kellog quality lecture

Operations/Quality © Van Mieghem

Process Quality & Improvement Module Quality & the Voice of the Customer

What is Quality? Quality Programs in practice Voice of the Customer

Process Capability and Improvement Process Capability Checking for Improvement: Quality Wireless

Control Charts & Voice of the Process Statistical Process Control (SPC) Quality Wireless (B)

Why 6-Sigma? Flyrock Tires


What is Quality?

Here is how specific brands ranked in J.D. Power and Associates' annual initial quality survey.

The study is based on responses from more than 52,000 people who bought or leased new 2003 cars and trucks.

The survey is done in the first 90 days of ownership. The figures represent the number of problems per 100 vehicles.

Source: J.D. Power 2003 Initial Quality Study

Lexus 76

Cadillac 103

Infiniti 110

Acura 111

Buick 112

Mercury 113

Porsche 117

BMW 118

Toyota 121

Jaguar 122

Honda 128

Volvo 128

Chevrolet 130

Audi 132

Mercedes-Benz

132

INDUSTRY AVERAGE

133

Oldsmobile 134

Chrysler 136

Ford 136

Dodge 137

Lincoln 139

Nissan 139

Pontiac 142

Hyundai 143

Volkswagen 143

GMC 144

Suzuki 144

Jeep 146

Subaru 146

Mazda 148

Mitsubishi 148

Saturn 158

Saab 160

Mini 166

Kia 168

Land Rover 190

Hummer 225

76

103

110

111

112

113

117

118

121

122

128

128

130

132

132

133

134

136

136

137

139

139

142

143

143

144

144

146

146

148

148

158

160

166

168

190

225

0 50 100 150 200 250

Lexus

Cadillac

Infiniti

Acura

Buick

Mercury

Porsche

BMW

Toyota

Jaguar

Honda

Volvo

Chevrolet

Audi

Mercedes-Benz

INDUSTRY AVERAGE

Oldsmobile

Chrysler

Ford

Dodge

Lincoln

Nissan

Pontiac

Hyundai

Volkswagen

GMC

Suzuki

Jeep

Subaru

Mazda

Mitsubishi

Saturn

Saab

Mini

Kia

Land Rover

Hummer


8 Dimensions of Quality

Performance Features Serviceability Aesthetics Perceived Quality Reliability Conformance Durability

Q of design

Q of process conformance to design = process capability


Quality in Practice:1. Elements of TQM

Management by fact Cross-functional (process) approach Culture and leadership

– Customer focus– Employee focus– High performance focus

Continuous improvement Benchmarking

External alliances - the value chain

Source: Eitan Zemel


www.quality.nist.gov 1 Leadership 110 2 Strategic Planning 80

– Strategy Development Process 3 Customer and Market Focus 80 4 Information and Analysis 80 5 Human Resource Development and Management 100 6 Process Management 100

– Product and Service Processes – Support Processes – Supplier and Partnering Processes

7 Business Results 450• TOTAL POINTS 1000

Quality in Practice:2. Malcolm Baldrige National Quality Award


2002 Motorola Commercial, Government & Industrial Solutions

Sector (Manufacturing)SSM Health Care (Health care)Branch-Smith Printing Division (Small business)

2001Clarke American Checks, Inc., San Antonio, Texas

(manufacturing); Pal's Sudden Service, Kingsport, Tenn. (small business); Chugach School District, Anchorage, Alaska (education); Pearl River School District, Pearl River, N.Y. (education); University of Wisconsin-Stout, Menomonie, Wis. (education).

2000 Dana Corporation-Spicer Driveshaft Division, Toledo, Ohio

(manufacturing)KARLEE Company, Inc., Garland, Texas (manufacturing)Operations Management International, Inc., Greenwood

Village, Colo. (service)Los Alamos National Bank, Los Alamos, N.M. (small

business).

1999 STMicroelectronics, Inc. - Region Americas (mfg) BI (service)The Ritz-Carlton Hotel Company, L.L.C. (service)Sunny Fresh Foods (small business)

1998 Boeing Airlift and Tanker Programs (mfg)Solar Turbines Incorporated (mfg)Texas Nameplate Company, Inc. (small business)

19973M Dental Products Division (mfg)Merrill Lynch Credit Corporation (service)Solectron Corporation (mfg)Xerox Business Services (service)

1996ADAC Laboratories (mfg)Custom Research Inc. (small business)Dana Commercial Credit Corp. (service)Trident Precision Manufacturing, Inc. (small business)

Malcolm Baldridge Award Winners

Malcolm Baldrige National Quality Award Recipients' Stock Outperforms S&P 500

For the past seven years, the Commerce Department's National Institute of Standards and Technology has compared winners of the Malcolm Baldrige National Quality Award to the Standard & Poor's 500.

The Baldrige group consistently has outperformed the S&P 500; this year the Baldrige group beat the S&P 500 by 4.4 to 1.


Quality in Practice:3. ISO 9000 and 4. ?

Series of standards agreed upon by the International Organization for Standardization (ISO): (http://www.iso.ch/iso/en/iso9000-14000/iso9000/iso9000index.html)

Adopted in 1987

More than 100 countries

A prerequisite for global competition?

ISO 9000: “document what you do and then do as you documented.”

– Most companies providing service strive for ISO9002, while mfg companies that do design go for 9001

– The familiar three standard (below) have now been integrated into ISO9001:2000.

Design Procurement Production Final test Installation Servicing

ISO 9003

ISO 9002

ISO 9001


Costs of Quality

Cost of Conformance

– Cost of Appraisal

– Cost of Prevention

Cost of Non-Conformance

– Cost of Internal Failure

– Cost of External Failure 100:1

10:1

1:1

ProductDesign Process

Design

Production

ImproveProduct

Quality LeverBenefits of Building Q in Early

Low VisibilityReward

High VisibilityReward

Time


Components of Quality

Voice of the customer

– Customer Needs

– Quality of Design

Voice of the process

– Quality of Conformance

– Process Capability

Process Control and Improvement


Voice of the Customer: Linking Customer Needs to Business Processes

Business Process Customer Need Internal Metric

Overall Quality

Product (30%)

Sales (30%)

Installation (10%)

Repair (15%)

Billing (15%)

Reliability (40 %) % Repair Call

Easy to Use (20%) % Calls for Help

Features/Functions (40%) Function Performance Test

Knowledge (30%) Supervisor Observations

Response (25%) % Proposals Mad on Time

Follow-Up (10%) % Follow-Up Made

Delivery Interval (30%) Average Order Interval

Does Not Break (25%) % Repair Reports

Installed When Promised % Installed on Due Date

No Repeat Trouble (30%) % Repeat Reports

Fixed Fast (25%) Average Speed of Repair

Kept Informed (10%) % Customers Informed

Accuracy, No Surprise (45%) % Billing Inquiries

Response on First Call (35%) % Respolved First Call

Easy to Understand (10%) % Billing InquiriesSource: Kordupleski et al., CMR ‘93.


Voice of the Customer: Quality Function Deployment

What do customers want? Are all preferences equally important? Will delivering perceived needs deliver a competitive

advantage? How can we change the product? How do engineering characteristics influence customer

perceived quality? How does one engineering attribute affect another? What are the appropriate targets for the engineering

characteristics?


House of Quality

Source: Hauser and Clausing 1988

Customer Requirements

Importance to Cust.

Easy to close

Stays open on a hill

Easy to open

Doesn’t leak in rain

No road noise

Importance weighting

Engineering Characteristics

Ene

rgy

need

ed

to c

lose

doo

r

Che

ck f

orce

on

leve

l gro

und

Ene

rgy

need

ed

to o

pen

door

Wat

er r

esis

tanc

e

10 6 6 9 2 3

7

5

6

3

2

X

X

X

X

X

Correlation:

Strong positive

Positive

NegativeStrong negative

X*

Competitive evaluation

X = OursA = Comp. AB = Comp. B(5 is best)

1 2 3 4 5

X AB

X AB

XAB

A X B

X A B

Relationships:

Strong = 9

Medium = 3

Small = 1Target values

Red

uce

ener

gy

leve

l to

7.5

ft/l

b

Red

uce

forc

eto

9 lb

.

Red

uce

ener

gy to

7.5

ft/

lb.

Mai

ntai

ncu

rren

t lev

el

Technical evaluation(5 is best)

5

4321

B

A

X

BA

X B

A

X

B

X

A

BXABA

X

Doo

r se

al

resi

stan

ce

Acc

oust

. Tra

ns.

Win

dow

Mai

ntai

ncu

rren

t lev

el

Mai

ntai

ncu

rren

t lev

el

X


Linked Houses From Customer To Manufacturing

EngineeringCharacteristics

PartsCharacteristics

Key ProcessCharacteristics

ProductionCharacteristics

House ofQuality

PartsDeployment

ProcessPlanning

ProductionPlanning

I II III IV

Eng

inee

ring

Cha

ract

eris

tics

Par

tsC

hara

cter

isti

cs

Key

Pro

cess

Cha

ract

eris

tics

Cus

tom

er A

ttri

bute

s


Benefits of QFD

Startup and Pre-production costs at Toyota Auto Body

Japanese auto maker with QFD made fewer changes than US company without QFD

time20 - 24months

90% of total Japanese changes complete

Job # 1

Japan

US

Design Changes

14 - 17months

1 - 3months

1 - 3months

Before QFD

After QFD(39% of preQFD costs)

tJob # 1

Source: Hauser and Clausing 1988


More New Product Development Tools

Value analysis / Value engineering

Design for manufacturability

Robust design


Value Analysis/Value Engineering

Achieve equivalent or better performance at a lower cost while maintaining all functional requirements defined by the customer

– Does the item have any design features that are not necessary?– Can two or more parts be combined into one?– How can we cut down the weight?– Are there nonstandard parts that can be eliminated?


Robust Quality: Taguchi’s View of Cost of Variability

Traditional View Taguchi’s View

Non-conformance to design cost

$$$

0

LowerTolerance

DesignSpec

UpperTolerance

Actual value Lower

ToleranceDesignSpec

UpperTolerance


Quality & the Voice of the Customer: Key Learning Objectives

Elements of TQM / Baldridge / ISO 9000

Costs of Quality

Components of Quality

Voice of the Customer– Linking business processes to customer needs– Product Design Methodologies:

Convert customer needs to product and process specifications: QFD Value Engineering


Operations Management:







Process Capability

Percent defective– Proportion of output that does not meet customer specifications

Sigma-capability– Number of standard deviations from the mean of the process output to the

closest specification limit.


Quality Wireless (A): Capability

Distribution of Average Daily Hold Time for 2003-04

0

2

4

6

8

10

12

14

16

18

20

39 43 47 51 55 59 63 67 71 75 79 83 87 91 95 99 103

107

111

115

119

123

127

131

135

139

143

147

151

155

159

163

Average Daily Hold Time

Nu

mb

er o

f D

ays

Out of SpecsWithin Specs


Quality Wireless (A): Capability

Proportion of days within specification in 2003-04 = 491/731 = 0.672

The call center had a mean hold time of 99.67 with a standard deviation of 24.24. With a specification of 110 seconds or less,

σ-capability of call center = (110 – 99.67)/24.24

= 0.426

The call center is a 0.426-sigma process.

Expected fraction of days within specifications from a 0.426-sigma process = NORMSDIST(0.426) = 0.665


What is Process Improvement?

Product/ServiceOutput Measure

“Defects”=Service is

unacceptable to customers

Critical customer requirementBeforeAfter


Continuous Improvement:PDCA Cycle (Deming Wheel)

Institutionalize the change or abandon or do it again.

Execute the change.Study the results; did it work?

1. Plan

2. Do3. Check

4. Act

Plan a change aimed at improvement.


Performance in April 2005: Mean = 79.50, Standard deviation = 16.86 What is the probability of observing such a sample if performance has not

improved relative to 2003-04?– Mean hold in 2003-04 = 99.67– Standard deviation = 24.24– Given that April 2005 had 30 days, we need to consider distribution of samples of

size 30. The standard deviation of sample means = 24.24/√30 = 4.43– Probability of observing a sample of size 30 with mean 79.50 or less =

NORMDIST(79.50, 99.67, 4.43, 1) = 2.64E-06

Quality Wireless (A): Checking for Improvement


Average hold time from September 1-10 =86.6 seconds– Ray yells at supervisors

Performance improves from September 11-20 to an average hold of 74.4 seconds

What do you think of Ray’s management style?

Has Process Performance Changed? Quality Wireless (B)


Performance Measurement Implications: Inventory Manager and Weight watchers

J F M A M J J A S O N

WIP

Award Given

Manager repents and kicks...

J F M A M J J A S O N D J F

WIP

J F M A M J J A S O N D J F M A M J

WIP

.. and concludes that kick ... mgt works !?

month

month

month

Weight watchersrule: measure only weekly…


Statistical Process Control (SPC):Sources of Variability and Conceptual Framework

Every process has variation that comes from two sources:

1. Inherent (common cause)

2. External (assignable cause)

Objective: Identify inherent variability and eliminate external variability.

First get the process “in control” by eliminating external variability. (A process is “in control” if it has only inherent variability.)

To improve the system, attack common causes (methods, people, material, machines). This is the role of management.


Statistical Process Control: Control Charts

m - 3Lower Control Limit

hypothesized (sampled)

process output

mean m

m + 3Upper Control Limit

F(z)99.74

%

Signal that a special cause has occurred

t

Control Improvement


Various Patterns in Control Charts

Pattern Description Possible Causes

Normal Random Variation

Lack of Stability Assignable (or special) causes (e.g. tool,material, operator, overcontrol

Cumulative trend Tool Wear

Cyclical Different work shifts, voltage fluctuations, seasonal effects


Calibration versus Quality:Sharp shooters


SPC – Quality Wireless (B)

After the improvements, daily hold time has an average of 79.50 and a standard deviation of 16.86.

Since we are considering samples of size 10 (10 days), we need to consider the distribution of sample means. Sample means have an average of 79.50 and a standard deviation of 16.86/√10 = 5.33.

Probability of observing 86.6 or higher even if process is in control = 1-NORMDIST(86.6, 79.50, 5.33, 1) = 0.0915


SPC – Quality Wireless (B)

Probability of observing 74.4 or lower even if process is in control = NORMDIST(74.4, 79.50, 5.33, 1) = 0.1693

What we need is a hypothesis test each time we observe a sample – Does the sample belong to the in-control population or not?


SPC – Setting Control Limits

Upper Control Limit = UCL = Mean + 3σXbar

Lower Control Limit = LCL = Mean - 3σXbar

In the case of Quality Wireless– UCL = 79.50 + 3×5.33 = 95.49– LCL = 79.50 - 3×5.33 = 63.51

The process was in control when samples with means of 86.6 and 74.4 were observed.


Control Charts & Voice of the Process:Key Learning Objectives

The role of variability in evaluating performance

A process that is – in control has only inherent (from common cause) variation

There is a known distribution for the (sampled) output with mean m and std. dev. output typically within the control limits m 3

– out of control has variation from an assignable cause Observations outside the control limits are so unlikely if process is in control that it is

likely that process is out of control

Pareto analysis to identify key causes of error

SPC framework for process control and improvement

SPC Tools:– Viewing quality data as a run chart to infer performance over time. – Constructing control charts . – Identifying whether a process is in or out of control. – Then link this to improvement:

Constructing a Pareto diagram to prioritize areas for improvement.







6-Sigma: What and Why? Flyrock Tires


99.9% Suppliers

At least 20,000 wrong prescriptions per year

More than 15,000 newborns dropped by doctors or nurses

No electricity, water or heat for 8.6 hours each year

No telephone service or TV transmission for nearly 10 minutes each week

Two short (or long) landings at O’Hare each week


Why 6-Sigma?

2 sigma:– 69.1% of products and/or services meet customer requirements with 308,538 defects per million

opportunities.

4 sigma:– 99.4% of products and/or services meet customer requirements ... but there are still 6,210

defects per million opportunities.

6 sigma:– 99.9997% (“5 nines”) – Close to flaw-free for most businesses, with just 3.4 failures per

million opportunities (e.g. products, services or transactions).


Magnitude of Difference Between Sigma Levels

# sigma’s Area Spelling Time Distance

1 Floor space of Soldier Field

170 misspelled words per page in a book

31.75 years per century

Here to the moon

2 Floor space of large supermarket

25 misspelled words per page in a book

0.45 years per century

1.5 times around the world

3 Floor space of small hardware store

1.5 misspelled words per page in a book

3.5 months per century

London to New York

4 Your living room 1 misspelled word per 30 pages

2.5 days per century

Basel to Zurich

5 The button of your telephone

1 misspelled word in a set of encyclopedias

30 minutes per century

Leverone to Norris

6 diamond 1 misspelled word in a library

6 seconds per century

Four steps from your chair


Why 6-Sigma? Impact of # of parts/stages in a process

Impact of mean shift– P(output outside specs) vs. P(detection of mean shift)

Probability that process/product meets specs3 -sigma 4 - sigma 5 - sigma 6 - sigma

# of steps/parts1 93.3% 99.4% 99.98% 99.9997%

10 50.1% 94.0% 99.8% 99.997%50 3.2% 73.2% 98.8% 99.98%

100 0.1% 53.6% 97.7% 99.97%144 0.00% 40.8% 96.7% 99.95%369 10.0% 91.8% 99.9%740 1.0% 84.2% 99.7%

1044 0.1% 78.4% 99.6%1590 0.00% 69.1% 99.5%

19581 1.0% 93.6%42559 0.00% 86.5%

100000 71.2%1000000 3.3%

0.001%

0.01%

0.1%

1.0%

10.0%

100.0%

1 10 100 1000 10000 100000 1000000

# steps/components

Probability that process/productmeets specs

3 -sigma

4 - sigma

5 - sigma

6 - sigma

* These numbers allow a mean shift of 1.5 .


Relationship Between Sigma Capability, Proportion Defects, and Cpk

LSL m USL

z z

z Sigma Process Probability of meeting specs Defects per million Cpk

z 2 F (z ) - 1 2 - 2 F (z ) z / 31 0.682689480482 317310.519518 0.332 0.954499875928 45500.124072 0.673 0.997300065554 2699.934446 1.004 0.999936627931 63.372069 1.335 0.999999425790 0.574210 1.676 0.999999998020 0.001980 2.007 0.999999999997 0.000003 2.33

Cm m

pk = minLSL

3 ,

USL -

3

* These are “raw” numbers; excluding mean shifts.


Six Sigma: Core methodology

Six Sigma was introduced by Motorola in 1986 as a new method for standardizing the way defects are counted (in response to increasing complaints from the field sales force about warranty claims)

The problem-solving framework and work-breakdown structure can be easily remembered using the acronym DMAIC:1. Define the problem to determine what needs to be improved

2. Measure the current state against the desired state

3. Analyze the root causes of the business gap

4. Improve by team brainstorming, selecting and implementing the best solutions

5. Control the long-term sustainability of the improvement by establishing monitoring mechanisms, accountabilities and work tools


Six Sigma: From original defects control to overall business improvement methodology

AlliedSignal (now Honeywell) and GE successfully applied and popularized Motorola’s Six Sigma methodology as part of leadership development, going far beyond counting defects.

Now, six sigma is an overall high-performance system that executes business strategy using four steps:1. Align executives to the right objectives and targets using a balanced scorecard

2. Mobilize improvement teams using DMAIC

3. Accelerate results by action learning and integrating all teams so the cumulative impact on the organization is “accelerated.”

4. Govern the process through visible executive sponsorship, review, and sharing best practices with other parts of the organization


Quality Performance at Flyrock:1. How well do we meet customer specs?

At the extruder, the rubber for the AX-527 tires had thickness specifications of 400 10 ‘thou’ (.001’’). Susan and her staff had analyzed many samples of output from the extruder and determined that if the extruder settings were accurate, the output produced by the extruder had a thickness that was normally distributed with a mean of 400 thou and a standard deviation of 4 thou.

If the setting is accurate, what proportion of the rubber extruded will be within specifications?


Quality Performance at Flyrock:

1. How well do we meet customer specs? Definition of Process Capability

Link: Voice of the Customer with Voice of the Process

Process Capability

= How well is process capable of meeting customer specifications?

Equivalent Measures of Process Capability:

1. Proportion of output flow units meeting customer specs– Example: at Flyrock:

2. Sigma-capability = the number of std. deviations to the closest specification limit

– Example: the Sigma capability of Flyrock’s extrusion process =

LSLmmUSL

,min


Quality Performance at Flyrock: 2. Statistical Process Control

Susan has asked operators to take a sample of 10 sheets of rubber each hour from the extruder and measure the thickness of each sheet. Based on the average thickness of this sample, operators will decide whether the extrusion process is in control or not.

Given that Susan plans 3-sigma control limits, what upper and lower control limits should she specify to the operators?

– UCL =

– LCL =


Quality Performance at Flyrock: Quality graphs for current process

A: Meeting Customer Specs

Sigma capability = Prob(Meeting specs) =

B: Keeping Process in Control

Prob(sample mean within control band) = Prob(investigate) =

UCL

396.2

400

403.8

X

Sample Mean

time

385 390 395 400 405 410 415

= 4

LSL USL

LCL

26.110

4

X

01

23

-3-2

-1


Quality Performance at Flyrock: 3. Impact and Detection of Mean Shift

If a bearing is worn out, the extruder produces a mean thickness of 403 thou when the setting is 400 thou. Under this condition, what proportion of produced sheets will be defective?

Assuming the earlier control limits, what is the probability that a sample taken from the extruder with the worn bearings will be out of control?

On average, how many hours are likely to go by before the worn bearing is detected?


Quality Performance at Flyrock: current process … but worn bearing (mean shift)A: Meeting Customer Specs

Prob(Meeting specs) =



UCL

396.2

400

403.8

X

time

385 390 395 400 405 410 415

= 4

LSL USL

LCL

26.110

4

X

= 4

403

01

23

-3-2

-1

403

Sample Mean


Quality Performance at Flyrock: 4. Improving Process Capability

What if extrusion is to become a 6-Sigma process?– Target mean =– Target standard deviation =

Process improvement has resulted in the extrusion process having a mean of 400 thou and a standard deviation of 1.67 thou. What should the new control limits be?

– UCL =

– LCL =

What is the proportion of defectives produced?


Quality Performance at Flyrock: Quality graphs for improved 6 process


Sigma capability = Prob(Meeting specs) =



UCL

396.2

400

403.8

X

Sample Mean

time

385 390 395 400 405 410 415

LSL USL

01

23

-3-2

-1 53.010

67.1

X

401.6

LCL398.4


Quality Performance at Flyrock: 5. Benefits of a 6Sigma process

Return to the case of the worn bearing where extrusion produces a mean thickness of 403 thou when the setting is 400 thou. Under this condition, what proportion of produced sheets will be defective (for the 6-sigma extrusion process)?

Assuming the new control limits, what is the probability that a sample taken from the extruder with the worn bearings will be out of control?

On average, how many hours are likely to go by before the worn bearing is detected?


Flyrock: Improved 6sigma process… but with worn bearing (mean shift)


Prob(Meeting specs) =



UCL

400

X

Sample Mean

time

385 390 395 400 405 410 415

LSL USL

01

23

-3-2

-1 53.010

67.1

X

401.6

LCL398.4

403

01

23

-3-2

-1

403


Key Learning Objectives: Six Sigma

Specification limits: Voice of the customer– Use output (population) distribution with mean m, stdev – This is where we determine sigma-capability

Control limits used to verify if process is in control (internal), i.e., is maintaining capability: Voice of the process

– Use sample mean distribution with mean m, stdev

Six Sigma and Process capability are measures of the quality delivered (external): links VoP with VoC

Improving capability may require variability reduction and/or mean shift– Typically, customer specs are fixed and cannot be relaxed

Reducing number of stages/parts improves capability

nX

kellog quality lecture

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