Chap009 and Chap010 Quality Mgt

8/3/2019 Chap009 and Chap010 Quality Mgt

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McGraw-Hill/Irwin Copyright 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

Chapters 9 & 10

Management of Quality

Cheng Li, Ph.D.

Management DepartmentCalifornia State University, Los Angeles


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What does the term qualitymean?

Most popular: Quality is the ability of a product orservice to consistently meet or exceed customerexpectations.

A more comprehensive definition:

Quality is a concept that involves multipledimensions, which are

Excellence

Value Conformance to specifications

Meeting or exceeding customer expectations

Management of Quality


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Dimensions of Quality

Excellence: absolute measures

Performance - main characteristics of theproduct/service

Special features - extra characteristics

Aesthetics - appearance, feel, smell, taste

Safety- risk of injury

Reliability - consistency of performance, infrequency ofbreakdowns

Availability: fraction of time equipment is available

Durability- useful life of the product/service

Service after sale - handling of customer complaints orchecking on customer satisfaction


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Dimensions of Quality (Contd)

Value: a relative measure, utility over cost

Conformance relative to specifications, how well

product/service conforms to specifications

Meeting or exceeding customer expectations: relative tocustomer expectations

Perceived Quality subjective evaluation of quality (e.g.reputation)


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Determinants of Quality

Product

Conceptual design: setting design objectives

Market research: understanding customer

requirements Strategic planning: supporting strategic objectives

Design: developing specifications that ensurethe achievement of objectives

Process design and control*: conformance tospecifications

Service: meeting or exceeding customerexpectations


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The Consequences of Poor Quality

Loss of business: typically happens gradually,takes a long time to recover if ever

Liability: may be forced into bankruptcy

Loss of productivity: e.g. inspection, rework,material handling of additional inventory,returns, etc.

Costs

Internal and external failure costs Costs of additional inspection

Costs of additional safety stock


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Responsibility for Quality

Top management: responsible for

Creating a quality culture in the organization

Setting incentives

Quality should be in everyones job

description, including but not limited to

Process owners, i.e., workers and managers

of the process, as well as inspectors Marketing, sales, and customer service


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Ethics and Quality

Having knowledge of below and failing to

correct and report it in a timely manner:

unethical and may be illegal.

Defective products

Substandard service

Poor designs

Shoddy workmanship Substandard parts and materials

Anticipated misuse: Producers are liable.


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Contributor

Shewhart

Deming

Juran

Feigenbaum

Crosby

Ishikawa

Taguchi

Known for

Control charts; variance reduction

14 points; special vs. common causes of variation

Quality is fitness for use; quality trilogy: planning, control,and improvement

Total Quality Control: a total field; the customer definesquality.

Quality is free; zero defects

Cause-and effect diagrams; quality circles

Taguchi loss function to determine the cost of poorquality

Quality

Key Contributors to Quality

Management


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Baldrige Award

Deming Prize

Quality Awards


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Malcolm Baldrige National Quality

Award: Point Values*

1.0 Leadership (120 pts., 12%)

2.0 Strategic Planning (85 pts., 8.5%)

3.0 Customer and Market Focus (85 pts., 8.5%)

4.0 Measurement, Analysis, and Knowledge

Management (90 pts., 9%)

5.0Human Resource Focus (85 pts., 8.5%)

6.0 Process Management (85 pts., 8.5%)

7.0 Business Results (450 pts., 45%)


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The Deming Prize

Honoring W. Edwards Deming

Japans highly coveted award

Main focus on statistical quality control

Japan Quality Award


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Quality Certification

ISO 9000 Series Standards

A set of international standards on qualitymanagement and quality assurance

Critical to international business

Require firms to document their quality-controlsystems at every step (incoming raw materials,product design, in-process monitoring and so forth)

Purpose: identify areas that are causing qualityproblems and correct them.

Hierarchical approach to documentation of the QualityManagement System


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ISO 9000 Helps companies determine which standard of

ISO 9001, 9002, and 9003 applies

ISO 9001 Outlines guidelines for companies that engaged

in design, development, production, installation,and servicing of products or service

ISO 9002 Similar to 9001, but does not include design and

development

ISO 9003 For companies engaged in final inspection and

testing

ISO 9004 The guidelines for applying the elements of the

Quality Management System

ISO 9000 Series


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ISO 9000 Registration Process

Self-study: Document the current system

Gap analysis and corrective actions

Possibly with assistance from consultants

Request an accredited registrar or other third partyaudit team for pre-assessment

The final audit by the audit team: Review of the company's quality manual: Does the

documented quality system meet the requirement of

ISO9000

? Site visit to verify that the organization is practicing

what is documented.

Registration: issued by the registrar


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ISO 14000: Environmental

Management

ISO 14000 a set of environmentalmanagement standards*

Minimize how their operations impact the

environment Comply with applicable laws and other

requirements

Continually improve in the above

As with ISO 9000, certification is performedby third-party organizations rather than byISO.


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ISO Guidelines

Guide documents: no certification Project Management

ISO 10006 Guidelines for quality management inprojects

ISO 10007 Guidelines for configurationmanagement

Quality Auditing ISO 19011 Guidelines for Quajlity and

Environmental Management Systems Auditing

(ISO 9000 and 14000 series) Quality Manual Development

ISO 10013 Quality manual development guide


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McGraw-Hill/Irwin Copyright 2009 by The McGraw-Hill Companies, Inc. All rights reserved.

Cheng Li, Ph.D.

Management DepartmentCalifornia State University, Los Angeles

Chapter 10 Quality

Control


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Inputs Transformation Outputs

Acceptance

sampling

Process

control

Acceptance

sampling

Inspection

In-process

inspection

Quality Control Model: Inspection and

Process Control

Internal

Processes

Distribution

/after-sales

Processes

Vendor

Processes


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Quality Control Approaches

Inspection:

Filtering out the bad products

Decision: quality of a batch of products

e.g. Is this shipment of raw material acceptable?


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Quality Control Approaches

Process Control:

Prevention, instead of after-the-fact filtering

Monitor conditions of the process

Direct monitoring: e.g. monitoring conditions of theequipment and tooling, monitoring quality of faculty,

site visits to vendors process (external processes)

Indirect monitoring: through the results produced by

the process; e.g. taking small samples of products

produced by the process, customer surveys Assess variances

Take corrective actions if necessary


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Acceptance

sampling

Process

control

Continuous

improvement

Inspection

before/after

production

Corrective

action during

production

Quality built

into the

process

The least

progressive

The most

progressive

Phases of Quality Assurance


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Inspection Decisions

Time: When/How often

e.g. in-process vs. post-production

e.g. every batch vs. occasional even

Quantity: How much

100% vs. sampling

sample size

Location: Where e.g. centralized vs. on-site for inputs

No inspection: the goal


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Inspection Costs

Cost of Inspection vs. Cost of Passing Defectives

0

50

100

150

200

250

300

350

400

450

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Amount of Inspection

C

o

s

t

Cost of Inspection

Cost of Passing

Defectives

Total Cost

Optimal

amount of

inspection

Given acceptance criterion, as

quality, prob{passing

defectives}, cost of passing

defectives. Result: optimal

amount of inspection


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Where to Inspect in the Process

Raw materials and purchased parts

The end of every major phase in the process, including finishedproducts

Critical Control Points: e.g. Hazard Analysis and Critical

Control Point (HACCP): points in a food processing processwhere pollution are most likely to happen

Others:

Before a costly operation

Before an irreversible process: e.g. cannot be reworkedbeyond this point

Before a covering process: e.g. packaging


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Type ofbusiness

Inspectionpoints

Characteristics

Fast FoodRestaurant

CashierCounter areaEating areaBuildingKitchen

AccuracyAppearance, productivityCleanliness

AppearanceHealth regulations

Hotel/motel Parking lotAccountingBuildingMain desk

Safe, well lightedAccuracy, timelinessAppearance, safetyWaiting times

Supermarket CashiersDeliveries

Accuracy, courtesyQuality, quantity

T

able10-1

modified

Examples ofInspection Points


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Statistical Process Control

Statistical Process Control (SPC):

Statistical evaluation of the output of a process

during production

Indirect monitoring of the conditions of theprocess by sampling the output

Objective: Is the process in-control?


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Variations and Control

Random variation: Natural variations in the

output of process, created by countless minor

factorsAssignable variation: A variation whose source

can be identified

In-control: random variations only

Out-of-control: random and assignable

variations


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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

UCL

LCL

Sample number

Mean

Out of

control

Normal variation

due to chance

Abnormal variation

due to assignable sources

Abnormal variation

due to assignable sources

A Typical Control Chart


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The Control Process (as applied to SPC)

1. Define: establish control limits

2. Measure: take a sample and measure the units in thesample

3. Compare to a standard: compare the sample resultsagainst the control limits

4. Evaluate:

If the sample results are within the limits, in-control;

otherwise, out of control, go on to the next step

5. Take corrective action: investigate the cause ofdeviation and take corrective actions if necessary

6. Evaluate corrective action and go back to step 2.


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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

UCL

LCL

Sample number

Mean

1. Set standards

A Typical Control Chart

2. Measure &

3. Compare

4. Evaluate:

in-control

4. Evaluate:

Out-of-control


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Observations from Sample Distribution

Sample number

UCL

LCL

1 2 3 4

Figure 10-9

Each sample is taken

from a distribution


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-6 -4 -2 0 2 4 6 8

In-Control Shifted by 2 Shifted by 4

-6 -4 -2 0 2 4 6 8

In-Control Shifted by 2

-6 -4 -2 0 2 4 6 8

In-Control

In-Control vs. Out-of-Control

In-control: random variations only, mean = 0, = 1


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-6 -4 -2 0 2 4 6 8


Out-of-Control: Shift of the Mean

Out-of-control: shift of the mean


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-6 -4 -2 0 2 4 6

In-Control StdDev=1 StdDev=2

Out-of-Control: Increase ofStandard

Deviation

Out-of-control: increase of standard deviation, = 2


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Types of Control Charts: Overview

Control Charts for Variables: for continuous

measures such as temperature, volume, etc.

Mean Chart (or -Chart): detects shift of the mean

Range Chart (orR-

Chart: detects change in shape ofdistribution

Control Charts for Attributes: for discrete measures

such as number of complaints, scratches, etc.

p-Chart: measures percent defective

c-Chart: measures # of defects per sample

X


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For Variables: Mean Control Chart

When the process mean () and standard deviation () are known:

The standard deviation of sample means (X-bar):

n

X

WW !:meanssampleofdeviationStandard

X

X

zLCL

zUCL

WQ

WQ

!

!

:LimitControlLower

:LimitControlUpper


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When the process mean () and standard deviation () are known:

The standard deviation of sample means (X-bar):

Example:

Process MeanO= 10

Process Std DevU= 0.3

Sample Size n = 9

No. of std dev z = 3

Exercise: Calculate UCL and LCL, and draw a mean control chart.


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For Variables: Mean & Range Control

Charts

When the process mean () and standard deviation () are unknown:

Example: Measuring sound

pressure level (in decibels)

of loudspeakers.

S am ple

U nit 1 2 3

1 95 97 97

2 94 95 963 93 96 96

4 95 95 92

5 95 97 93

m ean 94.4 96 94.8

range 2 2 5

G rand A vg = 95.07

A vg R ange = 3.00X

Question:W

hat is thesample size in this example?


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For3-sigma (i.e. standard deviation) control charts:

Use Table 10-2 and the sample size to find A2, D3, and D4

RDLCL

RDUCL

R

RAXLCL

RAXUCL

X

3

4

2

2

:chart)(Range

:chart)(Mean

!

!

!

!

Exercise: Calculate UCLs andLCLs for X-bar and Range chartts,

using data in the previous example.


Charts


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Procedures:

1. Collect multiple samples;

2. Calculate sample averages ( s) and ranges (Rs);

3. Calculate grand average ( ) and average of the ranges ( );4. Use Table 10-2 to find A2, D3, and D4;

5. Calculate UCLs and LCLs for the mean and range charts.

X R

X


Charts


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Mean Control Chart

92

93

94

95

96

97

98

1 2 3 4 5 6 7 8 9 10

Sample No.

Samp

le

Mean

LCL = 93.33

UCL = 96.81

Nominal Value = grand avg = 95.07



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Range Control Chart

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10

Sample No.

Range

LCL = 0

UCL = 6.33

Nominal Value = avg range = 3.00

For Variables: Range Control Chart


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For Variables: Using Mean & Range

Control Charts


Question:According to the

mean and range control

charts, is the process in

control when this latestsample was collected?

U nit D ecibels

1 97

2 1013 96

4 95

5 93

X


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Control Chart for Attributes

p-Chart: Control chart used to monitor the

proportion of defectives in a process

p = percent defective

c-Chart: Control chart used to monitor the

number of defects per sample

c = number of defects per sample


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Use of p-Charts

Use when observations of each unit can be

placed into two categories: either . or.

Good or bad

Pass or fail

Operate or dont operate

Question: If the sample size is n, what is the

number of possible outcomes for a givensample?

The number of possible outcomes is finite.


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Use of p-Charts

Requirements

Multiple samples

Relatively large sample size (n >= 20)

If n = 20, possible results can be found below:

0 defective: p = 0

1 defective: p = 1/20 or5%

2 defectives: p = 2/20 or10%, etc.

Note: Percent defective changes in increment of5%.

If the true percent defective = 0.3%, it will never showup in the results. Therefore, p-Charts must use largesamples.


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Use of p-Charts

p

p

p

zpLCL

zpUCL

n

ppp

p

W

W

W

!

!

!

v

!

)1(:ofdeviationStandard

sizesamplesamplesof#

defectivesof#total:defectivePercent

Construct p-Charts:


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Use of c-Charts

Use when

the number of occurrences per sample can becounted

non-occurrences cannot be counted

No upper limit on the number of occurrences (i.e.the number of possible outcomes is infinite)

Examples

Scratches, chips, dents, or errors per item

Cracks or faults per unit of distance

Breaks or tears per unit of area

Bacteria or pollutants per unit of volume

Calls, complaints, failures per unit of time


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Use of c-Charts

c

c

c

zcLCL

zcUCL

cc

c

W

W

W

!

!

!

!

:ofdeviationStandard

samplesof#

defectsof#total:sampleperdefectsofNo.

Construct c-Charts:


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Use of p-Charts and c-Charts

Example: p-Chart Example: c-Chart

p-C hartn = 200

S am ple# of

D efectives

1 0

2 1

3 2

4 0

5 2

6 1

7 1

c-C hart

S am ple# of

D efects

1 0

2 1

3 2

4 0

5 2

6 1

7 1

Exercise: Construct a p-Chart and a c-chart

respectively.


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-6 -4 -2 0 2 4 6 8

In-Control

Type IError: false alarm

/2/2

Type I Error: System is in-control, chart

falsely indicates out-of-control

= probability ofType I Error (z=2: = 2(1-0.97725) = 0.0455)

2-sigmaControl

Chart

2 (2 sigma)

Control Chart


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-6 -4 -2 0 2 4 6 8


Type IIError: failure to detect

assignable variation

Shifted by 2

Shifted by 2

Mean shifts by 2,

Prob{Type II} = 49.99%

2 (2 sigma)

Control Chart


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-6 -4 -2 0 2 4 6 8

In-Control Shifted by 2 Shifted by 4

Type IIError: shift of the mean

Shifted by 2

Shifted by 2

Mean shifts by 4,

Prob{Type II} decreases

to 2.28%

As the problem worsens, prob{Type II} decreases rapidly, i.e. the

problem is more likely to be detected.

2 (2 sigma)

Control Chart


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Type IIError: increase of standard

deviation

-6 -4 -2 0 2 4 6

In-Control StdDev=1 StdDev=2

StdDev=2

StdDev=2

Process is out-of-control:

StdDev increases to 2,

Prob{Type II} = 68.27%

Original distribution

when the process was

in-control

StdDev=2

StdDev=2

Prob{detecting the problem} = 1-68.27% = 31.73%


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Control charts for variables (continuous variables)

Mean Chart ( X-chart ): sample average

Range Chart (R-chart): sample range, i.e., max-min

Control charts for attributes (discrete variables)

p-chart: defective rate of the sample

c-chart: number of defect per sample

Control Charts

chart-X


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UCL

LCL

UCL

LCL

R-chart

x-Chart Detects shift

Does not

detect shift

Figure 10-10A

(Process mean is

shifting upward, but

shape remains the same.)Sampling

Distribution

Mean and Range Charts: shift of the

mean


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9&10-58LCL

Reveals increase

UCL

LCL

x-Chart

R-chart

UCL

Does not

reveal increase

Figure 10-10B

(Process variability is

increasing from 1 to 1.75,

but the mean remains

unchanged.)

Sampling

Distribution

Mean and Range Charts: increase in

variability


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Type I and Type IIErrors

OK Type II

Type I OK

Process is

In Out

Out

In

ChartI

ndicates


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Counting Above/Below Median Runs (7 runs)

Counting Up/Down Runs (8 runs)

U U D U D U D U U D

B A A B A B B B A A B

Figure 10-12

Figure 10-13

Counting Runs


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Tolerances

specifications

Process variability Natural variability in a process

Process capability

Process variability relative to specification

Process Capability


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Process Capability

Lower

Specification

Upper

Specification

Process variability matches

specifications

Lower

Specification

Upper

Specification

Process variability well within

specificationsLower

Specification

Upper

Specification

Process variability exceeds

specifications

Figure 10-15


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Process Capability Ratio

Process capability ratio, Cp =specification width

process width

Upper specification lower specification

6WCp =


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Process

mean

Lower

specification

Upper

specification

1350 ppm 1350 ppm

1.7 ppm 1.7 ppm

+/- 3 Sigma

+/- 6 Sigma

3 Sigma and 6 Sigma Quality

Documents

Chap009 and Chap010 Quality Mgt