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
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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
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)
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
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
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)
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)
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
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:
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
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
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
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