet

A1

A2

C1 E1 D1 B1 D2 D1 B2 D2

C2 E2

E1

C1 E2

E1

C2 E2

E1

E2

Check sheet

Make a check list of all the expenses in your home & the amount you spend on these expenses

Check sheet

Sl.No

Expense

Amount

1

House Rent

1500

2

Electricity & Water Bill

250

3

Cable TV Bill

150

4

News paper bill

100

5

Milk

400

6

Maid servant

150

7

Groceries

2000

8

Entertainment & Lifestyle

200

9

Travel

200

10

Educational

1000

11

Hospital

200

12

Loan repayment

1000

13

Clothes

200

14

Petrol

300

15

Others

300

Pareto

Pareto diagram

Pareto

Do you remember this? (14th March 2001 - Eden gardens )

Pareto

Let’s look at the second innings score board: India SS Das

hit wicket b Gillespie

39

S Ramesh

c ME Waugh b Warne

30

VVS Laxman

c Ponting b McGrath

281

SR Tendulkarc Gilchrist b Gillespie

10

SC Ganguly

c Gilchrist b McGrath

48

R Dravid

run out

180

N R Mongia

b McGrath

4

Zaheer Khan

not out

23

Harbhajan Singh

not out

8

Total

657

Pareto

Do you remember this? (14th March 2001 - Eden gardens )

Pareto

Who got the wickets?

O

M

R

W

Zaheer Khan

8

4

30

0

V Prasad

3

1

7

0

Harbhajan Singh

30.3

8

73

6

V Raju

15

3

58

1

S Tendulkar

11

3

31

3

S Ganguly

1

0

2

0

Pareto

Who got the maximum runs? • Laxman & Dravid – 461 / 657 runs. • 22% of the 9 batsmen who batted got 70% of the runs!

Who got the maximum wickets? • Harbhajan & Tendulkar – 9 / 11 wickets. • 30% of the 6 bowlers who bowled got 80% of the wickets!

This illustrates the Pareto principle

Pareto

Pareto • Vilfredo Pareto was an Italian engineer in the 19th Century who studied the number of people in various income classes & declared ‘’20% of the people own 80% of the country’s wealth;

80% of the people own 20% of the country’s wealth”

Pareto

Pareto Principle Pareto principle holds good to the present day in various applications ‘ A few causes lead to many defects; many causes lead to few defects.’

The few causes that lead to many defects are the vital few.

The many causes that lead to few defects are the trivial many.

Pareto

“Get to the biggest problems first” ‘Solve the vital few’

Pareto 200

84 79

150

75

73

55.5 100 75

50

66 33 45

50

25 20

25

15

12

10

8

6

5

4

4

2

2

1

Steps

1. Collect data

2. Arrange data in the descending order 3. Calculate the relative % for individual data 4. Calculate the cumulative % for individual data 5. Draw a graph with scales on both axis 6. Draw bar chart based on data 7. Using cumulative % data, draw cumulative curve 8. Identify the VITAL FEW (thumb rule > 70%)

Quality

Factory production

Manufacturing Planning

Stores

Others

Information Systems

Dept

Research & Development

Service

Personnel

Finance

Production Engineering

Plant Maintenance

Materials

0 Marketing

0

In %

65.5

125 In nos

Creating a Pareto Diagram

100 100

99.5

98.5

97.5

95.5

93.5

91

88

175

Pareto

Same problem, but different approach…

You have to cut down your

house expenditure by 20% / month How will you do it ? Paret o

Pareto

Now take your check sheet. Arrange these expenses & amounts in an order, with the highest expense being the first & lowest expense being the last

Pareto Sl.No

Expense

Amount

1

Groceries

2000

2

House rent

1500

3

Educational

1000

4

Loan repayment

1000

5

Milk

400

6

Petrol

300

7

Others

300

8

Electricity & water bill

250

9

Hospital

200

10

Travel

200

11

Entertainment & lifestyle

200

12

Clothes

200

13

Maid Servant

150

14

Cable TV bill

150 7850

Pareto

Calculate the percentage contribution of each of these expenses.

Percentage can be calculated by the formula

Individual expense Total expense

X 100

Pareto Sl.No

Department

Nos.

Relative %

1

Groceries

2000

25.47

2

House rent

1500

19.10

3

Educational

1000

12.74

4

Loan repayment

1000

12.74

5

Milk

400

5.10

6

Petrol

300

3.83

7

Others

300

3.83

8

Electricity & water bill

250

3.19

9

Hospital

200

2.54

10

Travel

200

2.54

11

Entertainment & lifestyle

200

2.54

12

Clothes

200

2.54

13

Maid Servant

150

1.92

14

Cable TV bill

150

1.92

7850

100

Pareto Sl.No

Department

Nos.

Relative %

Cumulative %

1

Groceries

2000

25.47

25.47

2

House rent

1500

19.10

44.57

3

Educational

1000

12.74

57.31

4

Loan repayment

1000

12.74

70.05

5

Milk

400

5.10

75.15

6

Petrol

300

3.83

78.98

7

Others

300

3.83

82.81

8

Electricity & water bill

250

3.19

86.00

9

Hospital

200

2.54

88.54

10

Travel

200

2.54

91.08

11

Entertainment & lifestyle

200

2.54

93.62

12

Clothes

200

2.54

96.16

13

Maid Servant

150

1.92

98.08

14

Cable TV bill

150

1.92

100

7850

100

100

Pareto Sl.No

Department

Nos.

Relative %

Cumulative %

1

Groceries

2000

25.47

25.47

2

House rent

1500

19.10

44.57

3

Educational

1000

12.74

57.31

4

Loan repayment

1000

12.74

70.05

5

Milk

400

5.10

75.15

6

Petrol

300

3.83

78.98

7

Others

300

3.83

82.81

8

Electricity & water bill

250

3.19

86.00

9

Hospital

200

2.54

88.54

10

Travel

200

2.54

91.08

11

Entertainment & lifestyle

200

2.54

93.62

12

Clothes

200

2.54

96.16

13

Maid Servant

150

1.92

98.08

14

Cable TV bill

150

1.92

100

7850

100

100

VITAL FEW

TRIVIAL MANY

Pareto

100

100

7500

VITAL FEW 75

57.31

5000 44.57

2500

50

25.47

Trivial Many 0

25

0 Groceries

House Rent

Educational Expenses

Loan Repayment

Others

Cumulative(%)

Amount

70.05

Pareto

Why pareto ? • To Clearly prioritise the magnitude of the problem. • To identify the vital few and trivial many problems. • To find 80/20 rule which states that 80% of the problems are created by 20% of the causes.

Pareto 84 79

150

75

73 65.5

125

In nos

100 100

99.5

98.5

97.5

95.5

93.5

91

88

175

55.5

100 75

50

66 33 45

50

25 20

25

15

12

10

8

6

5

4

4

2

2

1

1. The most important problem 2. The rate of each problem to the whole 3. The degree of improvement action 4. The comparison of improvement level 5. Before & after remedial action taken

Quality

Factory production

Manufacturing Planning

Stores

Others

Information Systems

Dept

Research & Development

Service

Personnel

Finance

Production Engineering

Plant Maintenance

Materials

0 Marketing

0

In %

Pareto diagram is used to find out … 200

Relation between Pareto diagram & Cause & effect diagram

The biggest problem has been found. What next?

A combination of Pareto diagram & cause and effect diagram is an ideal way to arrive at the main problem & its causes. Take the biggest problem from the pareto diagram & put it on the right side in the cause & effect diagram. Derive the causes for the same.

Cause & effect diagram Cause & Effect diagram derived from pareto

Pareto chart

Cause-and-effect diagram

Cause & effect diagram

Cause & Effect diagram

Cause & effect diagram

Why Cause & Effect ? • To identify and systematically list the different causes that can be attributed to a problem (or an effect)

• To identify the reasons why a process goes out of control

• To decide which causes to investigate for process improvement.

Cause & effect diagram

What is Effect ? EFFECT = A Result or an outcome EFFECT is What happens

Effect – Tyre puncture

Cause & effect diagram

What is cause ? CAUSE = Reason or Factor contributing to the EFFECT CAUSE is WHY it happens

Cause & effect diagram

The analysis of “why?” for “what?” is

cause and effect diagram

Cause & effect diagram In 1953, Kaoru Ishikawa, Professor of the University of Tokyo, used the Cause & effect diagram for the first time. A cause & effect diagram is also called a fish bone diagram since it looks like the skeleton of a fish.

Cause & effect diagram

The EFFECT or PROBLEM is stated on the right side of the diagram and the major INFLUENCES or CAUSES are listed to the left.

Effect

Cause & effect diagram

There are two steps of making cause & effect diagrams:

2

1. Identify all the causes in one cause & effect diagram. 2. Take all the identified causes & classify them systematically in another cause & effect diagram.

Cause & effect diagram

Cause & effect diagram for identifying the causes

Cause & effect diagram

In this type of cause & effect diagram: • Write all causes. • Don't classify them.

Cause & effect diagram

How to obtain most number of causes?

Brainstorming!

Cause & effect diagram

Remember

DURING BRAINSTORMING: • All the ideas/causes should be noted. • Don’t label any ideas “good” or “bad”. • Encourage free flow of ideas.

Cause & effect diagram

HOW TO DO IT?

1 Effect

Causes

Cause & effect diagram Example Poor operator skill

Wrong inspection method Improper clamping on jig or fixture

Insufficie nt training

Machine vibration

Wrong setting of job on locator

Wrong inspection instrument

Too much tightening of job

1

Wrong spindle speed

No inbound inspection of raw material

Improper material storage

Operator fatigue Wrong feed

Raw material dimension too close to final dimension Improper / warped shape of raw material

Dimensional Variation

Cause & effect diagram

Cause & effect diagram for systematically listing causes

Use these steps to make a successful Cause & effect diagram for systematically listing causes Step 1

List all the causes that have been suggested by team members as a part of brain storming.

Cause & effect diagram Step 2 Connect the sub causes to the main causes. The main causes should then be connected to the effect. Step 3 Assign an importance to each factor, & mark the particularly important factors which seem to have a significant effect.

Step 4 Draw the diagram & continually look for improvement.

Cause & effect diagram

2 Effect

Causes

Cause & effect diagram

MAN

2

MACHINE

Fatigue

Imbalance

HEALTH

STABILITY

Concentration

Illness

Training

Vibration Clamping

SPIRIT Attentiveness

SKILL

JIGS & FIXTURES

Experience

Location Degree of tightening

Inspection

SETTING

QUALITY Storage

Shape FORM Dimension

MATERIAL

Placement on locator Instrument INSPECTION

Feed WORKING

Method

METHOD

Spindle speed

Dimensional Variation

Cause & effect diagram

Thorough investigation of causes

ASK WHY? 5 TIMES 4th Why 3rd Why

5th

C Why

Policies

Procedure

1st Why

D

2nd

People

Why

Plant

Why defects?

Graph&control charts

Graph & Control charts

Graph&control charts

Graph What is Graph? When there are more than 2 interrelated data sets,you write the datasets on a graph so as to clearly define the relationships.

Why Graph? The details of the data should be • Correctly understood • In their entirety • With just a one look

Graph&control charts

Graph Types of Graphs & Its application Bar graphs, line graphs, pie charts, and band graphs. There are also other specialty graphs such as radar charts, Z charts and area graphs

Applications 

To understand relative sizes of numbers



To understand trends over time



To understand percent-ages of totals

Graph&control charts

Pie chart - Composition of sales turnover 2001-02 Export Sales 1%

Vehicle sales 92%

Spare parts sales 6%

Other income 1%

Graph&control charts

Bar chart - Sales performance Plan for 2002-03

Rs Million x 100 300

262

250 184 162

200 133

150

104 83

100 50

194

62

14

17

19

27

91-92

92-93

93-94

41

0 90-91

94-95

95-96

96-97

97-98

98-99

99-00

00-01

'01-02

'02-03

Graph&control charts

Line chart - Hourly output of Workmen 50 45 40

ACTUAL

40 38

45

47

41

44 42

Feb

Mar

42

44 42

43 41

Apr

May

Jun

PLAN

35 30 25 20 15 10 5 0 Jan

Graph&control charts

Line chart Cumulative Percentage 100

100

80 70.05 60

59.26 44.57

40 25.47 20

0 1

2

3

4

5

Histogram

Histogram

Histogram

• In quality control, we try to discover facts by collecting data & then take necessary action based on those facts. • The data is not collected as an end in itself, but as a means of finding out the facts behind the data.

Data

FACTS

Histogram

Example • Consider a sampling inspection. • We take a sample from a lot, carry out measurements on it. • We decide whether we should accept the whole lot or not. • The sample tells us the whether the lot is OK or NOT OK.

The totality of items under consideration is called the population.

Histogram

• The data obtained from a sample helps us to take a decision on the population. • The larger the sample size is, the more information we get about the population. • But an increase of sample size also means an increase in the amount of data

Histogram

• It becomes difficult to understand the population from these data even when they are arranged into tables. • In such a case we need a method which will enable us to understand the population at a glance. A histogram answers our needs.

Histogram

What is histogram ? Histogram is a bar chart…. 25

20

15

The sizes of the vertical bars reflects the number of data

The horizontal axis shows the values of the characteristics

10

5

0

2.50

2.51

2.52

2.53

2.54

2.55

Histogram

How to make a histogram? Let us make a histogram using an example. Example: To investigate the distribution of the diameters of steel shafts produced in the grinding process, the diameters of 90 shafts are measured as shown in the table.

Histogram

Diameter after grinding Sample

Results of Measurement

Number 1 - 10

2.51

2.517

2.522

2.522

2.51

2.511

2.519

2.532

2.543

2.525

11 - 20

2.527

2.536

2.506

2.541

2.512

2.515

2.521

2.536

2.529

2.524

21 - 30

2.529

2.523

2.523

2.523

2.519

2.528

2.543

2.538

2.518

2.534

31 - 40

2.52

2.514

2.512

2.534

2.526

2.53

2.532

2.526

2.523

2.52

41 - 50

2.535

2.523

2.526

2.525

2.532

2.522

2.502

2.53

2.522

2.514

51 - 60

2.533

2.51

2.542

2.524

2.53

2.521

2.522

2.535

2.54

2.528

61 - 70

2.525

2.515

2.52

2.519

2.526

2.527

2.522

2.542

2.54

2.528

71 - 80

2.531

2.545

2.524

2.522

2.52

2.519

2.519

2.529

2.522

2.513

81 - 90

2.518

2.527

2.511

2.519

2.531

2.527

2.529

2.528

2.519

2.521

Histogram

Step 1 Calculate the range (R) Obtain the largest & smallest of observed values & calculate R. R = (the largest observed value) – (the smallest observed value)

Histogram

Diameter after grinding Sample

Maximum Minimum

Value of Value of

Results of Measurement

Number

the Line the line

1 - 10

2.51

2.517

2.522

2.522

2.51

2.511

2.519

2.532

2.543

2.525

2.543

2.51

11 - 20

2.527

2.536

2.506

2.541

2.512

2.515

2.521

2.536

2.529

2.524

2.541

2.506

21 - 30

2.529

2.523

2.523

2.523

2.519

2.528

2.543

2.538

2.518

2.534

2.543

2.518

31 - 40

2.52

2.514

2.512

2.534

2.526

2.53

2.532

2.526

2.523

2.52

2.534

2.512

41 - 50

2.535

2.523

2.526

2.525

2.532

2.522

2.502

2.53

2.522

2.514

2.535

2.502

51 - 60

2.533

2.51

2.542

2.524

2.53

2.521

2.522

2.535

2.54

2.528

2.542

2.51

61 - 70

2.525

2.515

2.52

2.519

2.526

2.527

2.522

2.542

2.54

2.528

2.542

2.515

71 - 80

2.531

2.545

2.524

2.522

2.52

2.519

2.519

2.529

2.522

2.513

2.545

2.513

81 - 90

2.518

2.527

2.511

2.519

2.531

2.527

2.529

2.528

2.519

2.521

2.531

2.511

The

The

Largest Smallest Value

Value

2.545

2.502

Histogram

R = largest value – smallest value = 2.545 – 2.502

= 0.043

Histogram

CLASS INTERVAL Class

1 2 3 4 5 6 7 8 9

2.5005 – 2.5055 2.5055 – 2.5105 2.5105 – 2.5155 2.5155 – 2.5205 2.5205 – 2.5255 2.5255 – 2.5305 2.5305 – 2.5355 2.5355 – 2.5405 2.5405 – 2.5455 Total

Midpoint Frequency marks (tally)

2.503 2.508 2.513 2.518 2.523 2.528 2.533 2.538 2.543

/ //// //// //// //// //// //// //// ////

//// //// //// //// //// //// // //// //// //// //// /

Frequency

1 4 9 14 22 19 10 5 6

90

Histogram

INTERVAL BREADTH Class

1 2 3 4 5 6 7 8 9

2.5005 – 2.5055 2.5055 – 2.5105 2.5105 – 2.5155 2.5155 – 2.5205 2.5205 – 2.5255 2.5255 – 2.5305 2.5305 – 2.5355 2.5355 – 2.5405 2.5405 – 2.5455 Total

Midpoint Frequency marks (tally)

2.503 2.508 2.513 2.518 2.523 2.528 2.533 2.538 2.543

/ //// //// //// //// //// //// //// ////

//// //// //// //// //// //// // //// //// //// //// /

Frequency

1 4 9 14 22 19 10 5 6

90

Histogram

Step 2 Determine the class interval & interval breadth. The class interval is calculated by the formula Class interval = √ n where n is total number of observations Here, n = 90 Therefore, √ n = 9.48. Rounding to nearest integer, The class interval is 9. R Interval breadth = √n = 0.005

=

0.043 9

Histogram

Step 3 Prepare a frequency table form Prepare a form as shown below on which class, mid – point, frequency marks, frequency etc can be recorded Class

Total

Midpoint Frequency marks (tally)

Frequency

Histogram

Step 4 Determine the class boundaries • Each boundary should include the smallest & the largest of values. Steps

• Determine the lower boundary of the first class. • Add the interval breadth to obtain the class boundary

Histogram

The lower boundaries of the first class can be either 2.5000 or 2.5005 (it has to be less than the smallest value 2.502). Therefore, 2.5005 + interval breadth 2.5005 + 0.005 = 2.5055 Therefore first class boundary 2.005 – 2.5055

Histogram

The second class boundary 2.5055 –2.5105 Since the class interval is 9, the last class boundary will be the 9th. Note that this has to contain the largest recorded value. Therefore, 9th class boundary 2.5405 – 2.5455

Histogram

Step 5 Calculate the mid – point of the class Using the following equation, calculate the mid-point of class, & write this down on the frequency table. Mid – point of each class =

Sum of the upper & lower boundaries of each class 2

Histogram

Step 6 Obtain the frequencies Read the observed values one by one & record the frequencies Falling in each class using tally marks, in groups of five as follows: Frequency

Frequency notation

1

/

2

//

3

///

4

////

5

////

Histogram

Class

1 2 3 4 5 6 7 8 9

2.5005 – 2.5055 2.5055 – 2.5105 2.5105 – 2.5155 2.5155 – 2.5205 2.5205 – 2.5255 2.5255 – 2.5305 2.5305 – 2.5355 2.5355 – 2.5405 2.5405 – 2.5455 Total

Midpoint Frequency marks (tally)

2.503 2.508 2.513 2.518 2.523 2.528 2.533 2.538 2.543

/ //// //// //// //// //// //// //// ////

//// //// //// //// //// //// // //// //// //// //// /

Frequency

1 4 9 14 22 19 10 5 6

90

Histogram

How to draw a histogram

Step 1 • Mark the horizontal axis with a scale. • The scale need not be on the base of the class interval.

• A unit of measurement of data can be used. •In the current example we can take 0.01mm of diameter = 10mm on the histogram scale. •Leave a space equal to the class interval on the horizontal axis on each side of the scale.

Histogram

Step 2 Mark the left – hand vertical axis with a frequency scale.

Step 3 Draw the bar chart as per the data in the frequency table.

Step 4 Draw a line on the histogram to represent the mean, & also draw a line representing the specification limit, if any.

Histogram

HISTOGRAM No. of occurances

25

X = 2.5247

20 15 10 5 0

2.50

2.51

2.52

2.53

Diameter in mm

2.54

2.55

Histogram

Application  To analyze processes and discover items to be improved  To research process capability  To control the process (in a time series)

 To verify effects of an improvement

Histogram

Overview of Histogram • The characteristics of the frequency distribution are

shown more clearly when results are plotted in form of block diagram • The horizontal axis is divided into segments corresponding to ranges of the group • On each segment a rectangle is constructed whose height is proportional to the frequency in the group

Histogram

Uses of Histogram A Histogram can be used:

• To display large amounts of data values in a relatively simple chart form. • To tell relative frequency of occurrence.

• To easily see the distribution of the data. • To see if there is variation in the data. • To make future predictions based on the data.

Histogram

Types of distribution • The shape of the distribution gives a more clear concept than mean or standard deviation

• From the distribution we can deduce the peak value of frequency and symmetry of the data range (i) Normal distribution Normal distribution is commonly used type.Here the values are symmetric about the center and area gives the value of probability

Histogram - Interpretation

(ii) Positively skewed Values are more concentrated in one side nearer to origin of x line. Here most of the values lies in the lower part of the values of histogram (iii) Negatively skewed Values are more concentrated in one side far from the origin Values lies in the higher part of the values of histogram

Histogram - Interpretation

(iv) Bi modal distribution In this type of distribution, there are two peak values of frequency

(v) Multi modal distribution There are number of peak values of frequency

Stratification

Stratification

Stratification

Stratification

Stratification Stratification is the act of fine tuning the data in order to make sure of the significance of the assured factors, to the grass root level.

Stratification

Case study Problem : More No. of Accidents

Let us stratify the the data regarding the accidents

Stratification

STATISTICS

REPORTABLE ACCIDENTS

:

08

NON-REPORTABLE ACCIDENTS

:

33

NEAR MISS INCIDENTS

:

21

LOST TIME INJURIES

:

41

MANDAYS LOST

:

187

Rep.acct. for

– Operator not reporting back to duty more than 48hrs

Non-reportable acct. beyond less than 48 hrs

– Operator disablement extending the day of shift but

Lost time injury

– Reportable + Non-reportable

Stratification

ANALYSIS –REPORTABLE ACCIDENT

Total no of reportable accident :

8

Stratification ACCORDING TO CATEGORY

Contract Labour (1) 13%

Regular Employee (4) 49%

Total no.of Reportable accidents : 8

Temp.workman (3) 38%

Stratification

ACCORDING TO PHENOMENON

Adjusting/Cleaning/Loading/Unloading while M/C running

6

Wrong handling of material handling equipment

2

Hit against object

0

Hit by objects/Fallen objects

0

others

0

Fall from Height

0

Fall from Two wheeler

0

Contact with chemical

0

wrong assembly

0

0

1

2

3

4

No of Accidents

Total no.of Reportable accidents : 8

5

6

7

Stratification

ACCORDING TO BODY PARTS INJURED

Leg (2) 25%

Hand (1) 13%

Total no.of Reportable accidents : 8

Finger (5) 62%

Stratification

ACCORDING TO PLANT

Others (1) 13%

Plant-1 (0) 0% Plant-2 (3) 37%

Sp. Wh (1) 13%

R & D (1) 13% Plant-3 (2) 24%

Total no.of Reportable accidents : 8

Stratification

ACCORDING TO UNIT 1

No.of accidents

PLANT-1

0

0

0

0

0

0 FAB

ENG UNIT

Total no.of Reportable accidents : 8

PAINT

VECH UNIT

STORES

Stratification

ACCORDING TO UNIT

No.of accidents

2

PLANT- 2

1

1

1

1

0

0

0

0 FAB

ENG UNIT

Total no.of Reportable accidents : 8

PAINT

VECH UNIT

STORES

PLATING

Stratification

ACCORDING TO UNIT 3

PLANT- 3

No.of accidents

2 2

1

0

0

0

0 M/C shop Gear Shop Total no.of Reportable accidents : 8

HT/Plating

Stores

Stratification

ACCORDING TO PLANT

No.of accidents

2

Other areas

1

1

1

1

0 R&D Spares Ware house Total no.of Reportable accidents : 8

Civil

Stratification

ACCORDING TO SHIFT 5 4 No.of accidents

4 3 2

2

2 1 0

0

0 I II Total no.of Reportable accidents : 8

III

GEN

OT

Total no.of Reportable accidents : 8 A

RC

H

0 M

RY

RY

UA

NU A

BE R

BE R

1

FE BR A

JA

EM

EM

0

DE C

NO V

TO BE R

0

O C

BE R

UG US T

0

SE PT EM

A

0 0

JU LY

Y

0

JU NE

A

PR IL

M

A

No.of accidents

Stratification

ACCORDING TO MONTH

4 3

3 2

2 1 1

1 0

Stratification

ACCORDING TO FAULT

4

No.of accidents

3

3

3 2 2 1 0 OPERATORS FAULT

SUPERVISORY FAULT

Total no.of Reportable accidents : 8

SYSTEM AND ENVIRONMENT FAULT

Stratification

The data has been stratified 1. According to employee category

2. According to phenomenon 3. According to body parts injured 4. According to plant a. According to unit 5. According to shift 6. According to month 7. According to fault

Stratification

Why Stratification? Stratification helps us to Zero in on to the relevant areas for investigation. Rather than investigating all the data that is present, we can narrow down the field of investigation by stratification.

Scatter diagram

Scatter diagram

Scatter diagram

Scatter diagram

It is often essential to study the relation of two corresponding variables. For example, how does the speed of driving a two wheeler affect its fuel efficiency?

Scatter diagram

To study the relation of two variables such as the speed of the two wheeler & the fuel efficiency we can use what is called a Scatter diagram.

Scatter diagram

A scatter diagram is a type of a Graph. The X & Y axes contain the the two variables.

Speed of the two wheeler

Scatter diagram

Based on the data available, dots are marked on the graph & the distribution of the dots is observed.

.. .. ... .. Speed of the two wheeler

How to read scatter diagrams You can grasp the correlation between pairs of data just by looking at the shape of a scatter diagram.

Scatter diagram

5 examples are given

350 300 250 200 150 100 50 0

35 30 25 20 15 10 5 0 0

5

10

15

20

Positive correlation

0

100

200

300

400

Negative correlation

Scatter diagram

500

40

400

30

300

20

200

10

100

0

0

0

5

10

15

20

Positive correlation may be present

0

100

200

300

400

Negative correlation may be present

Scatter diagram

700 600 500 400 300 200 100 0 0

100

200

300

No correlation

400

Scatter diagram

The two variables we will deal with are: a) A quality – characteristic & a factor affecting it,

b) Two related quality characteristics, or c) Two factors relating to a single quality characteristic.

Let’s consider the steps in making a scatter diagram

Scatter diagram

Step 1 Collect paired data (x,y) between which you want to study the relations & arrange the data in a table. It is desirable to have at least 30 pairs of data.

Scatter diagram

Step 2 • Find the maximum & minimum values for both x & y. • Decide the scales of horizontal & vertical axes. • Make lengths of both axes become equal. • Keep the number of unit graduations between 3 to 10.

=

Scatter diagram

Step 3 Plot the data on the section paper. Step 4 Enter all necessary items. Make sure that the following items are included. a) Title of the diagram b) Time interval c) Number of pairs of data d) Title & units of each axis

Scatter diagram

Let us do an exercise! To what extent does riding speed affect braking distance? Let’s draw a Scatter diagram & find out.

Scatter diagram Data of vehicle riding speed & braking distance

Sl No.

Riding speed (km/hr)

Braking distance (feet)

1

5

2

2

10

5

3

15

8

4

20

10

5

25

13

6

30

19

7

35

23

8

40

26

9

45

30

10

50

33

11

55

37

12

60

41

13

65

47

14

70

53

15

75

59

Scatter diagram Data of vehicle riding speed & braking distance

Sl No.

Riding speed (km/hr)

Braking distance (feet)

16

80

65

17

85

70

18

90

80

19

95

86

20

100

92

21

105

101

22

110

108

23

115

117

24

120

124

25

125

131

26

130

144

27

135

151

28

140

160

29

145

167

30

150

175

Scatter diagram

Step 1

As seen in the table, we have 30 pairs of data.

Step 2

In this example, let riding speed be indicated by X (horizontal axis), & braking distance by Y (vertical axis).

Scatter diagram

We mark off • The horizontal axis in 50 (km/hr) intervals, from 0 to 200 (km/hr)

• The vertical axis in 20 (feet) intervals, from 0 to 200(feet)

Step 3 Plot the data.

Scatter diagram

200 180 160 140 120 100 80 60 40 20 0 0

50

100

150

200

Scatter diagram

Step 4 Enter the • number of samples (n = 30), • horizontal axis (Speed [km / hr]) • vertical axis (distance in feet )

• title of diagram .

Scatter diagram

Distance in feet

RIDING SPEED VS BRAKING DISTANCE 200 180 160 140 120 100 80 60 40 20 0

n=30

0

50

100 Speed in km/hr

150

200

Scatter diagram

Product:

Plastic tanks

Production method: Blow Moulding Problem: Defective tanks( Thin walls) Suspected root cause: Variation in Compressed air pressure

Scatter diagram Data of blowing air pressure & percent defective of plastic tank Date Air pressure Percent (kgf/cm2) Defective Oct-01 8.6 0.889 2 8.9 0.884 3 8.8 0.874 4 8.8 0.891 5 8.4 0.874 6 8.7 0.886 7 9.2 0.911 8 8.6 0.912 9 9.2 0.895 10 8.7 0.896 11 8.4 0.894 12 8.2 0.864 13 9.2 0.922 14 8.7 0.909 15 9.4 0.905 16 8.7 0.892 17 8.5 0.877 18 9.2 0.885 19 8.5 0.866 20 8.3 0.896 21 8.7 0.896 22 9.3 0.928 23 8.9 0.886 24 8.9 0.908 25 8.3 0.881 26 8.7 0.882 27 8.9 0.904 28 8.7 0.912 29 9.1 0.925 30 8.7 0.872

Scatter diagram

Step 1 As seen in the table, we have 30 pairs of data. Step 2

In this example, let blowing air pressure be indicated by X (horizontal axis), & percent defective by Y (vertical axis). Then,

The maximum value of X: Xmax = 9.4 (kgf/cm2) The minimum value of X : Xmin = 8.2 (kgf/cm2) The maximum value of Y: Ymax = 0.928 (%) The minimum value of Y : Ymin = 0.864 (%)

Scatter diagram

We mark off

the horizontal axis in 0.5(kgf/cm2) intervals, from 8.0 to 9.5 (kgf/cm2) and the vertical axis in0.01(%) intervals, from 0.85 to 0.93(%)

Step 3 Plot the data.

Scatter diagram

0.93 0.92 0.91 0.9 0.89 0.88 0.87 0.86 0.85 8

8.5

9

9.5

Scatter diagram Step 4 Enter the time interval of the sample number of samples (n = 30),

n=30

0.92 0.91 0.9

horizontal axis

(blowing air pressure

(Oct 1 – Oct 30)

0.93

[kgf/cm2]),

0.89 0.88

vertical axis (percent defective [%])

0.87

title of diagram .

0.86 0.85 8

8.5

9

9.5

Blowing air pressure(kgf/cm2)

Scatter diagram of Blowing Air Pressure Vs Percentage of Tanks defective

Scatter diagram

Why Scatter Diagram? As illustrated in the figure if two characteristics are correlated, & their correlation can be represented as a line or a curve: The scope of action can be determined easily.

In the diagram, Action on Sector A1 will get more significant results than on Sector A3

Scatter diagram

Let us do an exercise ! Let us say you are not satisfied with the marks that your son/daughter is scoring. So, you want him/her to study more. But does studying more really help? How to find out?

Scatter diagram

Percentage of marks

Try a scatter diagram !

90 80 70 60 50 0.5 1.0 1.5 2.0 2.5

No. of hours of study/day

There is another tool which we use as a part of all the 7 QC tools

Which is it?

Brain storming

BRAIN STORMING !

Brain storming

BRAIN STORMING Brain storming is a technique to obtain creative ideas from a group of persons in a shortest possible time on an effect.

WHY

To identify the problem - to identify the causes To find solution

- to prevent problem

Brain storming WHY BRAIN STORMING?

 TO IDENTIFY THE PROBLEM

 TO IDENTIFY CRITICAL CAUSES  TO FIND THE SOLUTION  TO PREVENT THE PROBLEM

Brain storming

BRAIN STORMING Brain storming can be conducted in two ways 1. Structured

•

Every person in a group must give an idea as their turn arises.

•

Forces even shy people to participate.

•

Creates a certain amount of pressure to contribute.

Brain storming 2. Unstructured • Group members simply give ideas as they come to mind. • Creates more relaxed atmosphere • Risks domination. Thumb rule : 5 – 15 minutes works well

Brain storming

BRAIN STORMING SESSION • Let all the members speak freely and give ideas

• Encourage wild ideas • “Quantity” rather than “Quality” ideas

• Suspend judgment on “Good” or “Bad” • Ride on another’s ideas • Never criticize other persons’ opinions

Brain storming • Never

prohibit a person from speaking • See the problem from different angles/facets • Write down all the viewpoints • List the cause/ideas

• Think of the countermeasures to eliminate the causes • Leader/facilitator needs to guide the members in generating ideas • Whenever necessary non – members can also be involved

CONCLUSION

Conclusion

Remember that the 7 QC Tools help in Problem Solving.

Problems can be solved through • Intuition / Experience • Statistical tools • Experimental research By using the QC story methodology for solving problems we are adapting a scientific approach.

Conclusion

CONCLUSION

Application of Tools QC story TOOLS Check sheet Pareto diagram Stratification Cause & effect diagram Histogram Scatter diagram Control chart,graphs DOE Test of significance Why, why analysis PM analysis

Gantt chart

Problem Observation Identification

Analysis Action

Check

Standardization

HD

Conclusion

Experience only

*

Experience & Scientific

Scientific

Medium risk

*

Less risk

* Investigative

High risk

Confirmative

Approach

Best guess