walter a shewhart

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Apparel Quality Management

Presentation onWalter A. Shewhart

National Institute of Fashion Technology, Gandhinagar

Copyright 2010: Esha Gupta || Nandini Gaur || Preksha Sharma || Saumya Mehrotra

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Walter Andrew

Shewhart

Father of Statistical

Quality Control

• Born: 18 March 1891 in New Canton Illinois, USA

• Died: 11 March 1967 in Troy Hills, New Jersey, USA

Walter A. Shewhart- The Father of Statistical Process Control

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Brief

One of the most notable contributors to modern industry is

Walter Shewhart, a quality control pioneer. He started his rise

to guru status as a Bell Telephone employee in 1918. Born in

New Canton, Illinois in 1891, Shewhart set precedents that

would be referenced for years to come in establishing the

importance of information distribution among quality

managers and production personnel.


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In 1918 Shewhart joined the Western Electric Company to assist

their engineers in improving the quality of telephone hardware.

Western Electric produced hardware for the Bell Telephone

Company, which became the American Telephone and Telegraph

Company (AT&T). The Western Electric Company manufactured

telephone equipment for them and since 1905 its major plant was

the Hawthorne Plant in Cicero, a suburb of Chicago. The company

and its factory grew rapidly with the need for telephones. By 1913

there were 14000 employees and by 1930 there were 43000. It

was one of the largest manufacturing plants in the country.

Shewart worked at Hawthorne until 1925 when he moved to the

Bell Telephone Research Laboratories where he remained until his

retirement in 1956.

Cont.Walter A. Shewhart- The Father of

Statistical Process Control

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Shewhart, Juran & Deming

While at Hawthorne, Shewhart met and influenced W

Edwards Deming who went on to champion Shewhart's

methods. Joseph Juran also worked at Hawthorne from 1924

to 1941 and was influenced by Shewhart. Shewhart, Deming,

and Juran are often considered to be the three founders of

the quality improvement movement.


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Achievements & Honors

In his obituary for the American Statistical Association, Deming wrote of

Shewhart:

“As a man, he was gentle, genteel, never ruffled, never off his dignity. He

knew disappointment and frustration, through failure of many writers in

mathematical statistics to understand his point of view.”

He was founding editor of the Wiley Series in Mathematical Statistics, a

role that he maintained for twenty years, always championing freedom

of speech and confident to publish views at variance with his own.


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His Honours included:

•Founding member, fellow and president of

the Institute of Mathematical Statistics;

•Founding member, first honorary member and

first Shewhart Medalist of the American Society

for Quality;

•Fellow and President of the American Statistical

Association;

•Fellow of the International Statistical Institute;

•Honorary fellow of the Royal Statistical Society;

•Holley medal of the American Society of

Mechanical Engineers;

•Honorary Doctor of Science, Indian Statistical

Institute, Calcutta.

Achievements & Honors


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Works

He gave concepts for following:

1. PDCA Cycle2. Statistical Process Control3. Six Sigma


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PDCA Cycle

The concept of the PDCA Cycle was originally developed by

Walter Shewhart, the pioneering statistician who developed

statistical process control in the Bell Laboratories in the US

during the 1930's. It is often referred to as `the Shewhart

Cycle'. It was taken up and promoted very effectively from the

1950s on by the famous Quality Management authority, W.

Edwards Deming, and is consequently known by many as `the

Deming Wheel'.


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PDCA Cycle-Uses

PDCA Cycle is used to coordinate your continuous improvement

efforts. It both emphasizes and demonstrates that improvement

programs must start with careful planning, must result in

effective action, and must move on again to careful planning in a

continuous cycle.

The PDCA Cycle diagram is also used in team meetings to take

stock of what stage improvement initiatives are at, and to choose

the appropriate tools to see each stage through to successful

completion.


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Plan-Do-Check-Act

Four stages of the Cycle:1. Plan to improve your operations

first by finding out what things are going wrong (that is identify the problems faced), and come up with ideas for solving these problems.

2. Do changes designed to solve the problems on a small or experimental scale first. This minimises disruption to routine activity while testing whether the changes will work or not.


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Plan-Do-Check-Act

3. Check whether the small scale or experimental changes are achieving the desired result or not. Also, continuously Check nominated key activities (regardless of any experimentation going on) to ensure that you know what the quality of the output is at all times to identify any new problems when they crop up.

4. Act to implement changes on a larger scale if the experiment is successful. This means making the changes a routine part of your activity. Also Act to involve other persons (other departments, suppliers, or customers) affected by the changes and whose cooperation you need to implement them on a larger scale, or those who may simply benefit from what you have learned (you may, of course, already have involved these people in the Do or trial stage).


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PDCA Cycle Tools and techniques which can be used to complete each stage of the

PDCA Cycle.


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Statistical Process Control (SPC)

Walter A. Shewhart defined control as follows:

"A phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to vary in the future. Here it is understood that prediction within limits means that we can state, at least approximately, the probability that the observed phenomenon will fall within the given limits."

This definition means that control is not equivalent to a complete absence of variation but rather that the system is in a state where variation is predictable within some fixed limit.


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Statistical Process Control (SPC)

Shewhart also realized that frequent process-adjustment in reaction to non-conformance actually increased variation and degraded quality. That's why he expressed the fundamental rule of statistical process control in this way:

"Variation from common-cause systems should be left to chance, but special causes of variation should be identified and eliminated."


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Basics of SPC

• Statistical Process Control (SPC)– monitoring production process to

detect and prevent poor quality• Sample

– subset of items produced to use for inspection

• Control Charts– process is within statistical control

limits

UCL

LCL


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Variability•comm

on causes

•inherent in a process

•can be eliminated only through improvements in the system

Random

•special causes

•due to identifiable factors

•can be modified through operator or management action

Non-Random


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Example

0Subgroup 5 10 15

5

6

7

8

9

10

Sam

ple

Mean

15

X=7.613

3.0SL=9.844

-3.0SL=5.383

0123456789

Sam

ple

Range

R=3.867

3.0SL=8.176

-3.0SL=0.00E+00

Xbar/R Chart for Data

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Measure1 6 4 9 6 6 9 10 6 8 10 9 9 5 8 8Measure2 6 8 10 5 7 10 6 5 7 10 10 10 8 5 11Measure3 5 3 5 5 10 9 10 7 6 9 8 5 5 9 6Measure4 7 7 7 8 5 10 5 10 7 9 10 6 8 5 12Measure5 6 9 7 5 8 12 8 5 8 10 10 5 9 9 11Sum 30 31 38 29 36 50 39 33 36 48 47 35 35 36 48xbar 6 6.2 7.6 5.8 7.2 10 7.8 6.6 7.2 9.6 9.4 7 7 7.2 9.6R 2 6 5 3 5 3 5 5 2 1 2 5 4 4 6LCLx= 5.37 UCLx= 9.86 Xdbar= 7.61LCLR= 0 UCLR= 8.16 Rbar= 3.87

Subgroup

12345678

12345678

n A2 D3 D4

2 1.88 0.00 3.27

3 1.02 0.00 2.57

4 0.73 0.00 2.28

5 0.58 0.00 2.11

6 0.48 0.00 2.00

7 0.42 0.08 1.92

8 0.37 0.14 1.86

9 0.34 0.18 1.82

Control Chart Rules

Control Chart Actions

Control Chart Factors

Chart rulesfollow industrystandards oradapt to suit

Actions to follow if Rules are breached

Standard Shewhart Control Factors, used to manuallycalculate centre linesand limits.

Table for data collection


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SPC in TQM

• Tool for identifying problems and make improvements

• Contributes to the TQM goal of continuous improvements


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Quality Measures

•a product characteristic that can be evaluated with a discrete response

•good – bad; yes - no

Attribute

•a product characteristic that is continuous and can be measured

•weight - length

Variable


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Control Charts

• A graph that establishes control limits of a process

• Control limits– upper and lower bands

of a control chart

Types of charts

Attributes

p-chart

c-chart

Variables

range (R-chart)

mean (x bar – chart)


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Where to use Control

Charts

• Process has a tendency to go out of control• Process is particularly harmful and costly if it goes out of control• Examples

– at the beginning of a process because it is a waste of time and money to begin production process with bad supplies

– before a costly or irreversible point, after which product is difficult to rework or correct

– before and after assembly or painting operations that might cover defects

– before the outgoing final product or service is delivered


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Developing Control Charts

Prepare• Choose Measurement• Determine how to collect

data, sample size, and frequency of sampling

• Set up an initial control chart

Collect data• Record data• Calculate appropriate

statistics• Plot statistics on chart


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Next Steps

Determine trial control limits• Center line (process

average)• Compute UCL, LCL

Analyze and Interpret results• Determine if in control• Eliminate out-of-control

points• Re compute control

limits as necessary


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Process Control Charts

1 2 3 4 5 6 7 8 9 10Sample number

Uppercontrol

limit

Processaverage

Lowercontrol

limit

Out of control


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Normal Distributio

n

=0 1 2 3-1-2-3

95%

99.74%


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A process is in

control if..

1. No sample points outside limits2. Most points near process average3. About equal number of points

above and below centerline4. Points appear randomly distributed


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Control Charts for Attributes

• Attribute refers to those quality characteristics that

confirm to specification or do not confirm to specification

• When an attribute does not confirm to specification, diff.

descriptive terms are used.

• A nonconformity is a departure of a quality characteristic

from its product or service not to meet the specification

requirement.


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Control Charts for Attributes

•Uses portion defective in a sample

p-charts

•Uses number of defects in an item

c-charts


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P- Charts

•P chart mainly used for the data that consist of the proportion of the

number of occurrence of an event to the total no. of

occurance(events).

•To control the fraction nonconformity in product, quality characteristic

or group of quality characteristic.

•As a fraction non conformity is the proportion of the number

nonconforming the sample or subgroup to the total no. of sample in

subgroup

•P= np/n.


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P- Charts

UCL = p + zp

LCL = p - zp

z = number of standard deviations from process averagep = sample proportion defective; an estimate of process averagep = standard deviation of sample proportion

p = p(1 - p)n


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P- Charts Example

20 samples of 100 pairs of jeans

NUMBER OF PROPORTIONSAMPLE DEFECTIVES DEFECTIVE

1 6 .062 0 .003 4 .04: : :: : :

20 18 .18200


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P- Chart Example

Cont.

UCL = p + z = 0.10 + 3p(1 - p)n

0.10(1 - 0.10)100

UCL = 0.190

LCL = 0.010

LCL = p - z = 0.10 - 3p(1 - p)n

0.10(1 - 0.10)100

= 200 / 20(100) = 0.10total defectivestotal sample observations

p =


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P- Chart Example

Cont.

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Pro

port

ion d

efe

ctiv

e

Sample number2 4 6 8 10 12 14 16 18 20

UCL = 0.190

LCL = 0.010

p = 0.10


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c- Charts

The c-Chart monitors the number of times a condition occurs, relative

to a constant sample size. In this case, a given sample can have more

than one instance of the condition, in which case we count all the

times it occurs in the sample. For our example, we would sample a set

number of transactions each month from all the transactions that

occurred, and from this sample count the total number of errors in all

the transactions. We would then track on the control chart the number

of errors in all the sampled transactions per month.


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c- Charts

UCL = c + zc

LCL = c - zc

where

c = number of defects per sample

c = c


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c- Charts cont.

Number of defects in 15 sample rooms

1 122 83 16

: :: :15 15

190

SAMPLE

c = = 12.6719015

UCL = c + zc

= 12.67 + 3 12.67= 23.35

LCL = c + zc

= 12.67 - 3 12.67= 1.99

NUMBER OF DEFECTS


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c- Charts cont.

3

6

9

12

15

18

21

24N

um

ber

of

defe

cts

Sample number

2 4 6 8 10 12 14 16

UCL = 23.35

LCL = 1.99

c = 12.67


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Control Charts for Variables

Mean chart ( x -Chart ) uses average of a sample

Range chart ( R-Chart ) uses amount of dispersion

in a sample


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X- bar chart

x = x1 + x2 + ... xk

k=

UCL = x + A2R LCL = x - A2R= =

where

x = average of sample means

=


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X- bar chart

example

OBSERVATIONS (SLIP- RING DIAMETER, CM)

SAMPLE k 1 2 3 4 5 x R

1 5.02 5.01 4.94 4.99 4.96 4.98 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 5.14 5.10 4.99 5.08 5.09 5.08 0.1510 5.01 4.98 5.08 5.07 4.99 5.03 0.10

50.09 1.15


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X- bar chart

example cont.

UCL = x + A2R = 5.01 + (0.58)(0.115) =

5.08

LCL = x - A2R = 5.01 - (0.58)(0.115) =

4.94

=

=

x = = = 5.01 cm= åxk

50.0910


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Retrieve Factor Value A2

X- bar chart

example cont.

UCL = x + A2R = 5.01 + (0.58)(0.115) = 5.08

LCL = x - A2R = 5.01 - (0.58)(0.115) = 4.94

=

=

x = = = = 5.01 cm= åxk

50.0910


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X- bar chart

example cont.

UCL = 5.08

LCL = 4.94

Mea

n

Sample number

|1

|2

|3

|4

|5

|6

|7

|8

|9

|10

5.10 –

5.08 –

5.06 –

5.04 –

5.02 –

5.00 –

4.98 –

4.96 –

4.94 –

4.92 –

x = 5.01=


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R- chart

OBSERVATIONS (SLIP-RING DIAMETER, CM)


1 5.02 5.01 4.94 4.99 4.96 4.98 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 5.14 5.10 4.99 5.08 5.09 5.08 0.1510 5.01 4.98 5.08 5.07 4.99 5.03 0.10

50.09 1.15


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R- chart

UCL = D4R LCL = D3R

R = åRk

where

R = range of each samplek = number of samples


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R- chart example

OBSERVATIONS (SLIP-RING DIAMETER, CM)


1 5.02 5.01 4.94 4.99 4.96 4.98 0.082 5.01 5.03 5.07 4.95 4.96 5.00 0.123 4.99 5.00 4.93 4.92 4.99 4.97 0.084 5.03 4.91 5.01 4.98 4.89 4.96 0.145 4.95 4.92 5.03 5.05 5.01 4.99 0.136 4.97 5.06 5.06 4.96 5.03 5.01 0.107 5.05 5.01 5.10 4.96 4.99 5.02 0.148 5.09 5.10 5.00 4.99 5.08 5.05 0.119 5.14 5.10 4.99 5.08 5.09 5.08 0.1510 5.01 4.98 5.08 5.07 4.99 5.03 0.10

50.09 1.15


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R- chart example

cont.

åRk

R = = = 0.115 1.1510

UCL = D4R = 2.11(0.115) = 0.243

LCL = D3R = 0(0.115) = 0

Retrieve Factor Values D3 and D4


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R- chart example

cont.

UCL = 0.243

LCL = 0

Ran

ge

Sample number

R = 0.115

|1

|2

|3

|4

|5

|6

|7

|8

|9

|10

0.28 –

0.24 –

0.20 –

0.16 –

0.12 –

0.08 –

0.04 –

0 –


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Performing a Pattern

Test

1 4.98 B — B2 5.00 B U C3 4.95 B D A4 4.96 B D A5 4.99 B U C6 5.01 — U C7 5.02 A U C8 5.05 A U B9 5.08 A U A

10 5.03 A D B

SAMPLE x ABOVE/BELOW UP/DOWNZONE


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Sample Test

• Attribute charts require larger sample sizes- 50 to 100 parts in a sample

• Variable charts require smaller samples- 2 to 10 parts in a sample


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Appendix

n A2 D3 D4

SAMPLE SIZE FACTOR FOR x-CHART FACTORS FOR R-CHART

2 1.88 0.00 3.273 1.02 0.00 2.574 0.73 0.00 2.285 0.58 0.00 2.116 0.48 0.00 2.007 0.42 0.08 1.928 0.37 0.14 1.869 0.44 0.18 1.82

10 0.11 0.22 1.7811 0.99 0.26 1.7412 0.77 0.28 1.7213 0.55 0.31 1.6914 0.44 0.33 1.6715 0.22 0.35 1.6516 0.11 0.36 1.6417 0.00 0.38 1.6218 0.99 0.39 1.6119 0.99 0.40 1.6120 0.88 0.41 1.59


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Selected aspects of the work of Shewhart and Deming can be compared witha common element of various six-sigma programs.

Shewhart & Six Sigma


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• Shewhart explains in the opening paragraph of Statistical Method:“Corresponding to these three steps there are three senses in which statistical control [i.e. process behavior charts] may play an important part in attaining uniformity in the quality of manufactured product: (a) as a concept of a statistical state constituting a limit to

which one may hope to go in improving the uniformity of quality;

(b) as an operation or technique of attaining uniformity; and

(c) as a judgment.”



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• The three-sigma limits of a process behavior chart characterize the potential of your process. They define what a predictable process will do, and they approximate what an unpredictable process can be made to do.



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Timeline

19941920's 1931 1940's 1943 1950 1960 1970 1980 1990 2000

Shewhart's studies into variation at Bell Telephone LabsShewhart publishes book, "Economic Control of Quality of Manufactured Product Widespread adoption of Shewhart's principles for War-time Production in the US

Ishikawa develops Ishikawa diagram and pioneers use of 7-toolsWidespread abandonment of Shewhart's principles in Post-War US.

Deming teaches Shewhart principles to JapaneseDeming develops management philosophy based on Shewhart concepts own ideas

Japanese extend Deming's teachings, develop the "Total Quality” conceptUSA starts to copy Japan, called TQC (Total Quality Control) eventually the term TQM (Total Quality Management) is used as the label.

US discovers DemingRapid spread TQM principles to US service industries

Pacific basin countries, excluding Australia commence adopting TQMWestern Europe discovers TQM

Developing countries rapidly adopting TQM

Australian services sector copies US with adoption of TQMAustralian manufacturing commences with TQM

Business Process Reengineering

Team based approaches to work gaining broad acceptance in industry

Organisational learning emerging as a key competitive issue

1996

Renewed focus on Process Management

Widespread emergence of Balanced Scorecard

6-sigma goes mainstream

2002


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Publications

• The Bell Telephone Laboratories were founded in 1925 and

Shewhart moved to them when the Laboratories opened and

worked there until his retirement in 1956.

• He expanded his interests to a broader use of statistics over this

period. During this period he published many articles papers in

the Bell System Technical Journal.

• In addition, he published Random sampling in the American

Mathematical Monthly in 1931. In 1939 he published the

important book Statistical Method from the Viewpoint of Quality

Control.


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Societies

Shewhart was a founder of the Institute of Mathematical Statistics, being elected a fellow and serving a term as vice-president in 1936 and president from 1936 to 1944. He was also a founder of the American Society for Quality Control. He was elected to a fellowship of the American Statistical Association, of the International Statistical Institute, and of the Royal Statistical Society. He was also elected to the American Association for the Advancement of Science (serving on the council during 1942-49), the Econometric Society, and the New York Academy of Science. He served a term as president of the American Statistical Association in 1945, and was awarded the Holley medal of the American Society of Mechanical Engineers in 1954. The Indian Statistical Institute in Calcutta awarded him an honorary doctorate.


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Shewhart Medal

The Shewhart Medal is awarded for technical leadership: “The

Shewhart Medal committee may designate, not more often than

once each year, that nominee, not previously so designated,

who is deemed by it to have demonstrated the most

outstanding technical leadership in the field of modern quality

control, especially through the development to its theory,

principles, and techniques...”(ASQ bylaws)

The Society made him their first honorary member in 1947 and

also made him the first to receive their Shewhart Medal.


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Shewhart’s visit to India

The ISI also played a pioneering role in starting the Statistical

Quality Control (SQC) movement in India by organising a visit of

Professor W.A. Shewhart, the father of SQC, to India in

November 1947


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References

•http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Shewhart.html

•http://mospi.nic.in/arep0003_chapter7.pdf

http://walter-a-shewart.blogspot.com/

•http://asq.org/about-asq/who-we-are/bio_shewhart.html

•http://www.jstor.org/pss/25047976

•http://www.eolss.net/ebooks/Sample%20Chapters/C02/E6-02-05-04.pdf

•http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Shewhart.html


http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Shewhart.html

http://mospi.nic.in/arep0003_chapter7.pdf

http://walter-a-shewart.blogspot.com/

http://asq.org/about-asq/who-we-are/bio_shewhart.html

http://www.jstor.org/pss/25047976

http://www.eolss.net/ebooks/Sample%20Chapters/C02/E6-02-05-04.pdf

http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Shewhart.html

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Thank You

walter a shewhart

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