heizer mod a

34
2008 Prentice Hall, Inc. A – 1 Operations Management Module A – Module A – Decision-Making Tools Decision-Making Tools PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer/Render Heizer/Render Principles of Operations Management, 7e Principles of Operations Management, 7e Operations Management, 9e Operations Management, 9e

Upload: rizwan-khurram

Post on 01-Nov-2014

1.138 views

Category:

Documents


21 download

DESCRIPTION

 

TRANSCRIPT

Page 1: Heizer mod a

© 2008 Prentice Hall, Inc. A – 1

Operations ManagementOperations ManagementModule A – Module A – Decision-Making ToolsDecision-Making Tools

PowerPoint presentation to accompany PowerPoint presentation to accompany Heizer/Render Heizer/Render Principles of Operations Management, 7ePrinciples of Operations Management, 7eOperations Management, 9e Operations Management, 9e

Page 2: Heizer mod a

© 2008 Prentice Hall, Inc. A – 2

OutlineOutline

The Decision Process in The Decision Process in OperationsOperations

Fundamentals of Decision MakingFundamentals of Decision Making

Decision TablesDecision Tables

Page 3: Heizer mod a

© 2008 Prentice Hall, Inc. A – 3

Outline – ContinuedOutline – Continued

Types of Decision-Making Types of Decision-Making EnvironmentsEnvironments Decision Making Under UncertaintyDecision Making Under Uncertainty

Decision Making Under RiskDecision Making Under Risk

Decision Making Under CertaintyDecision Making Under Certainty

Expected Value of Perfect Expected Value of Perfect Information (EVPI)Information (EVPI)

Page 4: Heizer mod a

© 2008 Prentice Hall, Inc. A – 4

Outline – ContinuedOutline – Continued

Decision TreesDecision Trees A More Complex Decision TreeA More Complex Decision Tree

Using Decision Trees in Ethical Using Decision Trees in Ethical Decision MakingDecision Making

Page 5: Heizer mod a

© 2008 Prentice Hall, Inc. A – 5

Learning ObjectivesLearning Objectives

When you complete this module you When you complete this module you should be able to:should be able to:

1.1. Create a simple decision treeCreate a simple decision tree

2.2. Build a decision tableBuild a decision table

3.3. Explain when to use each of the three Explain when to use each of the three types of decision-making types of decision-making environmentsenvironments

4.4. Calculate an expected monetary Calculate an expected monetary value (EMV)value (EMV)

Page 6: Heizer mod a

© 2008 Prentice Hall, Inc. A – 6

Learning ObjectivesLearning Objectives

When you complete this module you When you complete this module you should be able to:should be able to:

5.5. Compute the expected value of Compute the expected value of perfect information (EVPI)perfect information (EVPI)

6.6. Evaluate the nodes in a decision treeEvaluate the nodes in a decision tree

7.7. Create a decision tree with sequential Create a decision tree with sequential decisionsdecisions

Page 7: Heizer mod a

© 2008 Prentice Hall, Inc. A – 7

The Decision Process in The Decision Process in OperationsOperations

1.1. Clearly define the problems and the Clearly define the problems and the factors that influence itfactors that influence it

2.2. Develop specific and measurable Develop specific and measurable objectivesobjectives

3.3. Develop a modelDevelop a model

4.4. Evaluate each alternative solutionEvaluate each alternative solution

5.5. Select the best alternativeSelect the best alternative

6.6. Implement the decision and set a Implement the decision and set a timetable for completiontimetable for completion

Page 8: Heizer mod a

© 2008 Prentice Hall, Inc. A – 8

Fundamentals of Fundamentals of Decision MakingDecision Making

1.1. Terms:Terms:

a.a. AlternativeAlternative – a – a course of action or course of action or strategy that may be chosen by the strategy that may be chosen by the decision makerdecision maker

b.b. State of nature – an occurrence or State of nature – an occurrence or a situation over which the decision a situation over which the decision maker has little or no controlmaker has little or no control

Page 9: Heizer mod a

© 2008 Prentice Hall, Inc. A – 9

Fundamentals of Fundamentals of Decision MakingDecision Making

2.2. Symbols used in a decision tree:Symbols used in a decision tree:

.a.a – – decision node from which one decision node from which one of several alternatives may be of several alternatives may be selected selected

.b.b – – a state-of-nature node out of a state-of-nature node out of which one state of nature will occurwhich one state of nature will occur

Page 10: Heizer mod a

© 2008 Prentice Hall, Inc. A – 10

Decision Tree ExampleDecision Tree Example

Favorable marketFavorable market

Unfavorable marketUnfavorable market

Favorable marketFavorable market

Unfavorable marketUnfavorable market

Construct Construct small plantsmall plant

Do nothing

Do nothing

A decision nodeA decision node A state of nature nodeA state of nature node

Construct

Construct

large plant

large plant

Figure A.1Figure A.1

Page 11: Heizer mod a

© 2008 Prentice Hall, Inc. A – 11

Decision Table ExampleDecision Table Example

State of NatureState of Nature

AlternativesAlternatives Favorable MarketFavorable Market Unfavorable MarketUnfavorable Market

Construct large plantConstruct large plant $200,000$200,000 –$180,000–$180,000

Construct small plantConstruct small plant $100,000$100,000 –$ 20,000–$ 20,000

Do nothingDo nothing $ 0$ 0 $ 0 $ 0

Table A.1Table A.1

Page 12: Heizer mod a

© 2008 Prentice Hall, Inc. A – 12

Decision-Making Decision-Making EnvironmentsEnvironments

Decision making under uncertaintyDecision making under uncertainty Complete uncertainty as to which Complete uncertainty as to which

state of nature may occurstate of nature may occur

Decision making under riskDecision making under risk Several states of nature may occurSeveral states of nature may occur

Each has a probability of occurringEach has a probability of occurring

Decision making under certaintyDecision making under certainty State of nature is knownState of nature is known

Page 13: Heizer mod a

© 2008 Prentice Hall, Inc. A – 13

UncertaintyUncertainty

1.1. MaximaxMaximax Find the alternative that maximizes Find the alternative that maximizes

the maximum outcome for every the maximum outcome for every alternativealternative

Pick the outcome with the maximum Pick the outcome with the maximum numbernumber

Highest possible gainHighest possible gain

This is viewed as an optimistic This is viewed as an optimistic approachapproach

Page 14: Heizer mod a

© 2008 Prentice Hall, Inc. A – 14

UncertaintyUncertainty

2.2. MaximinMaximin Find the alternative that maximizes Find the alternative that maximizes

the minimum outcome for every the minimum outcome for every alternativealternative

Pick the outcome with the minimum Pick the outcome with the minimum numbernumber

Least possible lossLeast possible loss

This is viewed as a pessimistic This is viewed as a pessimistic approachapproach

Page 15: Heizer mod a

© 2008 Prentice Hall, Inc. A – 15

UncertaintyUncertainty

3.3. Equally likelyEqually likely Find the alternative with the highest Find the alternative with the highest

average outcomeaverage outcome

Pick the outcome with the maximum Pick the outcome with the maximum numbernumber

Assumes each state of nature is Assumes each state of nature is equally likely to occurequally likely to occur

Page 16: Heizer mod a

© 2008 Prentice Hall, Inc. A – 16

Uncertainty ExampleUncertainty Example

States of NatureStates of Nature

FavorableFavorable UnfavorableUnfavorable MaximumMaximum MinimumMinimum RowRowAlternativesAlternatives MarketMarket MarketMarket in Rowin Row in Rowin Row AverageAverage

ConstructConstruct large plantlarge plant $200,000$200,000 -$180,000-$180,000 $200,000$200,000 -$180,000-$180,000 $10,000$10,000

ConstructConstructsmall plantsmall plant $100,000$100,000 -$20,000 -$20,000 $100,000$100,000 -$20,000 -$20,000 $40,000$40,000

Do nothingDo nothing $0$0 $0$0 $0$0 $0$0 $0$0

1.1. Maximax choice is to construct a large plantMaximax choice is to construct a large plant2.2. Maximin choice is to do nothingMaximin choice is to do nothing3.3. Equally likely choice is to construct a small plantEqually likely choice is to construct a small plant

MaximaxMaximax MaximinMaximin Equally Equally likelylikely

Page 17: Heizer mod a

© 2008 Prentice Hall, Inc. A – 17

RiskRisk

Each possible state of nature has an Each possible state of nature has an assumed probabilityassumed probability

States of nature are mutually exclusiveStates of nature are mutually exclusive

Probabilities must sum to 1Probabilities must sum to 1

Determine the expected monetary value Determine the expected monetary value (EMV) for each alternative(EMV) for each alternative

Page 18: Heizer mod a

© 2008 Prentice Hall, Inc. A – 18

Expected Monetary ValueExpected Monetary Value

EMV (Alternative i) =EMV (Alternative i) = (Payoff of 1(Payoff of 1stst state of state of nature) x (Probability of 1nature) x (Probability of 1stst state of nature)state of nature)

++ (Payoff of 2(Payoff of 2ndnd state of state of nature) x (Probability of 2nature) x (Probability of 2ndnd state of nature)state of nature)

+…++…+ (Payoff of last state of (Payoff of last state of nature) x (Probability of nature) x (Probability of last state of nature)last state of nature)

Page 19: Heizer mod a

© 2008 Prentice Hall, Inc. A – 19

EMV ExampleEMV Example

1.1. EMV(EMV(AA11) = (.5)($200,000) + (.5)(-$180,000) = $10,000) = (.5)($200,000) + (.5)(-$180,000) = $10,000

2.2. EMV(EMV(AA22) = (.5)($100,000) + (.5)(-$20,000) = $40,000) = (.5)($100,000) + (.5)(-$20,000) = $40,000

3.3. EMV(EMV(AA33) = (.5)($0) + (.5)($0) = $0) = (.5)($0) + (.5)($0) = $0

States of NatureStates of Nature

FavorableFavorable UnfavorableUnfavorable Alternatives Alternatives Market Market MarketMarket

Construct large plant (A1)Construct large plant (A1) $200,000$200,000 -$180,000-$180,000

Construct small plant (A2)Construct small plant (A2) $100,000$100,000 -$20,000-$20,000

Do nothing (A3)Do nothing (A3) $0$0 $0$0

ProbabilitiesProbabilities .50.50 .50.50

Table A.3Table A.3

Page 20: Heizer mod a

© 2008 Prentice Hall, Inc. A – 20

EMV ExampleEMV Example

1.1. EMV(EMV(AA11) = (.5)($200,000) + (.5)(-$180,000) = $10,000) = (.5)($200,000) + (.5)(-$180,000) = $10,000

2.2. EMV(EMV(AA22) = (.5)($100,000) + (.5)(-$20,000) = $40,000) = (.5)($100,000) + (.5)(-$20,000) = $40,000

3.3. EMV(EMV(AA33) = (.5)($0) + (.5)($0) = $0) = (.5)($0) + (.5)($0) = $0

States of NatureStates of Nature

FavorableFavorable UnfavorableUnfavorable Alternatives Alternatives Market Market MarketMarket

Construct large plant (A1)Construct large plant (A1) $200,000$200,000 -$180,000-$180,000

Construct small plant (A2)Construct small plant (A2) $100,000$100,000 -$20,000-$20,000

Do nothing (A3)Do nothing (A3) $0$0 $0$0

ProbabilitiesProbabilities .50.50 .50.50

Best Option

Table A.3Table A.3

Page 21: Heizer mod a

© 2008 Prentice Hall, Inc. A – 21

CertaintyCertainty

Is the cost of perfect information Is the cost of perfect information worth it?worth it?

Determine the expected value of Determine the expected value of perfect information (EVPI)perfect information (EVPI)

Page 22: Heizer mod a

© 2008 Prentice Hall, Inc. A – 22

Expected Value of Expected Value of Perfect InformationPerfect Information

EVPI is the difference between the payoff EVPI is the difference between the payoff under certainty and the payoff under riskunder certainty and the payoff under risk

EVPI = –EVPI = –Expected value Expected value

with perfect with perfect informationinformation

Maximum Maximum EMVEMV

Expected value with Expected value with perfect information perfect information (EVwPI)(EVwPI)

== (Best outcome or consequence for 1(Best outcome or consequence for 1stst state state of nature) x (Probability of 1of nature) x (Probability of 1stst state of nature) state of nature)

++ Best outcome for 2Best outcome for 2ndnd state of nature) state of nature) x (Probability of 2x (Probability of 2ndnd state of nature) state of nature)

++ … … + Best outcome for last state of nature) + Best outcome for last state of nature) x (Probability of last state of nature)x (Probability of last state of nature)

Page 23: Heizer mod a

© 2008 Prentice Hall, Inc. A – 23

EVPI ExampleEVPI Example

1.1. The best outcome for the state of nature The best outcome for the state of nature “favorable market” is “build a large “favorable market” is “build a large facility” with a payoff of facility” with a payoff of $200,000$200,000. The . The best outcome for “unfavorable” is “do best outcome for “unfavorable” is “do nothing” with a payoff of nothing” with a payoff of $0$0..

Expected value Expected value with perfect with perfect informationinformation

= ($200,000)(.50) + ($0)(.50) = $100,000= ($200,000)(.50) + ($0)(.50) = $100,000

Page 24: Heizer mod a

© 2008 Prentice Hall, Inc. A – 24

EVPI ExampleEVPI Example

2.2. The maximum EMV is The maximum EMV is $40,000$40,000, which is , which is the expected outcome without perfect the expected outcome without perfect information. Thus:information. Thus:

= $100,000 – $40,000 = $60,000= $100,000 – $40,000 = $60,000

EVPI = EVwPI –EVPI = EVwPI – Maximum Maximum EMVEMV

The most the company should pay for The most the company should pay for perfect information is perfect information is $60,000$60,000

Page 25: Heizer mod a

© 2008 Prentice Hall, Inc. A – 25

Decision TreesDecision Trees

Information in decision tables can be Information in decision tables can be displayed as decision treesdisplayed as decision trees

A decision tree is a graphic display of the A decision tree is a graphic display of the decision process that indicates decision decision process that indicates decision alternatives, states of nature and their alternatives, states of nature and their respective probabilities, and payoffs for respective probabilities, and payoffs for each combination of decision alternative each combination of decision alternative and state of natureand state of nature

Appropriate for showing sequential Appropriate for showing sequential decisionsdecisions

Page 26: Heizer mod a

© 2008 Prentice Hall, Inc. A – 26

Decision TreesDecision Trees

Page 27: Heizer mod a

© 2008 Prentice Hall, Inc. A – 27

Decision TreesDecision Trees

1.1. Define the problemDefine the problem

2.2. Structure or draw the decision treeStructure or draw the decision tree

3.3. Assign probabilities to the states of Assign probabilities to the states of naturenature

4.4. Estimate payoffs for each possible Estimate payoffs for each possible combination of decision alternatives and combination of decision alternatives and states of naturestates of nature

5.5. Solve the problem by working backward Solve the problem by working backward through the tree computing the EMV for through the tree computing the EMV for each state-of-nature nodeeach state-of-nature node

Page 28: Heizer mod a

© 2008 Prentice Hall, Inc. A – 28

Decision Tree ExampleDecision Tree Example

= (.5)($200,000) + (.5)(-$180,000)= (.5)($200,000) + (.5)(-$180,000)EMV for node 1= $10,000

EMV for node 2= $40,000 = (.5)($100,000) + (.5)(-$20,000)= (.5)($100,000) + (.5)(-$20,000)

PayoffsPayoffs

$200,000$200,000

-$180,000-$180,000

$100,000$100,000

-$20,000-$20,000

$0$0

Construct la

rge plant

Construct la

rge plant

Construct Construct

small plantsmall plantDo nothing

Do nothing

Favorable market Favorable market (.5)(.5)

Unfavorable market Unfavorable market (.5)(.5)1

Favorable market Favorable market (.5)(.5)

Unfavorable market Unfavorable market (.5)(.5)2

Figure A.2Figure A.2

Page 29: Heizer mod a

© 2008 Prentice Hall, Inc. A – 29

Complex Complex Decision Decision

Tree Tree ExampleExample

Figure A.3Figure A.3

Page 30: Heizer mod a

© 2008 Prentice Hall, Inc. A – 30

Complex ExampleComplex Example

1.1. Given favorable survey resultsGiven favorable survey results

EMV(2) = (.78)($190,000) + (.22)(-$190,000) = $106,400EMV(2) = (.78)($190,000) + (.22)(-$190,000) = $106,400EMV(3) = (.78)($90,000) + (.22)(-$30,000) = $63,600EMV(3) = (.78)($90,000) + (.22)(-$30,000) = $63,600

The EMV for no plant The EMV for no plant = -$10,000= -$10,000 so, so, if the survey results are favorable, if the survey results are favorable, build the large plantbuild the large plant

Page 31: Heizer mod a

© 2008 Prentice Hall, Inc. A – 31

Complex ExampleComplex Example

2.2. Given negative survey resultsGiven negative survey results

EMV(4) = (.27)($190,000) + (.73)(-$190,000) = -$87,400EMV(4) = (.27)($190,000) + (.73)(-$190,000) = -$87,400EMV(5) = (.27)($90,000) + (.73)(-$30,000) = $2,400EMV(5) = (.27)($90,000) + (.73)(-$30,000) = $2,400

The EMV for no plant The EMV for no plant = -$10,000= -$10,000 so, so, if the survey results are negative, if the survey results are negative, build the small plantbuild the small plant

Page 32: Heizer mod a

© 2008 Prentice Hall, Inc. A – 32

Complex ExampleComplex Example

3.3. Compute the expected value of the Compute the expected value of the market surveymarket survey

EMV(1) = (.45)($106,400) + (.55)($2,400) = $49,200EMV(1) = (.45)($106,400) + (.55)($2,400) = $49,200

The EMV for no plant The EMV for no plant = $0= $0 so, given so, given no survey, build the small plantno survey, build the small plant

4.4. If the market survey is not conductedIf the market survey is not conducted

EMV(6) = (.5)($200,000) + (.5)(-$180,000) = $10,000EMV(6) = (.5)($200,000) + (.5)(-$180,000) = $10,000EMV(7) = (.5)($100,000) + (.5)(-$20,000) = $40,000EMV(7) = (.5)($100,000) + (.5)(-$20,000) = $40,000

Page 33: Heizer mod a

© 2008 Prentice Hall, Inc. A – 33

Decision Trees in Ethical Decision Trees in Ethical Decision MakingDecision Making

Maximize shareholder value and Maximize shareholder value and behave ethicallybehave ethically

Technique can be applied to any Technique can be applied to any action a company contemplatesaction a company contemplates

Page 34: Heizer mod a

© 2008 Prentice Hall, Inc. A – 34

YesYes

NoNo

YesYes

NoNo

Decision Trees in Ethical Decision Trees in Ethical Decision MakingDecision Making

YesYes

Is it ethical? (Weigh the affect on employees, customers, suppliers,

community verses shareholder benefit)

NoNoIs it ethical not to take

action? (Weigh the harm to shareholders

verses benefits to other stakeholders)NoNo

YesYes

Does action maximize company returns?

Is action legal?

Figure A.4Figure A.4

Do itDo it

Don’t Don’t do itdo it

Don’t Don’t do itdo it

Do it, Do it, but notify but notify appropriate appropriate partiesparties

Don’t Don’t do itdo it

Action outcomeAction outcome