© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-1

Operations Operations ManagementManagement

Decision-Making ToolsDecision-Making ToolsModule AModule A

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-2

Steps to Good DecisionsSteps to Good Decisions

Define problem and influencing factors Establish decision criteria Select decision-making tool (model) Identify and evaluate alternatives using

decision-making tool (model) Select best alternative Implement decision Evaluate the outcome

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-3

ModelsModels Are less expensive and disruptive than experimenting

with the real world system Allow operations managers to ask “What if” types of

questions Are built for management problems and encourage

management input Force a consistent and systematic approach to the

analysis of problems Require managers to be specific about constraints and

goals relating to a problem Help reduce the time needed in decision making

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-4

Limitations of ModelsLimitations of Models

They may be expensive and time-consuming to develop

and test are often misused and misunderstood (and feared)

because of their mathematical and logical complexity tend to downplay the role and value of

non-quantifiable information often have assumptions that oversimplify the

variables of the real world

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-5

The Decision-Making ProcessThe Decision-Making Process

Problem Decision

Quantitative Analysis

LogicHistorical DataMarketing ResearchScientific AnalysisModeling

Qualitative Analysis

EmotionsIntuitionPersonal Experience and MotivationRumors

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-6

Decision-Making ToolsDecision-Making Tools

Decision Trees Decision Tables

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-7

Getz Products Decision TreeGetz Products Decision Tree

1

2Unfavorable market

Unfavorable market

Favorable market

Favorable market

Construct small plant

Construct

large plant

Do nothing

A decision node

A state of nature node

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-8

Getz Products Decision TableGetz Products Decision Table

States of Nature

Alternatives Favorablemarket

Unfavorablemarket

Large plant $200,000 – $180,000

Small plant $100,000 – $20,000

Do nothing $0 $0

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-9

Three Types of Decision ModelsThree Types of Decision Models

Decision making under uncertainty Decision making under risk Decision making under certainty

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-10

Decision Making Under UncertaintyDecision Making Under Uncertainty

Maximax – Choose the alternative that maximizes the maximum outcome for every alternative (Optimistic criterion)

Maximin – Choose the alternative that maximizes the minimum outcome for every alternative (Pessimistic criterion)

Equally likely – chose the alternative with the highest average outcome.

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-11

Decision Making Under UncertaintyDecision Making Under Uncertainty

States of Nature Alternatives Favorable

MarketUnfavorable

MarketMaximum

in Row Minimum in Row

Row Average

Construct large plant

$200,000 – $180,000 $200,000 – $180,000 $10,000

Construct small plant

$100,000 – $20,000 $100,000 – $20,000 $40,000

$0 $0 $0 $0 $0

MaximaxMaximin

Equally likely

Do nothing

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-12

Probabilistic decision situation States of nature have probabilities of occurrence Select alternative with largest Expected Monetary Value

– EMV is the average return for the alternative if the decision were repeated many times

Decision Making Under RiskDecision Making Under Risk

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-13

Decision Making Under RiskDecision Making Under Risk

States of NatureAlternatives Favorable

MarketP(0.6)

Unfavorable

Construct

Constructsmall plant

Do nothing $0 $0 $0

large plant$200,000

$100,000

– $180,000

– $20,000

$48,000

$52,000

ExpectedvalueMarket

P(0.4)

Best choice

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-14

Decision Making Under CertaintyDecision Making Under Certainty

Expected Value of Perfect Information places an upper bound on what one would pay for additional information

Expected Value of Perfect Information is the difference between the payoff under certainty and the payoff under risk

Payoff under Certainty = ($200,000 x 0.6) + ($0 x 0.4)

Payoff under Risk = $52,000

EVPI = $120,000 – $52,000 = $68,000

States of NatureAlternatives Favorable

MarketP(0.6)

Unfavorable

small plant

Do nothing $0 $0 $0

large plant $200,000

$100,000

– $180,000

– $20,000

$48,000

$52,000

ExpectedvalueMarket

P(0.4)

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-15

Graphical display of decision process Used for solving problems with sequential decisions

and states of nature Expected Monetary Value (EMV) is most often used

Decision TreesDecision Trees

State 1

State 2

State 1

State 2

Alternative 1

Alternative 2

Outcome 1Outcome 1

Outcome 2Outcome 2

Outcome 3Outcome 3

Outcome 4Outcome 4

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-16

Getz Products Decision TreeGetz Products Decision Tree

Payoffs

$200,000

– $180,000

$100,000

– $20,000

0

2Unfavorable market (0.4)

Unfavorable market (0.4)

Favorable market (0.6)

Favorable market (0.6)

Construct small plant

Construct

large plant

Do nothing EMV for node 2 = $52,000

EMV for node 1 = $48,000

1

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-17

States of NatureAlternatives Favorable

MarketP(0.6)

Unfavorable

small plant

Do nothing $0 $0 $0

large plant $200,000

$100,000

– $180,000

– $20,000

$48,000

$52,000

ExpectedvalueMarket

P(0.4)

Payoffs

$200,000

– $180,000

$100,000

– $20,000

0

2Unfavorable market (0.4)

Unfavorable market (0.4)

Favorable market (0.6)

Favorable market (0.6)

small plant

large

plant

Do nothing EMV for node 2 = $52,000

EMV for node 1 = $48,000

1

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-18

Getz Products Decision Tree with Getz Products Decision Tree with Probabilities and EMVs ShownProbabilities and EMVs Shown

1 4

7

$49,

200

$106

,400

$40,

000

$2,4

00

2

3

5

6

$190,000

-$190,000

$90,000

$30,000

-$10,000$190,000

-$190,000

$90,000

$30,000

-$10,000

$200,000

-$180,000

$100,000

$20,000$0

Surv

ey

No survey

Large plant

Small plantNo plant

Large plant

Small plantNo plant

Large plant

Small plantNo plant

Fav. Mkt (0.78)

Fav. Mkt (0.78)

Fav. Mkt (0.27)

Fav. Mkt (0.27)

Fav. Mkt (0.5)

Fav. Mkt (0.5)

Unfav. Mkt (0.22)

Unfav. Mkt (0.22)

Unfav. Mkt (0.73)

Unfav. Mkt (0.73)

Unfav. Mkt (0.5)

Unfav. Mkt (0.5)

$106,000

$63,600

-$87,400

$2,400

$10,000

$40,000

Sur. Res. Neg. (.55)

Sur. Res.

Pos. (.45)

1st decision point

2nd decision point

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458A-19

1. Decision alternatives

5. Define payoffs for each end point

2. States of Nature & probabilities

3. Subsequent alternatives

4. States of Nature & probabilities

9. Choose best alternative

6. Calculate (prob X payoff )

7. Choose best alternative

8. Calculate (prob X payoff )

N

N N

N

Decision Tree WorksheetDecision Tree Worksheet