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© 2011 Pearson Education, Inc. publishing as Prentice Hall A - 1
A
Decision-Making
Tools
PowerPoi
nt presentation to accompanyPowerPoint presentation to accompany
Heizer and RenderHeizer and Render
Operations Management, 10eOperations Management, 10e
Principles of Operations Management, 8ePrinciples of Operations Management, 8e
PowerPoint slides by eff Heyl
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OutlineOutline
"#e $ecision Process inOperations
%&ndamentals of $ecision Ma'ing $ecision "ables
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Outline – Continued Outline – Continued
"ypes of $ecision-Ma'ing)n*ironments
$ecision Ma'ing +nder +ncertainty $ecision Ma'ing +nder Ris'
$ecision Ma'ing +nder ertainty
)pected .al&e of Perfect/nformation ).P/
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Outline – Continued Outline – Continued
$ecision "rees
A More omple $ecision "ree
+sing $ecision "rees in )t#ical$ecision Ma'ing
"#e Po'er $ecision Problem
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Learning ObjectivesLearning Objectives
4#en yo& complete t#is mod&le yo&4#en yo& complete t#is mod&le yo&s#o&ld be able to5s#o&ld be able to5
16 reate a simple decision tree
!6 7&ild a decision table
(6 )plain w#en to &se eac# of t#e t#reetypes of decision-ma'ing
en*ironments
26 alc&late an epected monetary *al&e)M.
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Learning ObjectivesLearning Objectives
4#en yo& complete t#is mod&le yo&4#en yo& complete t#is mod&le yo&s#o&ld be able to5s#o&ld be able to5
36 omp&te t#e epected *al&e ofperfect information ).P/
6 )*al&ate t#e nodes in a decision tree
96 reate a decision tree wit# se:&entialdecisions
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Decision to Go All InDecision to Go All In
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The Decision Process inThe Decision Process in
OperationsOperations16 learly define t#e problems and t#e
factors t#at infl&ence it
!6 $e*elop specific and meas&rableob;ecti*es
(6 $e*elop a model
26 )*al&ate eac# alternati*e sol&tion36 <elect t#e best alternati*e
6 /mplement t#e decision and set atimetable for completion
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Fundamentals oFundamentals o
Decision !a"ing Decision !a"ing 16 "erms5
a6 Alternati*e > a co&rse of action orstrategy t#at may be c#osen by t#edecision ma'er
b6 <tate of nat&re > an occ&rrence or
a sit&ation o*er w#ic# t#e decisionma'er #as little or no control
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Fundamentals oFundamentals o
Decision !a"ing Decision !a"ing !6 <ymbols &sed in a decision tree5
> decision node from w#ic# oneof se*eral alternati*es may beselected
> a state-of-nat&re node o&t of
w#ic# one state of nat&re willocc&r
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Decision Tree #$ampleDecision Tree #$ample
%a*orable mar'et
+nfa*orable mar'et
%a*orable mar'et
+nfa*orable mar'et
onstr&ctsmall plant
$ o n o t # i n g
A decision node A state of nat&re node
, o n s t r &
c t
l a r g e
p l a n t
%ig&re A61
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Decision Table #$ampleDecision Table #$ample
"able A61
<tate of ?at&re
Alternati*es %a*orable Mar'et +nfa*orable Mar'et
onstr&ct large plant @!00,000 >@180,000onstr&ct small plant @100,000 >@ !0,000
$o not#ing @ 0 @ 0
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Decision%!a"ingDecision%!a"ing
#nvironments#nvironments $ecision ma'ing &nder &ncertainty
omplete &ncertainty as to w#ic#
state of nat&re may occ&r $ecision ma'ing &nder ris'
<e*eral states of nat&re may occ&r
)ac# #as a probability of occ&rring
$ecision ma'ing &nder certainty
<tate of nat&re is 'nown
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&ncertaint' &ncertaint'
16 Maima
%ind t#e alternati*e t#at maimizest#e maim&m o&tcome for e*ery
alternati*e
Pic' t#e o&tcome wit# t#e maim&mn&mber
Hig#est possible gain "#is is *iewed as an optimistic
approac#
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&ncertaint' &ncertaint'
!6 Maimin
%ind t#e alternati*e t#at maimizest#e minim&m o&tcome for e*eryalternati*e
Pic' t#e o&tcome wit# t#e minim&mn&mber
east possible loss "#is is *iewed as a pessimistic
approac#
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&ncertaint' &ncertaint'
(6 ):&ally li'ely
%ind t#e alternati*e wit# t#e #ig#est
a*erage o&tcome Pic' t#e o&tcome wit# t#e maim&m
n&mber
Ass&mes eac# state of nat&re ise:&ally li'ely to occ&r
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&ncertaint' #$ample&ncertaint' #$ample
16 Maima c#oice is to constr&ct a large plant!6 Maimin c#oice is to do not#ing(6 ):&ally li'ely c#oice is to constr&ct a small plant
Maima Maimin ):&ally
li'ely
<tates of ?at&re
%a*orable +nfa*orable Maim&m Minim&m RowAlternati*es Mar'et Mar'et in Row in Row A*erage
onstr&ct
large plant @!00,000 -@180,000 @!00,000 -@180,000 @10,000onstr&ct
small plant @100,000 -@!0,000 @100,000 -@!0,000 @20,000
$o not#ing @0 @0 @0 @0 @0
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(is" (is"
)ac# possible state of nat&re #as anass&med probability
<tates of nat&re are m&t&ally ecl&si*e Probabilities m&st s&m to 1
$etermine t#e epected monetary *al&e
)M. for eac# alternati*e
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#$pected !onetar' )alue#$pected !onetar' )alue
)M. Alternati*e i B Payoff of 1st state ofnat&re Probability of 1st state of nat&re
C Payoff of !nd state ofnat&re Probability of !nd state of nat&re
CDC Payoff of last state ofnat&re Probability oflast state of nat&re
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#!) #$ample#!) #$ample
16 )M. A1 B 63@!00,000 C 63-@180,000 B @10,000!6 )M. A! B 63@100,000 C 63-@!0,000 B @20,000
(6 )M. A( B 63@0 C 63@0 B @0
"able A6(
<tates of ?at&re
%a*orable +nfa*orable Alternati*es Mar'et Mar'et
onstr&ct large plant A1 @!00,000 -@180,000
onstr&ct small plant A! @100,000 -@!0,000
$o not#ing A( @0 @0
Probabilities 630 630
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#!) #$ample#!) #$ample
16 )M. A1 B 63@!00,000 C 63-@180,000 B @10,000!6 )M. A! B 63@100,000 C 63-@!0,000 B @20,000
(6 )M. A( B 63@0 C 63@0 B @07est Option
"able A6(
<tates of ?at&re
%a*orable +nfa*orable Alternati*es Mar'et Mar'et
onstr&ct large plant A1 @!00,000 -@180,000
onstr&ct small plant A! @100,000 -@!0,000
$o not#ing A( @0 @0
Probabilities 630 630
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A - !!
Certaint' Certaint'
/s t#e cost of perfect informationwort# itE
$etermine t#e epected *al&e ofperfect information ).P/
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A - !(
#$pected )alue o#$pected )alue o
Perect InormationPerect Inormation).P/ is t#e difference between t#e payoff&nder certainty and t#e payoff &nder ris'
).P/ B >)pected *al&e
wit# perfectinformation
Maim&m)M.
)pected *al&e wit#perfect information).wP/
B 7est o&tcome or conse:&ence for 1st stateof nat&re Probability of 1st state of nat&re
C 7est o&tcome for !nd state of nat&re
Probability of !nd state of nat&re
C D C 7est o&tcome for last state of nat&re
Probability of last state of nat&re
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A - !2
#)PI #$ample#)PI #$ample
16 "#e best o&tcome for t#e state of nat&reFfa*orable mar'etG is Fb&ild a largefacilityG wit# a payoff of @!00,0006 "#e
best o&tcome for F&nfa*orableG is Fdonot#ingG wit# a payoff of @06
)pected *al&ewit# perfectinformation
B @!00,000630 C @0630 B @100,000
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A - !3
#)PI #$ample#)PI #$ample
!6 "#e maim&m )M. is @20,000, w#ic# ist#e epected o&tcome wit#o&t perfectinformation6 "#&s5
B @100,000 > @20,000 B @0,000
).P/ B ).wP/ > Maim&m)M.
"#e most t#e company s#o&ld pay forperfect information is @0,000
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A - !
Decision TreesDecision Trees
/nformation in decision tables can bedisplayed as decision trees
A decision tree is a grap#ic display of t#e
decision process t#at indicates decisionalternati*es, states of nat&re and t#eirrespecti*e probabilities, and payoffs foreac# combination of decision alternati*e
and state of nat&re Appropriate for s#owing se:&ential
decisions
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A - !9
Decision TreesDecision Trees
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Decision TreesDecision Trees
16 $efine t#e problem
!6 <tr&ct&re or draw t#e decision tree
(6 Assign probabilities to t#e states of
nat&re26 )stimate payoffs for eac# possible
combination of decision alternati*es andstates of nat&re
36 <ol*e t#e problem by wor'ing bac'wardt#ro&g# t#e tree comp&ting t#e )M. foreac# state-of-nat&re node
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Decision Tree #$ampleDecision Tree #$ample
B 63@!00,000 C 63-@180,000)M. for node 1B @10,000
)M. for node !B @20,000
B 63@100,000 C 63-@!0,000
Payoffs
@!00,000
-@180,000
@100,000
-@!0,000
@0
, o n s t
r & c t l a
r g e p l a n t
onstr&ct
small plant$ o n o t # i n g
%a*orable mar'et 63
+nfa*orable mar'et 631
%a*orable mar'et 63
+nfa*orable mar'et 63!
%ig&re A6!
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Comple$Comple$
DecisionDecisionTreeTree#$ample#$ample
%ig&re A6(
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Comple$ #$ampleComple$ #$ample
16 i*en fa*orable s&r*ey res<s
)M.! B 698@1=0,000 C 6!!-@1=0,000 B @10,200
)M.( B 698@=0,000 C 6!!-@(0,000 B @(,00
"#e )M. for no plant B -@10,000 so,
if t#e s&r*ey res<s are fa*orable,b&ild t#e large plant
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Comple$ #$ampleComple$ #$ample
!6 i*en negati*e s&r*ey res<s
)M.2 B 6!9@1=0,000 C 69(-@1=0,000 B -@89,200
)M.3 B 6!9@=0,000 C 69(-@(0,000 B @!,200
"#e )M. for no plant B -@10,000 so,
if t#e s&r*ey res<s are negati*e,b&ild t#e small plant
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Comple$ #$ampleComple$ #$ample
(6 omp&te t#e epected *al&e of t#emar'et s&r*ey
)M.1 B 623@10,200 C 633@!,200 B @2=,!00
"#e )M. for no plant B @0 so, gi*enno s&r*ey, b&ild t#e small plant
26 /f t#e mar'et s&r*ey is not cond&cted
)M. B 63@!00,000 C 63-@180,000 B @10,000
)M.9 B 63@100,000 C 63-@!0,000 B @20,000
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Decision Trees in #thicalDecision Trees in #thical
Decision !a"ing Decision !a"ing
Maimize s#are#older *al&e and
be#a*e et#ically "ec#ni:&e can be applied to any
action a company contemplates
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Ies
?o
Ies
?o
Decision Trees in #thicalDecision Trees in #thical
Decision !a"ing Decision !a"ing
Ies
/s it et#icalE 4eig# t#eaffect on employees,c&stomers, s&ppliers,
comm&nity *erses
s#are#older benefit
?o/s it et#ical not to ta'e
actionE 4eig# t#e
#arm to s#are#olders*erses benefits to ot#ersta'e#olders
?o
Ies
$oes actionmaimizecompanyret&rnsE
/sactionlegalE
%ig&re A62
$o it
$onJtdo it
$onJtdo it
$o it,b&t notifyappropriateparties
$onJtdo it
Action o&tcome
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The Po"er Design ProcessThe Po"er Design Process
/f "6 6 folds,
/f "6 6 calls,
)M. B 680@==,000
B @9=,!00
)M. B 6!0K623@83(,000 - P#illipsJ bet of @2!!,000L
B 6!0K@(8(,830 - @2!!,000L
B 6!0K-@(8,130L B -@9,(0
O*erall )M. B @9=,!00 - @9,(0 B @91,930
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A (9
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