1 chapter 12 supplement quality control tools for process improvement 1 chapter 12-supplement...

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Chapter 12 Supplemen

t

Quality Control Tools for Process Improvement

Chapter 12-SupplementChapter 12-Supplement

QUALITY CONTROL QUALITY CONTROL TOOLS FOR PROCESS TOOLS FOR PROCESS

IMPROVEMENTIMPROVEMENT

McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc., All Rights Reserved.

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LEARNING OBJECTIVES

• Introduce the basic tools for analyzing processes and quality.

• Describe acceptance sampling and when it can be used to control quality in services.

• Introduce statistical process control and describes how and when it can be used to analyze service processes.

• Define the types of sampling errors that can occur when statistical sampling is used.

• Distinguish between the statistical analysis of attributes and variables.

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THE SEVEN BASIC QUALITY TOOLS

• Process Flow Diagrams

• Checksheets

• Histograms/Bar Charts

• Pareto Charts

• Scatterplots

• Run Charts

• Cause-and-Effect Diagrams

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Exhibit 12S.1PROCESS FLOW DIAGRAM: DRYCLEANING SERVICE

Pay in advance?

Requestreceipt

Acceptpayment

Pickingup?

Give clothesTo customer;

Verify ownership

yes

no

Droppingoff?

CheckClothing/

Complete form

Getclothes

no

yesGreetcustomer

Providereceipt

yes

end

no

Give farewellgreeting

startPay in

advance?

Requestreceipt

Acceptpayment

Pickingup?

Give clothesTo customer;

Verify ownership

yes

no

Droppingoff?

CheckClothing/

Complete form

Getclothes

no

yesGreetcustomer

Providereceipt

yes

end

no

Give farewellgreeting

start

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Exhibit 12S.3CHECKSHEET

Count

1

2

3

4

5

6

>6

Customers

In PartyCount

Location question

Rate question

Complaint

Reservation

Service question

Guest call

Other

Call Type Count

1

2

3

4

5

6

>6

Customers

In PartyCount

Location question

Rate question

Complaint

Reservation

Service question

Guest call

Other

Call Type

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Exhibit 12S.4EXAMPLE OF A BAR CHART

Country of Origin of Customers During Lunch Thursday June 10

02468

10121416

US

Ind

ia

Ko

rea

Ja

pa

n

Fra

nce

Arg

en

tin

a

Le

ba

no

n

Fre

qu

en

cy

Country of Origin of Customers During Lunch Thursday June 10

02468

10121416

US

Ind

ia

Ko

rea

Ja

pa

n

Fra

nce

Arg

en

tin

a

Le

ba

no

n

Fre

qu

en

cy

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Exhibit 12S.5EXAMPLE OF A HISTOGRAM

0

2

4

6

85 10

15

20

25

30

35

40

Fre

qu

en

cy

Time to Take Order

Seconds

0

2

4

6

85 10

15

20

25

30

35

40

Fre

qu

en

cy

Time to Take Order

Seconds

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Exhibit 12S.6EXAMPLE OF A PARETO CHART

0

2

4

6

8

10

12

14

16

18

Fre

qu

enc

y

0%

20%

40%

60%

80%

100%

Cu

mu

lati

ve P

erce

nt

proc

ed

ure

s

spac

e

pat

ien

ts

ED

Sta

ff

Hos

pita

l st

aff

equ

ipm

ent

Survey Responses: Factors Requiring Change

0

2

4

6

8

10

12

14

16

18

Fre

qu

enc

y

0%

20%

40%

60%

80%

100%

Cu

mu

lati

ve P

erce

nt

proc

ed

ure

s

spac

e

pat

ien

ts

ED

Sta

ff

Hos

pita

l st

aff

equ

ipm

ent


0

2

4

6

8

10

12

14

16

18

Fre

qu

enc

y

0%

20%

40%

60%

80%

100%

Cu

mu

lati

ve P

erce

nt

proc

ed

ure

s

spac

e

pat

ien

ts

ED

Sta

ff

Hos

pita

l st

aff

equ

ipm

ent


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Exhibit 12S.8EXAMPLE OF A SCATTERPLOT

01

23

456

78

910

0 2 4 6 8 10 12 14

Waiting Time to Order (minutes)

Sa

tisf

acti

on

01

23

456

78

910

0 2 4 6 8 10 12 14

Waiting Time to Order (minutes)

Sa

tisf

acti

on

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Exhibit 12S.9EXAMPLE OF A RUN CHART

Arrival Time

Wait

Tim

e,

min

ute

s

10

:00

p.m

.

Average Waiting Time for a Table

6:3

0 p

.m.

7:0

0 p

.m.

7:3

0 p

.m.

8:3

0 p

.m.

8:0

0 p

.m.

9:3

0 p

.m.

9:0

0 p

.m.

6:0

0 p

.m.

30

20

10

40

60

50

70

Arrival Time

Wait

Tim

e,

min

ute

s

10

:00

p.m

.

Average Waiting Time for a Table

6:3

0 p

.m.

7:0

0 p

.m.

7:3

0 p

.m.

8:3

0 p

.m.

8:0

0 p

.m.

9:3

0 p

.m.

9:0

0 p

.m.

6:0

0 p

.m.

30

20

10

40

60

50

70

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Exhibit 12S.10EXAMPLE OF A CAUSE-AND-EFFECT DIAGRAM Server Attitude Waits

Food Environment

Rude

Too many tables

Confused

Inadequate training

Long waits for check

Uncomfortable wait for table

Food served cold

Server notificationsystem inadequate

Cold dining area

Soiled carpets

Inadequatehousekeepertraining

DissatisfactionWith Experience

Server Attitude Waits

Food Environment

Rude

Too many tables

Confused

Inadequate training



Food served cold


Cold dining area

Soiled carpets



Food Environment

Rude

Too many tables

Confused

Inadequate training



Food served cold


Cold dining area

Soiled carpets


DissatisfactionWith Experience


Food Environment

Rude

Too many tables

Confused

Inadequate training



Food served cold


Cold dining area

Soiled carpets


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Exhibit 12S.11THE EIGHT STEP QUALITY IMPROVEMENT PROCESSPDCA # STEP/DESCRIPTION TOOLS

P 1 Identify the Problem

L Collect Data

A Analyze the Problem

N Scatterplots Pareto Charts

Histograms/Bar ChartsFlow Charts Run Charts

Identify Cause-and-EffectRoot Causes Diagrams

Develop a solutionAnd action plan

DO

C Scatter PlotsH Pareto ChartsE HistogramsC Flow ChartsK Run Charts

Control Charts

A FlowchartsC Control chartsT

2 Checksheets

3 Analyze the Problem

4

5 Flowcharts

6

Implement the Solution

7 Review and Evaluate

8

Standardize the Process

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TWO BASIC MODELS FOR STATISTICAL QUALITY CONTROL

• Acceptance Sampling: This involves testing samples after the goods or services have been produced.

• Statistical Process Control: This involves testing samples of goods or services as they are being produced.

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Exhibit 12S.12THE RELATIONSHIP BETWEEN POPULATION QUALITY AND SAMPLE FINDINGS

Good Lot Accepted: No

Error

Bad Lot Accepted: Type II Error or

Good Lot Rejected: Type II

Error or

Bad Lot Rejected: No Error

What the Population Quality Actually Is

Good

Bad

Wh

at

the S

am

ple

Fin

din

g I

s

Goo

dB

ad

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Exhibit 12S.13OPERATING CHARACTERISTIC CURVE

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

OC Curve: N = 100, n = 15, c = 1

Actual Percent Defective (d)

0 51 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

OC Curve: N = 100, n = 15, c = 1

Actual Percent Defective (d)

0 51 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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Exhibit 12S.14THE NORMAL DISTRIBUTION

68.26%

95.44%

99.73%

+1s +2s +3s-2s -1s-3s

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PROCESS CAPABILITY

1. Collect data on the process while the process is operating without known causes of variation.

2. Compare the customer’s requirements to the inherent variation of the process.

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Exhibit 12S.16CAPABILITY STUDY

Process is not capable: Specifications are well within 3 s.Much of the output of the process will not meet the customer’s requirements.

+3s-3s Mean=12

specifications

process variation

Process is Capable: Specifications are outside 3 s.Virtually all of the output of the process will meet the customer’s requirements.

+3s-3s Mean=12

specifications

process variation

(A) (B)Process is not capable: Specifications are well within 3 s.Much of the output of the process will not meet the customer’s requirements.

+3s-3s Mean=12

specifications

process variation

Process is Capable: Specifications are outside 3 s.Virtually all of the output of the process will meet the customer’s requirements.

+3s-3s Mean=12

specifications

process variation

(A) (B)

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Exhibit 12S.17SIX-SIGMA QUALITY

-3s

specifications

+3sMean

process variation

-6s +6s

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Exhibit 12S.20CHANGES IN MEAN AND DISPERSION OF SAMPLE MEAN DISTRIBUTIONS

Shift in mean of distribution Shift in dispersion of distribution.Shift in mean of distribution Shift in dispersion of distribution.

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KEY TERMS

• Acceptance Sampling• Capability• Central Limit Theorem• Operating Characteristic Curve• Statistical Process Control• Type I Error• Type II Error• Variables

1 chapter 12 supplement quality control tools for process improvement 1 chapter 12-supplement...

Documents

statistical process

process capability

population quality

basic tools

histogram slide

scatterplot slide

checksheet slide

good bad slide