Chapter 1, Heizer/Render, 5th edition

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Transcript Chapter 1, Heizer/Render, 5th edition

Operations Management

Decision-Making Tools Module A

Outline



The Decision Process in Operations



Fundamentals of Decision Making



Decision Tables



Decision Making under Uncertainty



Decision Making Under Risk



Decision Making under Certainty



Expected Value of Perfect Information (EVPI)



Decision Trees



A More Complex Decision Tree

Learning Objectives

When you complete this chapter, you should be able to :



Identify or Define



Decision trees and decision tables

 

Highest monetary value Expected value of perfect information

 

Describe or Explain :



Sequential decisions

Decision making under risk

Models, and the Techniques of Scientific Management



Can Help Managers To:



Gain deeper insight into the nature of business relationships

 

Find better ways to assess values in such relationships; and See a way of reducing, or at least understanding, uncertainty that surrounds business plans and actions

Steps 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

Models

     

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

Limitations 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 nonquantifiable information



often have assumptions that oversimplify the variables of the real world

The Decision-Making Process

Problem

Quantitative Analysis Logic Historical Data Marketing Research Scientific Analysis Modeling

Decision PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)

Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors

Ways of Displaying a Decision Problem



Decision trees



Decision tables Out comes States of Nature Alternatives Decision Problem

Fundamentals of Decision Theory

The three types of decision models:

  

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

Fundamentals of Decision Theory - continued

Terms:

 

Alternative : course of action or choice State of nature : an occurrence over which the decision maker has no control Symbols used in decision tree:



A decision node from which one of several alternatives may be selected



A state of nature node nature will occur out of which one state of

Decision Table

Alternatives Alternative 1 Alternative 2 States of Nature State 1 State 2 Outcome 1 Outcome 3 Outcome 2 Outcome 4

Decision 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.

Example - Decision Making Under Uncertainty

Alternatives Construct large plant Construct small plant

States of Nature

Favorable Market $200,000 Unfavorable Market Maximum in Row Minimum in Row Row Average -$180,000 $200,000 -$180,000 $10,000 $100,000 -$20,000 $100,000 -$20,000 $40,000 $0 $0 Maximax $0 Maximin $0 Equally likely $0 PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) A-14 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

Decision Making Under Risk



Probabilistic decision situation



States of nature have probabilities of occurrence



Select alternative with largest expected monetary value (EMV)



EMV = Average return for alternative if decision were repeated many times

Expected Monetary Value Equation

) = =

N  = 1 Number of states of nature

Value of Payoff Probability of payoff

+

V N

)

Example - Decision Making Under Uncertainty

Alternatives

States of Nature Favorable Market P(0.5) $200,000 Unfavorable Market P(0.5) -$180,000

Construct large plant Construct small plant

Do nothing $100,000 $0 -$20,000 $0 Expected value $10,000 $40,000 Best choice $0 PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) A-17 © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458

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



EVPI

places an upper bound on what one would pay for additional information



EVPI

is the expected value with perfect information minus the maximum EMV

**Expected Value With Perfect Information (EV|PI)**

EV | PI

j n

 = 1

(Best outcome for the state of nature j) * P(S j ) where j=1 to the number of states of nature, n

Expected Value of Perfect Information



EVPI = EV|PI - maximum EMV

Expected Value of Perfect Information

Alternative State of Nature Favorable Market ($) Unfavorable Market ($) 200,000 -$180,000 Construct a large plant Construct a small plant Do nothing Probabilities $100,000 0.50

$0 $20,000 $0 0.50

EMV $20,000 $40,000 $0

Expected Value of Perfect Information

EVPI = expected value with perfect information max(EMV) = $200,000*0.50 + 0*0.50

$40,000 = $60,000

Expected Opportunity Loss



EOL is the cost of not picking the best solution



EOL = Expected Regret

Computing EOL - The Opportunity Loss Table

Alternative Large Plant Small Plant Do Nothing Probabilities State of Nature Favorable Market ($) 200,000 - 200,000 200,000 - 100,000 200,000 - 0 0.50

Unfavorable Market ($) 0 - (-180,000) 0 -(-20,000) 0-0 0.50

The Opportunity Loss Table continued

Alternative Large Plant Small Plant Do Nothing Probabilities State of Nature Favorable Market ($) Unfavorable Market ($) 0 100,000 180,000 20,000 200,000 0 0.50

0.50

The Opportunity Loss Table continued

Alternative Large Plant Small Plant Do Nothing (0.50)*$0 + (0.50)*($180,000) (0.50)*($100,000) + (0.50)(*$20,000) (0.50)*($200,000) + (0.50)*($0) EOL $90,000 $60,000 $100,000

Sensitivity Analysis

EMV(Large Plant) = $200,000P - (1-P)$180,000 EMV(Small Plant) = $100,000P - $20,000(1-P) EMV(Do Nothing) = $0P + 0(1-P)

Sensitivity Analysis - continued

250000 200000 150000 100000 50000 0 -50000 0 -100000 -150000 -200000 Point 1 0.2

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render)

0.4

Point 2 0.6

0.8

1 Values of P

Decision Trees



Graphical display of decision process

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Used for solving problems

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With 1 set of alternatives and states of nature, decision tables can be used also

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With several sets of alternatives and states of nature (sequential decisions), decision tables cannot be used



EMV is criterion most often used

Analyzing Problems with Decision Trees

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Define the problem

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Structure or draw the decision tree

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Assign probabilities to the states of nature

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Estimate payoffs for each possible combination of alternatives and states of nature

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Solve the problem by computing expected monetary values for each state-of-nature node

Decision Tree

1 State 1 State 2 Outcome 1 Outcome 2 Decision Node PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) 2 State 1 State 2 Outcome 3 Outcome 4 State of Nature Node © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458 A-31