heizer om10 moda

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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



© 2011 PearsonA - !

OutlineOutline

"#e $ecision Process inOperations

%&ndamentals of $ecision Ma'ing $ecision "ables



© 2011 PearsonA - (

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/



© 2011 PearsonA - 2

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



© 2011 PearsonA - 3

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.



© 2011 PearsonA -

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



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



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



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



&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#




!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#




(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



&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



(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/



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



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



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



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



A - !9




© 2011 PearsonA - !8


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



© 2011 PearsonA - !=

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!




Comple$ #$ampleComple$ #$ample

16 i*en fa*orable s&r*ey res&lts

)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&lts are fa*orable,b&ild t#e large plant



© 2011 PearsonA - (!


!6 i*en negati*e s&r*ey res&lts

)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&lts are negati*e,b&ild t#e small plant



© 2011 PearsonA - ((


(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




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




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



© 2011 PearsonA - (

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



A (9

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