Operations Management DecisionMaking Tools Module A Power Point

Operations Management Decision-Making Tools Module A Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 1 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

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 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 2 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

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 ¨ Sequential decisions ¨ ¨ Describe or Explain: ¨ Decision making under risk Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 3 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Models, and the Techniques of Scientific Management ¨ Can Help Managers To: 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 ¨ Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 4 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

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 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 5 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

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 6 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

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 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 7 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

The Decision-Making Process Quantitative Analysis Problem Logic Historical Data Marketing Research Scientific Analysis Modeling Decision Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 8 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Ways of Displaying a Decision Problem ¨ Decision trees ¨ Decision tables Outcomes States of Nature Alternatives Decision Problem Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 9 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Fundamentals of Decision Theory The three types of decision models: ¨ Decision making under uncertainty ¨ Decision making under risk ¨ Decision making under certainty Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 10 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Terms: Fundamentals of Decision Theory continued ¨ Alternative: course of action or choice ¨ State of nature: an occurrence over which the decision maker has no control Symbols used in decision tree: o A decision node from which one of several alternatives may be selected ¡ A state of nature node out of which one state of nature will occur Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 11 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Decision Table States of Nature Alternatives State 1 State 2 Alternative 1 Outcome 2 Alternative 2 Outcome 3 Outcome 4 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 12 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

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 13 with the highest average outcome. Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Example - Decision Making Under Uncertainty Maxima x Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 14 Maximin Equally likely © 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 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 15 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Expected Monetary Value Equation N Number of states of nature Value of Payoff EMV ( Ai ) = Vi *P(Vi ) Probability of payoff i =1 = V 1 *P(V 1 ) + V 2 * P(V 2 )+. . . +VN * P(VN ) Alternative i Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 16 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Example - Decision Making Under Uncertainty Best choice Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 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 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 18 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Expected Value With Perfect Information (EV|PI ) ( n EV| PI = å (Best outcome for the state of j =1 nature j) * P(Sj ) where j=1 to the number of states of nature, n Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 19 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Expected Value of Perfect Information ¨ EVPI = EV|PI - maximum EMV Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 20 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Expected Value of Perfect Information Favorable Unfavorabl Market ($)e Market ($) Construct a large plant Construct a small plant Do nothing 200, 000 $100, 0 00 $0 0. 50 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 21 EMV -$180, 000 $20, 00 0 $0 $40, 000 $0 0. 50 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Expected Value of Perfect Information EVPI = expected value with perfect information - max(EMV) EMV = $200, 000*0. 50 + 0*0. 50 $40, 000 = $60, 000 Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 22 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Expected Opportunity Loss ¨ EOL is the cost of not picking the best solution ¨ EOL = Expected Regret Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 23 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Computing EOL - The Opportunity Loss Table Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 24 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

The Opportunity Loss Table continued Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 25 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

The Opportunity Loss Table continued Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 26 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Sensitivity Analysis EMV(Large Plant) = $200, 000 P - (1 P)$180, 000 EMV(Small Plant) = $100, 000 P - $20, 000(1 P) EMV(Do Nothing) = $0 P + 0(1 -P) 1 -P Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 27 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Sensitivity Analysis continued ) EMV nt a l P l l Sma ( t) n la P e arg L ( V EM Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 28 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Decision Trees ¨ Graphical display of decision process ¨ Used for solving problems With 1 set of alternatives and states of nature, decision tables can be used also ¨ With several sets of alternatives and states of nature (sequential decisions), decision tables cannot be used ¨ ¨ EMV is criterion most often used Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) 29 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Analyzing Problems with Decision Trees ¨ Define the problem ¨ Structure or draw the decision tree ¨ Assign probabilities to the states of nature ¨ Estimate payoffs for each possible combination of alternatives and states of nature ¨ Solve the problem by computing expected monetary values for each 30 state-of-nature node Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458

Decision Tree State 1 1 e v ati rn e t Al Alt ern ativ e 2 1 State 2 State 1 2 Decision Node Power. Point presentation to accompany Operations Management, 6 E (Heizer & Render) State 2 Outcome 1 Outcome 2 Outcome 3 Outcome 4 State of Nature Node 31 © 2001 by Prentice Hall, Inc. , Upper Saddle River, N. J. 07458