krajewski chapter 06
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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
2007 Pearson Education
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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
s!eci'ications!eci'ication
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$le
2020 2#2# 00 /inutes/inutes
>!!er>!!er
s!eci'ications!eci'ication
BowerBower
s!eci'ications!eci'ication
KominalKominal
"alue"alue
Process distri)utionProcess distri)ution
Process Capa'ility
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&ower&ower
specificationspecification
4ean4ean
;pper;pper
specificationspecification
+ominal value+ominal value
ix sigmaix sigma
>our sigma>our sigma
Two sigmaTwo sigma
ffects of (educing
)aria'ility on Process Capa'ility
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Cpk6 4inimum of;pper specification 5x
-
x5 &ower specification
-
?= =
Process Capa$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
$oes not meet 5
8-,22 Cpp9 target
+ample 6,7
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#pplication 6,5
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#pplication 6,5
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Quality ngineering
Quality engineeringis an a!!roachoriginated )y *enichi Taguchi that in"ol"escom)ining engineering and statistical methods
to reduce costs and im!ro"e &uality )yo!timizing !roduct design and manu'acturing!rocesses%
Quality loss functionis the rationale that aser"ice or !roduct that )arely con'orms to the
s!eci'ications is more li$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%
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