d30 quality

S. Chopra/Operations/Quality 1

Operations Management:

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


8 Dimensions of Quality Performance Features Serviceability Aesthetics Perceived Quality Reliability Conformance Durability

Q of design

Q of process conformance to design = process capability


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


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

Malcolm Baldridge National Quality Award


Malcolm Baldridge Award Winners Ames Rubber Corporation (1993) Armstrong World Industries Building Products

Operations (1995) AT&T Consumer Communications Services (1994) AT&T Network Systems Group (1992) AT&T Universal Card Services (1992) Cadillac Motor Car Company (1990) Chugach School District (2001) Clarke American Checks (2001) Corning Telecommunications Products Division (1995) Dana Corporation (2000) Eastman Chemical Company (1993) Federal Express Corporation (1990) Globe Metallurgical Inc. (1988) Granite Rock Company (1992) GTE Directories Corporation (1994) IBM Rochester (1990)

Karlee Company, Inc. (2000) Los Alamos National Bank (2000) Marlow Industries (1991) Milliken & Company (1989) Motorola Inc. (1988) Operations Management International (2000) Pal’s Sudden Service (2001) Pearl River School District (2001) The Ritz-Carlton Hotel Company (1992) Solectron Corporation (1991) Texas Instruments Incorporated - Defense Systems &

Electronics Group (1992) University of Wisconsin-Stout (2001) Wainwright Industries, Inc. (1994) Wallace Co., Inc. (1990) Westinghouse Electric Corporation - Commerical Nuclear

Fuel Division (1988) Xerox Corporation - Business Products & Systems (1989) Zytec Corporation (1991)

Last Updated: May 28, 2002


ISO 9000 Series of standards agreed upon by the International Organization for

Standardization (ISO)

Adopted in 1987

More than 100 countries

A prerequisite for global competition?

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

Source: Adapted from Chase & Aquilano

Design Procurement Production Final test Installation Servicing

ISO 9003ISO 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

DesignProduction

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 noiseImportance weighting

Engineering Characteristics

Ener

gy n

eede

d to

clo

se d

oor

Che

ck fo

rce

on

leve

l gro

und

Ener

gy n

eede

d to

ope

n do

or

Wat

er re

sist

ance

10 6 6 9 2 3

7

5

3

3

2

X

X

X

X

X

Correlation:Strong positivePositiveNegativeStrong negative

X*

Competitive evaluationX = 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/lb

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)

54321

B

A

X

BAX B

AX

BX

A

BXABA

X

Doo

r sea

l re

sist

ance

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

Engi

neer

ing

Cha

ract

eris

tics

Parts

Cha

ract

eris

tics

Key

Pro

cess

Cha

ract

eris

tics

Cus

tom

er A

ttrib

utes


Benefits of QFD

Startup and Preproduction costs at Toyota Auto Body

Japanese automaker 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

$$$

0Lower

ToleranceDesignSpec

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


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): CapabilityDistribution 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

Num

ber o

f Day

s

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?


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.


Quality Wireless (A): Checking for 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


Has Process Performance Changed? Quality Wireless (B)

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?


Performance of Inventory Manager

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


Statistical Process Control: Source of Variability

Inherent (common cause)

External (assignable cause)

Objective: Identify inherent variability and eliminate 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.


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


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

– in control has only inherent (from common cause) variation– out of control has variation from an assignable cause

SPC framework for process control and improvement


Why 6-Sigma? 2 sigma: 69.146% of products and/or services meet customer requirements

with 308,538 defects per million opportunities.

4 sigma: 99.379% of products and/or services meet customer requirements ...

but there are still 6,210 defects per million opportunities.

6 sigma: 99.99966% – As close to flaw-free as a business can get, with just

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


Why 6-Sigma?

Impact of # of parts/stages in a process

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

# of steps/parts1 93.3% 99.4% 100.0% 100.0%

10 50.1% 94.0% 99.8% 100.0%50 3.2% 73.2% 98.8% 100.0%

100 0.1% 53.6% 97.7% 100.0%144 0.00% 40.8% 96.7% 100.0%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%

0.0%

0.0%

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


Why 6-Sigma? Robustness to Mean Shifts

100 130 160

LSL USL

= 10

100 143 160

LSL USL

= 10

LSL USL

= 5

100 130 160

LSL USL

= 5

100 143 160

99.9 % 99.9 %


Why 6-Sigma? 6-Sigma Quality at Flyrock

At the extruder, the rubber for the AX-527 tires had thickness specifications of 400 10. 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 and a standard deviation of 4.

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

S. Chopra/Operations/Quality

Process Capability: Sigma Capability Sigma capability is the number of standard deviations

from the mean to the closest specification limit. Sigma capability of extrusion process =

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?


Impact of Mean Shift

If a bearing is worn out, the extruder produces a mean thickness of 403 when the setting is 400. Under this condition, what proportion of defective sheet will the extruder produce? Assuming the control limits in (2), 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.


Why 6-Sigma? Rapid Detection

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 and a standard deviation of 1.667. What should the new control limits be? What is the proportion of defectives produced?


Improving Process Capability

Return to the case of the worn bearing in (3) where extrusion produces a mean thickness of 403 when the setting is 400. Under this condition, what proportion of defective sheets will the extruder produce (for the 6-sigma process)? Assuming the control limits in (5), 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.


Key Learning Objectives: SPC Specification limits: Voice of the customer Process capability is a measure of the quality

delivered (external): links VoP with VoC Improving capability may require variability reduction

and/or mean shift Control limits used to verify if process is in control

(internal), i.e., is maintaining capability: Voice of the process

Higher process capability reduces defectives and speeds up detection of assignable cause

d30 quality

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