Krajewski Chapter 06

7/23/2019 Krajewski Chapter 06

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2007 Pearson Education

Process Performanceand Quality

Chapter 6Chapter 6


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How Process Performance and Quality

fits the Operations Management

Philosophy

Operations As a CompetitiveWeapon

Operations StrategyProject Management Process Strategy

Process AnalysisProcess Performance and Quality

Constraint ManagementProcess LayoutLean Systems

Supply Chain StrategyLocation

Inventory ManagementForecasting

Sales and Operations PlanningResource Planning

Scheduling


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Quality at

Crowne Plaza Christchurch

The Crowne Plaza is a luxury hotel with 298 guest

rooms three restaurants, two lounges and 20

em!loyees to ser"e 2,2#0 guests each wee$%

Customers ha"e many o!!ortunities to e"aluate the&uality o' ser"ices they recei"e%

Prior to the guest(s arri"al, the reser"ation sta''

gathers a considera)le amount o' in'ormation a)out

each guest(s !re'erences% *uest !re'erences are shared with house$ee!ing

and other sta'' to customize ser"ice 'or each guest%

Em!loyees are em!owered to ta$e !re"entati"e,

and i' necessary, correcti"e action%


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Costs of Poor

Process Performance

Defects+ ny instance when a !rocess 'ails tosatis'y its customer%

Prevention costsare associated with

!re"enting de'ects )e'ore they ha!!en% Appraisal costsare incurred when the 'irm

assesses the !er'ormance le"el o' its !rocesses%

Internal failurecostsresult 'rom de'ects that

are disco"ered during !roduction o' ser"ices or!roducts%

External failurecostsarise when a de'ect isdisco"ered a'ter the customer recei"es theser"ice or !roduct%


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Total Quality Management

Quality+ term used )y customers to descri)e

their general satis'action with a ser"ice or

!roduct%

Total quality management-T./ is a!hiloso!hy that stresses three !rinci!les 'or

achie"ing high le"els o' !rocess !er'ormance

and &uality+

1% Customer satis'action2% Em!loyee in"ol"ement

% Continuous im!ro"ement in !er'ormance


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CustomerCustomer

satisfactionsatisfaction

TQM Wheel


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Customer Satisfaction

Customers, internal or external, are satis'ied whentheir ex!ectations regarding a ser"ice or !roductha"e )een met or exceeded%

Con'ormance+3ow a ser"ice or !roduct con'orms

to !er'ormance s!eci'ications% 4alue+ 3ow well the ser"ice or !roduct ser"es its

intended !ur!ose at a !rice customers are willingto !ay%

5itness 'or use+3ow well a ser"ice or !roduct

!er'orms its intended !ur!ose% 6u!!ort+6u!!ort !ro"ided )y the com!any a'ter a

ser"ice or !roduct has )een !urchased% Psychological im!ressions+ atmos!here, image, or

aesthetics


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mployee !n"ol"ement

ne o' the im!ortant elements o' T./ is em!loyeein"ol"ement%

Quality at the sourceis a !hiloso!hy where)yde'ects are caught and corrected where they werecreated%

Teams+ 6mall grou!s o' !eo!le who ha"e acommon !ur!ose, set their own !er'ormance goalsand a!!roaches, and hold themsel"es accounta)le

'or success% Employee empowermentis an a!!roach to

teamwor$ that mo"es res!onsi)ility 'or decisions'urther down the organizational chart to the le"el o'the em!loyee actually doing the o)%


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Quality circles+ nother name 'or !ro)lemsol"ing

teams: small grou!s o' su!er"isors and em!loyees

who meet to identi'y, analyze, and sol"e !rocess

and &uality !ro)lems%

pecial!purpose teams+ *rou!s that address

issues o' !aramount concern to management,

la)or, or )oth%

elf!managed team+ small grou! o' em!loyeeswho wor$ together to !roduce a maor !ortion, or

sometimes all, o' a ser"ice or !roduct%

Team #pproaches


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Continuous !mpro"ement

Continuous improvementis the !hiloso!hy o'continually see$ing ways to im!ro"e !rocesses)ased on a ;a!anese conce!t called kaizen%

1% Train em!loyees in the methods o' statistical!rocess control -6PC and other tools%

2% /a$e 6PC methods a normal as!ect o'o!erations%

% tilize !ro)lemsol"ing tools within the wor$

teams%

#% ?e"elo! a sense o' o!erator ownershi! in the!rocess%


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PlanPlan

DoDo

Chec"Chec"

ActAct

The $eming WheelPlan%$o%Chec&%#ct Cycle


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Statistical

Process Control

tatistical process controlis the a!!lication o'statistical techni&ues to determine whether a !rocess isdeli"ering what the customer wants%

Acceptance samplingis the a!!lication o' statisticaltechni&ues to determine whether a &uantity o' materialshould )e acce!ted or reected )ased on the ins!ectionor test o' a sam!le%

#aria$les+ 6er"ice or !roduct characteristics that can)e measured, such as weight, length, "olume, or time%

Attri$utes+ 6er"ice or !roduct characteristics that can)e &uic$ly counted 'or acce!ta)le !er'ormance%


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Sampling

ampling plan+ !lan that s!eci'ies a

sam!le size, the time )etween successi"e

sam!les, and decision rules that determinewhen action should )e ta$en%

ample si%e+ &uantity o' randomly

selected o)ser"ations o' !rocess out!uts%


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Sample Means and

the Process $istri'ution

6am!le statistics ha"e their own distri)ution, which

we call a sampling distribution%


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Sampling $istri'utionsSampling $istri'utions

x =

xii=1

n

n

Sample Mean

sample meanis the sum o' the o)ser"ations

di"ided )y the total num)er o' o)ser"ations%

where

xi@ o)ser"ations o' a &uality

characteristic such as time%

n@ total num)er o' o)ser"ations

x@ mean

The distribution of sample means can be

approximated by the normal distribution.


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Sample (ange

( )1

2

= n

xxi

The rangeis the di''erence )etween the largest

o)ser"ation in a sam!le and the smallest%

The standard deviationis the s&uare root o' the

"ariance o' a distri)ution%

where

@ standard de"iation o' a sam!le

n@ total num)er o' o)ser"ations

xi@ o)ser"ations o' a &uality characteristic

x@ mean


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Process $istri'utions

processdistri$utioncan )e characterized )y its

location, s!read, and sha!e%

&ocationis measured )y the meano' the

distri)ution and spreadis measured )y the range orstandard deviation%

The shapeo' !rocess distri)utions can )e

characterized as either symmetric or s$ewed%

symmetricdistri$utionhas the same num)er o'o)ser"ations a)o"e and )elow the mean%

s"eweddistri$utionhas a greater num)er o'

o)ser"ations either a)o"e or )elow the mean%


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Causes of )ariation

Two )asic categories o' "ariation in out!ut includecommon causesand assignable causes%

Commoncausesare the !urely random,unidenti'ia)le sources o' "ariation that areuna"oida)le with the current !rocess%

A' processvaria$ilityresults solely 'rom common causeso' "ariation, a ty!ical assum!tion is that the distri)ution is

symmetric, with most o)ser"ations near the center%

Assigna$lecauseso' "ariation are any "ariationcausing 'actors that can )e identi'ied and eliminated,such as a machine needing re!air%


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#ssigna'le Causes

The red distri)ution line )elow indicates that the !rocess !roduced a

!re!onderance o' the tests in less than a"erage time% 6uch a distri)ution

is s$ewed, or no longer symmetric to the a"erage "alue%

A process is said to $e in statistical controlwhen the location, s!read,

or sha!e o' its distri)ution does not change o"er time%'ter the !rocess is in statistical control, managers use 6PC !rocedures

to detect the onset o' assigna)le causes so that they can )e eliminated%

*ocation Spread Shape



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Control Charts

Control chart+ timeordered diagram that is used to

determine whether o)ser"ed "ariations are a)normal%

sam!le statistic that 'alls )etween the >CB and the BCB indicates that the !rocess

is exhi)iting common causes o' "ariation: a statistic that 'alls outside the control

limits indicates that the !rocess is exhi)iting assigna)le causes o' "ariation%


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Control Chart +amples


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Type ! and !! rrors

Control charts are not !er'ect tools 'or detecting

shi'ts in the !rocess distri)ution )ecause they are

)ased on sam!ling distri)utions% Two ty!es o' error

are !ossi)le with the use o' control charts%

Type I erroroccurs when the em!loyee concludes

that the !rocess is out o' control )ased on a sam!le

result that 'alls outside the control limits, when in

'act it was due to !ure randomness%Type II erroroccurs when the em!loyee concludes

that the !rocess is in control and only randomness

is !resent, when actually the !rocess is out o'

statistical control%


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Statistical Process

Control Methods

Control Charts 'or "aria)les are used to monitor themean and "aria)ility o' the !rocess distri)ution%

(!chart-ange Chart is used to monitor !rocess

"aria)ility%+!chartis used to see whether the !rocess is

generating out!ut, on a"erage, consistent with atarget "alue set )y management 'or the !rocess orwhether its current !er'ormance, with res!ect to thea"erage o' the !er'ormance measure, is consistentwith !ast !er'ormance%

A' the standard de"iation o' the !rocess is $nown, we can!lace UCLand LCLat Dz standard de"iations 'rom themean at the desired con'idence le"el%


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Control *imits

The control limits for the+!chartare+

>CBx@xFA2R and BCBx@xA2R

Ghere

@ central line o' the chart, which can )e either the a"erage o' !ast

sam!le means or a target "alue set 'or the !rocess%

A2 @ constant to !ro"ide threesigma limits 'or the sam!le mean%

The control limits for the (!chartare >CB@ !=Rand BCB@ !R

where

R@ a"erage o' se"eral !ast R"alues and the central line o' the chart%

!,!= @ constants that !ro"ide standard de"iations -threesigma

limits 'or a gi"en sam!le size%

H @H @

@


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Calculating

Three%Sigma *imits

Ta$le '()


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West #llis !ndustriesWest #llis !ndustries+ample 6,-+ample 6,-

*est Allisis concerned a)out their !roduction o' a s!ecial metal

screw used )y their largest customers% The diameter o' the

screw is critical% ?ata 'rom 'i"e sam!les is shown in the ta)le

)elow% 6am!le size is =% As the !rocess in statistical controlI


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West #llis !ndustries ControlChart $e"elopment

ample ample

+um$er ) , - . ( x) /(0/). /(0/,, /(0//1 /(0/,2 /(//)3 /(0/)3

, /(0/,) /(0/.) /(0/,. /(0/,/

- /(0/)3 /(0/,' /(0/-0 /(0/,-

. /(0//3 /(0/-. /(0/,. /(0/)0

0 /(0/.) /(0/0' /(0/-. /(0/-1

pecial 4etal crew

_

/(0/,2 5 /(0//16 /(//)3

7/(0/). 8 /(0/,,8

/(0//1 8 /(0/,29:. 6 /(0/)3

+ample 6,-


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ample ample

+um$er ) , - . ( x) /(0/). /(0/,, /(0//1 /(0/,2 /(//)3 /(0/)3

, /(0/,) /(0/.) /(0/,. /(0/,/ /(//,) /(0/,2

- /(0/)3 /(0/,' /(0/-0 /(0/,- /(//)2 /(0/,'

. /(0//3 /(0/-. /(0/,. /(0/)0 /(//,' /(0/,/

0 /(0/.) /(0/0' /(0/-. /(0/.2 /(//,, /(0/.0

R6 /(//,)

x6 /(0/,2

pecial 4etal crew

=

_

West #llis !ndustriesCompleted Control Chart $ata

+ample 6,-


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5actor 'or >CB 5actor 'or

5actor

6ize o' and BCB 'or BCB 'or >CB

'or

6am!le xCharts RCharts RCharts

-n -A2 -! -!=

2 1%880 0

%27

1%02 0

2%#7#

= 0%729 /

,(,3,

# 0%#77 0

2%11#

0%=8 02%00=

West #llis !ndustries(%chart Control Chart .actors

R6 /(//,)

D46 ,(,3,

D36 / ;C&R6D4R 6 ,(,3,7/(//,)9 6 /(//.21 in(

&C&R6D3R/7/(//,)9 6 / in(

+ample 6,-


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West #llis !ndustries (ange

Chart

+ample 6,-


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5actor 'or >CB 5actor 'or

5actor

6ize o' and BCB 'or BCB 'or >CB

'or

6am!le xCharts RCharts R

Charts

-n -A2 -! -!=

2 1%880 0

%27 1%02 0

2%#7#

= /(2,1 0

2%282

# 0%#77 0

2%11#

West #llis !ndustries+%chart Control Chart .actor

R 6 /(//,) A26 /(2,1 x6 /(0/,2@

;C&x6x+A2R6 /(0/,28 /(2,17/(//,)9 6 /(0/., in(

&C&x6x-A2R6 /(0/,25 /(2,17/(//,)9 6 /(0/), in(

=

=

+ample 6,-


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West #llis !ndustries

x!Chart

Sample the process .ind the assigna'le cause

liminate the pro'lem (epeat the cycle

+ample 6,-


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#pplication 6,-


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#pplication 6,-

708.0)38.0(864.14 === RDUCLR

052.0)38.0(136.03 === RDLCLR


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#pplication 6,-


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Sunny $ale /an&+ample 6,0

==;C&;C&xx==xx++zzxx

xx@@ 11 nn&C&&C&xx==xxzzxx==

unny Dale


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Control Charts

for #ttri'utes

p!chart+ chart used 'or controlling the

!ro!ortion o' de'ecti"e ser"ices or !roducts

generated )y the !rocess%

pp66 pp7) 57) 5 pp9911nnGhere

n@ sam!le size

p@ central line on the chart, which can )e either the historical

a"erage !o!ulation !ro!ortion de'ecti"e or a target "alue%

z @ normal de"iate -num)er o' standard de"iations 'rom the a"erage

Control limits are+ >CBp@pFzpp and BCBp@pJzpH H


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Hometown /an&Hometown /an&+ample 6,2+ample 6,2

The o!erations manager o' the )oo$ing ser"ices de!artment o'

3ometown se threesigma control limits%


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ample *rong Proportion

+um$er Account = Defective

) )0 /(//'

, ), /(//.3

- )1 /(//2' . , /(///3

0 )1 /(//2'

' . /(//)'

2 ,. /(//1'

3 2 /(//,3 1 )/ /(//.

)/ )2 /(//'3

)) )0 /(//'

), - /(//),

Total ).2

Hometown /an&Hometown /an&3sing a p%Chart to monitor a process3sing a p%Chart to monitor a process

n 4 ,0//

p6).2

),7,0//96 /(//.1

pp66 pp7) 57) 5pp9911nn

pp66 /(//.1/(//.17) 57) 5 /(//.1/(//.19:9:,0//,0//

pp6 /(//).6 /(//).

>CBp @ 0%00=9 F -0%001=

@ /(//1)BCBp @ 0%00=9 H -0%001=

@ /(///2


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Hometown /an&Hometown /an&3sing a p%Chart to monitor a process3sing a p%Chart to monitor a process

+ample 6,2


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#pplication 6,0


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#pplication 6,0

( ) 025.0

14420

72===

tubesofnumberTotal

tubesleakyofnumberTotalp

( ) ( )01301.0144

025.01025.01

=

=

= n

ppp

( ) 06403.001301.03025.0 =+=+= pp zpUCL

( ) 01403.001301.03025.0 === pp zpLCL

0=pLCL


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c!chart+ chart used 'or controlling the num)er o' de'ects whenmore than one de'ect can )e !resent in a ser"ice or !roduct%

The underlying sam!ling distri)ution 'or a cchart is the Poisson

distri)ution% The mean o' the distri)ution is cc

The standard de"iation is cc

use'ul tactic is to use the normal a!!roximation to the Poissonso that the central line o' the chart is ccand the control limits are

>CBc@ cFz c and BCBc@ cJz c

c%Charts


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Woodland Paper Company+ample 6,5

An the Goodland Pa!er Com!any(s 'inal ste! in their !a!er

!roduction !rocess, the !a!er !asses through a machine that

measures "arious !roduct &uality characteristics% Ghen the

!a!er !roduction !rocess is in control, it a"erages 20 de'ects

!er roll%

a 6et u! a control chart 'or the num)er o' de'ects !er roll% >se two

sigma control limits%

c "c " 20

z " #

>CBc@ cFz c " 20 F 2 20 @ 28%9=

BCBc@ cJz c " 20 2 20 @ 11%0

) 5i"e rolls had the 'ollowing num)er o' de'ects+ 1, 21, 17, 22, and 2=,

res!ecti"ely% The sixth roll, using !ul! 'rom a di''erent su!!lier, had #de'ects% As the !a!er !roduction !rocess in controlI


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Sol"er % c%Charts

6am!le Kum)er

Kum)ero'?e'ects

Woodland Paper Company3sing a c%Chart to monitor a process3sing a c%Chart to monitor a process

+ample 6,5


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#pplication 6,2

412

290561464056=

+++++++++++=c 24 ==c

( ) 8224 =+=+= cc zcUCL ( ) 0224 === cc zcLCL


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Process Capa'ility

Process capa$ilityis the a)ility o' the

!rocess to meet the design s!eci'ications

'or a ser"ice or !roduct%

+ominal valueis a target 'or design

s!eci'ications%

Toleranceis an allowance a)o"e or )elow

the nominal "alue%


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2020 2#2# 00 /inutes/inutes

>!!er>!!er

s!eci'ications!eci'ication

BowerBower


KominalKominal

"alue"alue

Process Capa'ility

Process is capa$le

Process distri)utionProcess distri)ution


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Process is not capa$leProcess is not capa$le

2020 2#2# 00 /inutes/inutes

>!!er>!!er


BowerBower


KominalKominal

"alue"alue

Process distri)utionProcess distri)ution

Process Capa'ility


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&ower&ower

specificationspecification

4ean4ean

;pper;pper

specificationspecification

+ominal value+ominal value

ix sigmaix sigma

>our sigma>our sigma

Two sigmaTwo sigma

ffects of (educing

)aria'ility on Process Capa'ility


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Cpk6 4inimum of;pper specification 5x

-

x5 &ower specification

-

?= =

Process Capa$ility Index? Cp&,is an index that measures the

!otential 'or a !rocess to generate de'ecti"e out!uts relati"e to

either u!!er or lower s!eci'ications%

Process Capa'ility !nde+ Cp&

Ge ta$e the minimum o' the two ratios )ecause it gi"es theworstcase situation%


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Process capa$ility ratio? Cp, is the tolerance width

di"ided )y standard de"iations -!rocess "aria)ility%

Process Capa'ility (atio Cp

CCpp66;pper specification ! &ower specification;pper specification ! &ower specification

''


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3sing Continuous !mpro"ement

to $etermine Process Capa'ility

tep )@Collect data on the !rocess out!ut: calculate

mean and standard de"iation o' the distri)ution%

tep ,@>se data 'rom the !rocess distri)ution to

com!ute !rocess control charts%

tep -@Ta$e a series o' random sam!les 'rom the

!rocess and !lot results on the control charts%

tep .@Calculate the !rocess ca!a)ility index, C!$, and

the !rocess ca!a)ility ratio, C!, i' necessary% A' results

are acce!ta)le, document any changes made to the!rocess and continue to monitor out!ut% A' the results

are unacce!ta)le, 'urther ex!lore assigna)le causes%

!ntensi"e Care *a'


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!ntensi"e Care *a'+ample 6,7

>!!er s!eci'ication @ 0 minutes>!!er s!eci'ication @ 0 minutesBower s!eci'ication @ 20 minutesBower s!eci'ication @ 20 minutes

"erage ser"ice @ 2%2 minutes"erage ser"ice @ 2%2 minutes

@ 1%# minutes@ 1%# minutes

The intensi"e care unit la) !rocess has an a"erage turnaround

time o' 2%2 minutes and a standard de"iation o' 1%# minutes%

The nominal "alue 'or this ser"ice is 2# minutes with an u!!er

s!eci'ication limit o' 0 minutes and a lower s!eci'ication limit

o' 20 minutes%

The administrator o' the la) wants to ha"e 'oursigma

!er'ormance 'or her la)% As the la) !rocess ca!a)le o' this le"el

o' !er'ormanceI

!ntensi"e Care *a'


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Cpk6 4inimum of;pper specification 5x

-

x5 &ower specification

-?

= =

>!!er s!eci'ication @ 0 minutes

Bower s!eci'ication @ 20 minutes

"erage ser"ice @ ,'(,minutes

@ )(-0minutes

!ntensi"e Care *a'#ssessing Process Capa'ility

CCpkpk66 4inimum of4inimum of,'(,,'(,5 ,/(/5 ,/(/

-7-7)(-0)(-099 ??

-/(/ 5-/(/ 5 ,'(,,'(,

-7-7)(-0)(-099

CCpkpk66 4inimum of )(0-? /(1.4inimum of )(0-? /(1. 6 /(1.6 /(1.Process

Capa$ility

Index

+ample 6,7


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Cp&66;pper specification ! &ower specification

'

Cpp66

-/ ! ,/

'7)(-09

6 )(,- Process Capa$ilityatio

!!er s!eci'ication @ 0%0 minutes Bower s!eci'ication @ 20%0 minutes

"erage ser"ice @ 2%1 minutes

@ 1%2 minutes Cpk

" )(/3 CCpp

" )(-1

!ntensi"e Care *a'#ssessing Process Capa'ility

$oes not meet 5

8-,22 Cpp9 target

+ample 6,7


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#pplication 6,5


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#pplication 6,5


59/66


Quality ngineering

Quality engineeringis an a!!roachoriginated )y *enichi Taguchi that in"ol"escom)ining engineering and statistical methods

to reduce costs and im!ro"e &uality )yo!timizing !roduct design and manu'acturing!rocesses%

Quality loss functionis the rationale that aser"ice or !roduct that )arely con'orms to the

s!eci'ications is more li$e a de'ecti"e ser"iceor !roduct than a !er'ect one%.uality loss 'unction is o!timum -zero when the

!roduct(s &uality measure is exactly on the targetmeasure%

T hi:


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Taguchi:sQuality *oss .unction

&oss

7dollars9

&oss

7dollars9

&ower&ower +ominal+ominal ;pper;pper

specificationspecification valuevalue specificationspecification


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Si+ Sigma

ix igmais a com!rehensi"e and 'lexi)le system'or achie"ing, sustaining, and maximizing )usinesssuccess )y minimizing de'ects and "aria)ility in!rocesses%

At relies hea"ily on the !rinci!les and tools o' T./%

At is dri"en )y a close understanding o' customerneeds: the disci!lined use o' 'acts, data, and

statistical analysis: and diligent attention tomanaging, im!ro"ing, and rein"enting )usiness!rocesses%

Si Sigma


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Si+ Sigma!mpro"ement Model

)( Define?etermine the current !rocesscharacteristics critical to customersatis'action and identi'y any ga!s%

,( 4easure.uanti'y the wor$ the !rocessdoes that a''ects the ga!%

-( Analy%e>se data on measures to !er'orm!rocess analysis%

.( Improve/odi'y or redesign existingmethods to meet the new !er'ormanceo)ecti"es%

0( Control/onitor the !rocess to ma$e surehigh !er'ormance le"els are maintained%

Si+ Sigma


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Si+ Sigma!mplementation

Top Down Commitment'rom cor!orateleaders%

4easurement ystems to Trac" Progress

Tough Boal ettingthrough )enchmar$ing)estinclass com!anies%

Education+ Em!loyees must )e trained inthe Dwhys and Dhowtos o' &uality%

Communication+ 6uccesses are asim!ortant to understanding as 'ailures%

Customer Priorities+ Ke"er lose sight o'the customer(s !riorities%


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Si+ Sigma ducation

Breen


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!nternational Quality!nternational Quality$ocumentation Standards$ocumentation Standards

!SO!SO

;


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1% Category 1 L Beadershi! 120 !oints2% Category 2 L6trategic Planning 8# !oints

% Category LCustomer and /ar$et 5ocus 8# !oints

=% Category = L/easurement, nalysis, and

Mnowledge /anagement 90 !oints

#% Category # L3uman esource 5ocus 8# !oints

% Category LProcess /anagement 8# !oints

Malcolm /aldrige =ationalQuality #ward

Kamed a'ter the late secretary o' commerce, a strong

!ro!onent o' enhancing &uality as a means o' reducing the

trade de'icit% The award !romotes, recognizes, and !u)licizes

&uality strategies and achie"ements%

Documents

Krajewski Chapter 06