chapter 15 quality management to accompany russell and taylor, operations management, 4th edition, ...

Chapter 15

Quality Quality ManagementManagement

To Accompany Russell and Taylor, Operations Management, 4th Edition, 2003 Prentice-Hall, Inc. All rights reserved.

2

Definition:Total Quality Management

• Total Quality Management (TQ, QM or TQM) and Six Sigma (6) are sweeping “culture change” efforts to position a company for greater customer satisfaction, profitability and competitiveness.

• TQ may be defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer.

• We often think of features when we think of the quality of a product or service; TQ is about conformance quality, not features.

• Meeting Our Customer’s Requirements

• Doing Things Right the First Time; Freedom from Failure (Defects)

• Consistency (Reduction in Variation)

• Continuous Improvement

• Quality in Everything We Do

Total Quality Is…

33

A Quality Management System Is…

• A belief in the employee’s ability to solve problems

• A belief that people doing the work are best able to improve it

• A belief that everyone is responsible for quality

44

Elements for Success

• Management Support• Mission Statement• Proper Planning• Customer and Bottom Line Focus• Measurement• Empowerment• Teamwork/Effective Meetings• Continuous Process Improvement• Dedicated Resources

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Measurement

Measu

rem

en

t Measu

rem

ent

Measurement

Empowerment/

Shared Leadership

Process Improvement/

Problem Solving

Team Management

Customer Satisfaction

Business Results

The Continuous Improvement Process

. . .

66

Modern History of Quality Management• Frederick W. Taylor wrote Principles of Scientific Management in 1911.

• Walter A. Shewhart used statistics in quality control and inspection, and showed that productivity improves when variation is reduced (1924); wrote Economic Control of Manufactured Product in 1931.

• W. Edwards Deming and Joseph M. Juran, students of Shewhart, went to Japan in 1950; began transformation from “shoddy” to “world class” goods.

• In 1960, Dr. K. Ishikawa formalized “quality circles” - the use of small groups to eliminate variation and improve processes.

• In the late ‘70’s and early ‘80’s:– Deming returned from Japan to write Out of the Crisis,

and began his famous 4-day seminars in the United States– Phil Crosby wrote Quality is Free– NBC ran “If Japan can do it, why can’t we?” – Motorola began 6 Sigma

77

Deming’s 14 Points

1. Create constancy of purpose for improvement

2. Adopt a new philosophy

3. Cease dependence on mass inspection

4. Do not award business on price alone

5. Work continually on the system of production and service

6. Institute modern methods of training

7. Institute modern methods of supervision of workers

8. Drive out fear

9. Break down barriers between departments

10. Eliminate slogans, exhortations, and targets for the work force

11. Eliminate numerical quotas

12. Remove barriers preventing pride of workmanship

13. Institute a vigorous program of education and retraining

14. Take action to accomplish the transformation

History of Quality Management

88

History of Quality Management

Deming’s Concept of “Profound Knowledge”

Understanding (and appreciation) of Systems

- optimizing sub-systems sub-optimizes the total system

- the majority of defects come from systems, the responsibility of

management (e.g., machines not in good order, defective material, etc. Knowledge of Statistics (variation, capability, uncertainty in data, etc.)

- to identify where problems are, and point managers and workers

toward solutions Knowledge of Psychology (Motivation)

- people are afraid of failing and not being recognized,

so they fear how data will be used against them Theory of Knowledge

- understanding that management in any form is a prediction, and is

based on assumptions

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According to Dr. Joseph M. Juran (1991):

“On the assembly line at the Ford Motor Company in 1923, most of the workers producing Model T’s were immigrants and could not speak English. Many were also illiterate. Workers learned their trade by modeling the actions of other workers. They were unable to plan, problem-solve, and make decisions. As a result, the Taylor scientific school of management flourished, and MBAs and industrial engineers were invented to do this work. Today, however, the workforce is educated. Workers know what is needed to improve their jobs, and companies that do not tap into this significant source of knowledge will truly be at a competitive disadvantage.”

History of Total Quality

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According to Phil Crosby, Quality is . . .

An attitude:- Zero Defects- Continuous Improvement

A measurement:- Price of Conformance, plus- Price of Nonconformance (defects)

History of Total Quality

1111

From Motivation through fear and loyalty

To Motivation through shared vision

Attitude: “It’s their problem” Ownership of every problem affecting the customer

Attitude: “the way we’ve always done it”

Continuous improvement

Decisions based on assumptions/ judgment calls

Decisions based on data and facts

Everything begins and ends with management

Everything begins and ends with customers

Crisis management and recovery Doing it right the first time

Choosing participative OR scientific management

Choosing scientific AND participative management

TQ: Transforming an Organization

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Statistical Process Control

• Take periodic samples from processTake periodic samples from process

• Plot sample points on control chartPlot sample points on control chart

• Determine if process Determine if process is within limitsis within limits

• Prevent quality Prevent quality problemsproblems

UCLUCL

LCLLCL

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Variation

Common CausesCommon CausesVariation inherent in a processVariation inherent in a processCan be eliminated only through Can be eliminated only through

improvements in the systemimprovements in the system

Special CausesSpecial CausesVariation due to identifiable factorsVariation due to identifiable factorsCan be modified through operator or Can be modified through operator or

management actionmanagement action

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Types of Data

Attribute dataAttribute data Product characteristic Product characteristic

evaluated with a discrete choiceevaluated with a discrete choice Good/bad, yes/noGood/bad, yes/no

Variable dataVariable data Product characteristic that Product characteristic that

can be measuredcan be measured Length, size, weight, height, Length, size, weight, height,

time, velocitytime, velocity

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SPC Applied to Services

Nature of defect is different in Nature of defect is different in servicesservices

Service defect is a failure to meet Service defect is a failure to meet customer requirementscustomer requirements

Monitor times, customer Monitor times, customer satisfactionsatisfaction

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Service Quality Examples

Hospitals Hospitals Timeliness, responsiveness, Timeliness, responsiveness,

accuracy of lab testsaccuracy of lab testsGrocery StoresGrocery Stores

Check-out time, stocking, cleanlinessCheck-out time, stocking, cleanlinessAirlinesAirlines

Luggage handling, waiting times, Luggage handling, waiting times, courtesycourtesy

Fast food restaurantsFast food restaurantsWaiting times, food quality, Waiting times, food quality,

cleanliness, employee courtesycleanliness, employee courtesy1717

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Service Quality Examples

Catalog-order companiesCatalog-order companiesOrder accuracy, operator Order accuracy, operator

knowledge and courtesy, knowledge and courtesy, packaging, delivery time, packaging, delivery time, phone order waiting timephone order waiting time

Insurance companiesInsurance companiesBilling accuracy, timeliness of claims Billing accuracy, timeliness of claims

processing, agent availability and processing, agent availability and response timeresponse time

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Control Charts

Graph establishing process control Graph establishing process control limitslimits

Charts for variablesCharts for variablesMean (x-bar), Range (R)Mean (x-bar), Range (R)

Chart for attributesChart for attributesP ChartP Chart

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Process Control Chart

11 22 33 44 55 66 77 88 99 1010

Sample numberSample number

UpperUppercontrolcontrol

limitlimit

ProcessProcessaverageaverage

LowerLowercontrolcontrol

limitlimit

Out of controlOut of control

Figure 15.1Figure 15.12020

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A Process is In Control if

1.1. No sample points outside limitsNo sample points outside limits

2.2. Most points near process averageMost points near process average

3.3. About equal number of points About equal number of points above & below centerlineabove & below centerline

4.4. Points appear randomly Points appear randomly distributeddistributed

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Development of Control Chart

Based on in-control dataBased on in-control data

If non-random causes present, If non-random causes present, find the special cause and find the special cause and discard datadiscard data

Correct control chart limitsCorrect control chart limits

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Control Chart for Attributes

p Chartsp ChartsCalculate percent defectives in sampleCalculate percent defectives in sample

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p-Chart

UCL = UCL = pp + + zzpp

LCL = LCL = pp - - zzppwherewhere

zz == the number of standard deviations the number of standard deviations from the process averagefrom the process averagepp == the sample proportion defective; an the sample proportion defective; an estimate of the process averageestimate of the process averagepp == the standard deviation of the the standard deviation of the

sample proportionsample proportion

pp = = pp(1 - (1 - pp))

nn

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The Normal Distribution

=0=0 11 22 33-1-1-2-2-3-3

95%

99.74%

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Control Chart Z Values

Smaller Z values make more Smaller Z values make more sensitive chartssensitive charts

Z = 3.00 is standardZ = 3.00 is standardCompromise between sensitivity Compromise between sensitivity

and errorsand errors

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p-Chart Example20 samples of 100 pairs of jeans20 samples of 100 pairs of jeans

NUMBER OFNUMBER OF PROPORTIONPROPORTIONSAMPLESAMPLE DEFECTIVESDEFECTIVES DEFECTIVEDEFECTIVE

11 66 .06.06

22 00 .00.00

33 44 .04.04

:: :: ::

:: :: ::

2020 1818 .18.18

200200

Example 15.1Example 15.12727

28


NUMBER OFNUMBER OF PROPORTIONPROPORTIONSAMPLESAMPLE DEFECTIVESDEFECTIVES DEFECTIVEDEFECTIVE

11 66 .06.06

22 00 .00.00

33 44 .04.04

:: :: ::

:: :: ::

2020 1818 .18.18

200200


p =

= 200 / 20(100)= 0.10

total defectives

total sample observations

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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

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30

0.020.02

0.040.04

0.060.06

0.080.08

0.100.10

0.120.12

0.140.14

0.160.16

0.180.18

0.200.20

Pro

po

rtio

n d

efec

tive

Pro

po

rtio

n d

efec

tive

Sample numberSample number22 44 66 88 1010 1212 1414 1616 1818 2020

UCL = 0.190

LCL = 0.010

p = 0.10

p-Chart

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Control Charts for Variables

Mean chart ( x -Chart )Mean chart ( x -Chart )Uses average of a sampleUses average of a sample

Range chart ( R-Chart )Range chart ( R-Chart )Uses amount of dispersion in Uses amount of dispersion in

a samplea sample

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Range ( R ) Chart

UCL = UCL = DD44RR LCL = LCL = DD33RR

RR = = RRkk

wherewhere

RR = range of each sample= range of each samplekk = number of samples= number of samples

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Range ( R- ) Chart

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

Table 15.1Table 15.1 3333

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R-Chart ExampleOBSERVATIONS (SLIP-RING DIAMETER, CM)OBSERVATIONS (SLIP-RING DIAMETER, CM)

SAMPLE SAMPLE kk 11 22 33 44 55 xx RR

11 5.025.02 5.015.01 4.944.94 4.994.99 4.964.96 4.984.98 0.080.0822 5.015.01 5.035.03 5.075.07 4.954.95 4.964.96 5.005.00 0.120.1233 4.994.99 5.005.00 4.934.93 4.924.92 4.994.99 4.974.97 0.080.0844 5.035.03 4.914.91 5.015.01 4.984.98 4.894.89 4.964.96 0.140.1455 4.954.95 4.924.92 5.035.03 5.055.05 5.015.01 4.994.99 0.130.1366 4.974.97 5.065.06 5.065.06 4.964.96 5.035.03 5.015.01 0.100.1077 5.055.05 5.015.01 5.105.10 4.964.96 4.994.99 5.025.02 0.140.1488 5.095.09 5.105.10 5.005.00 4.994.99 5.085.08 5.055.05 0.110.1199 5.145.14 5.105.10 4.994.99 5.085.08 5.095.09 5.085.08 0.150.15

1010 5.015.01 4.984.98 5.085.08 5.075.07 4.994.99 5.035.03 0.100.10

50.0950.09 1.151.15


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R-Chart Example


Rk

R = = = 0.115 1.1510

UCL = D4R = 2.11(0.115) = 0.243

LCL = D3R = 0(0.115) = 0

UCL = 0.243

LCL = 0

Ra

ng

e

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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x-Chart Calculations

xx = = xx11 + + xx22 + ... + ... xxkk

kk==

UCL = UCL = xx + + AA22RR LCL = LCL = xx - - AA22RR== ==

wherewhere

xx = the average of the sample means= the average of the sample means==

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x-Chart Example

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= x

k50.09

10

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x-Chart Example

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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Using x- and R-Charts Together

Each measures the process Each measures the process differently differently

Both process average and variability Both process average and variability must be in controlmust be in control

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Sample Size Determination

Attribute control chartsAttribute control charts50 to 100 parts in a sample50 to 100 parts in a sample

Variable control chartsVariable control charts2 to 10 parts in a sample2 to 10 parts in a sample

4040

Process Capability

•Process limits (The “Voice of the Process” or The “Voice of the Data”) - based on natural (common cause) variation

•Tolerance limits (The “Voice of the Customer”) – customer requirements

•Process Capability – A measure of how “capable” the process is to meet customer requirements; compares process limits to tolerance limits

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Process Capability

Range of natural variability in processRange of natural variability in process Measured with control charts.Measured with control charts.

Process cannot meet specifications if Process cannot meet specifications if natural variability exceeds tolerancesnatural variability exceeds tolerances

3-sigma quality3-sigma quality Specifications equal the process control Specifications equal the process control

limits. limits.

6-sigma quality6-sigma quality Specifications twice as large as control Specifications twice as large as control

limitslimits4242

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Process Capability

(b) Design specifications (b) Design specifications and natural variation the and natural variation the same; process is capable same; process is capable of meeting specifications of meeting specifications most the time.most the time.

Design Design SpecificationsSpecifications

ProcessProcess

(a) Natural variation (a) Natural variation exceeds design exceeds design specifications; process specifications; process is not capable of is not capable of meeting specifications meeting specifications all the time.all the time.


ProcessProcess


44

Process Capability


(c) Design specifications (c) Design specifications greater than natural greater than natural variation; process is variation; process is capable of always capable of always conforming to conforming to specifications.specifications.


ProcessProcess

(d) Specifications greater (d) Specifications greater than natural variation, but than natural variation, but process off center; process off center; capable but some output capable but some output will not meet upper will not meet upper specification.specification.


ProcessProcess

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Process Capability Measures

Process Capability IndexProcess Capability Index

CCpkpk = minimum = minimum

xx - lower specification limit - lower specification limit

33

==

upper specification limit -upper specification limit - x x

33

==

,,

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Computing Cpk

Net weight specification = 9.0 oz Net weight specification = 9.0 oz 0.5 oz 0.5 ozProcess mean = 8.80 ozProcess mean = 8.80 ozProcess standard deviation = 0.12 ozProcess standard deviation = 0.12 oz

CCpkpk = minimum= minimum

= minimum , = 0.83= minimum , = 0.83

xx - lower specification limit - lower specification limit

33

==

upper specification limit -upper specification limit - x x

33

==,,

8.80 - 8.508.80 - 8.50

3(0.12)3(0.12)

9.50 - 8.809.50 - 8.80

3(0.12)3(0.12)Example 15.7Example 15.7

4646

Interpreting the Process Capability Index

Cpk < 1 Not Capable

Cpk > 1 Capable at 3

Cpk > 1.33 Capable at 4

Cpk > 1.67 Capable at 5

Cpk > 2 Capable at 6

4747

What is Six Sigma?

• A goal of near perfection in meeting customer requirements

• A sweeping culture change effort to position a company for greater customer satisfaction, profitability and competitiveness

• A comprehensive and flexible system for achieving, sustaining and maximizing business success; uniquely driven by close understanding of customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving and reinventing business processes

(Source:The Six Sigma Way by Pande, Neuman and Cavanagh)

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Is 99% Quality Good Enough?

• 22,000 checks will be deducted from the wrong bank accounts in the next 60 minutes.

• 20,000 incorrect drug prescriptions will be written in the next 12 months.

• 12 babies will be given to the wrong parents each day.

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Six Sigma Quality

The objective of Six Sigma quality is 3.4 defects per million opportunities!

(Number of Standard Deviations) 3 Sigma 4 Sigma 5 Sigma 6 Sigma

0.0 2700 63 0.57 0.002

0.5 6440 236 3.4 0.019

1.0 22832 1350 32 0.019

1.5 66803 6200 233 3.4

2.0 158,700 22800 1300 32

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But is Six Sigma Realistic?

·

1

11

21

31

41

3 4 5 6 7

10

1

100

1K

10K

100K

765432

(66810 ppm)· IRS – Tax Advice (phone-in)

Best in Class

(3.4 ppm)

Domestic AirlineFlight Fatality Rate

(0.43 ppm)

·(233 ppm)

AverageCompany

Purchased MaterialLot Reject Rate

Air Line Baggage Handling

Wire Transfers

Journal VouchersOrder Write-up

Payroll Processing

Doctor Prescription WritingRestaurant Bills

·······

Defe

cts

Per

Million

Op

port

un

itie

s (

DP

MO

)

SIGMA 5151

Six Sigma Improvement MethodsDMAIC vs. DMADV

Define

Measure

Analyze

Design

Validate

Improve

Control

Continuous Improvement Reengineering

5252

Six Sigma DMAIC Process

Measure

Control

Define

Analyze

Improve

Define: Define who your customers are, and what their requirements are for your products and services – Their expectations. Define your team goals, project boundaries, what you will focus on and what you won’t. Define the process you are striving to improve by mapping the process. 5353


Measure

Control

Define

Analyze

Improve

Measure: Eliminate guesswork and assumptions about what customers need and expect and how well processes are working. Collect data from many sources to determine speed in responding to customer requests, defect types and how frequently they occur, client feedback on how processes fit their needs, how clients rate us over time, etc. The data collection may suggest Charter revision.

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Measure

Control

Define

Analyze

Improve

Analyze: Grounded in the context of the customer and competitive environment, analyze is used to organize data and look for process problems and opportunities. This step helps to identify gaps between current and goal performance, prioritize opportunities to improve, identify sources of variation and root causes of problems in the process. 5555


Measure

Control

Define

Analyze

ImproveImprove: Generate both obvious and creative solutions to fix and prevent problems. Finding creative solutions by correcting root causes requires innovation, technology and discipline.

5656


Measure

Control

Define

Analyze

Improve

Control: Insure that the process improvements, once implemented, will “hold the gains” rather than revert to the same problems again. Various control tools such as statistical process control can be used. Other tools such as procedure documentation helps institutionalize the improvement.

5757

Six Sigma DMADV Process

Measure

Validate

Define

Analyze

Design

Design: Develop detailed design for new process. Determine and evaluate enabling elements. Create control and testing plan for new design. Use tools such as simulation, benchmarking, DOE, Quality Function Deployment (QFD), FMECA analysis, and cost/benefit analysis.

5858

Six Sigma DMADV Process

Measure

Validate

Define

Analyze

Design

Validate: Test detailed design with a pilot implementation. If successful, develop and execute a full-scale implementation. Tools in this step include: planning tools, flowcharts/other process management techniques, and work documentation.

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Key Learning's

• History of quality management• TQM• Six sigma• Control charts• Process capability

04/19/23

chapter 15 quality management to accompany russell and taylor, operations management, 4th edition, ...

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