Operations Management Module A – Decision-Making Tools PowerPoint presentation for Operations Management Class Updated and extended by Prof. Dedeke © 2006 Prentice Hall, Inc.

Outline Fundamentals of Decision Making Decision Tables Types of Decision-Making Environments Decision Making Under Uncertainty Decision Making Under Risk Decision Making Under Certainty Expected Value of Perfect Information (EVPI)

Introduction Decision Making Approaches Structured Unstructured

Structured Decision Making Process Clearly define the problem and the factors that influence it Develop the specific goals to be achieved Develop quantitative measures that relate the goals to the problem Develop alternate solutions to problem Compare the alternate solutions using a model or structured methodology and the quantitative measures from step 3 Select the best alternative Implement the decision and set a timetable for completion This slide provides some reasons that capacity is an issue. The following slides guide a discussion of capacity.

Decision Making Environment Level of confidence about occurrence of outcomes and consequences Decisions under risk Decisions under certainty Total Decisions under uncertainty Decisions under risk This slide provides some reasons that capacity is an issue. The following slides guide a discussion of capacity. Partial Value or Size of outcomes and consequences Estimate-able Known

Decision-Making Environments Decision making under uncertainty Complete uncertainty as to which state of nature may occur Decision making under risk Several states of nature may occur Each has a probability of occurring Decision making under certainty State of nature is known

Decision Making Under Certainty States of Nature Favorable Unfavorable Alternatives Market Market Construct large plant (A1) $200,000 -$180,000 Construct small plant (A2) $100,000 -$90,000 Do nothing (A3) $0 $0 Probabilities 1 0 From Table A.3 EMV(A1) = (1)($200,000) + (0)(-$180,000) = $200,000 EMV(A2) = (1)($100,000) + (0)(-$90,000) = $100,000 The preferable option is A1

Decision Making Under Risk States of Nature Favorable Unfavorable Alternatives Market Market Construct large plant (A1) $200,000 -$180,000 Construct small plant (A2) $100,000 -$90,000 Do nothing (A3) $0 $0 Probabilities 0.3 0.7 From Table A.3 EMV(A1) = (0.3)($200,000) + (0.7)(-$180,000) = -$66,000 EMV(A2) = (0.3)($100,000) + (0.7)(-$90,000) = -$33,000 EMV(A3) = (0.3)($0) + (0.7)($0) = $0 A3 is the option to choose. If A3 is excluded, The preferable option is A2

Decision Making Under Risk (2) In some cases the states of nature expected are certain, however the values of each states are uncertain. States of Demand Seasonal Ticket Occasional Alternatives Prob. Market Prob. Market Sell 100 tickets now (A1) 0.7 $200,000 0.3 $50,000 Sell 100 tickets later (A2) 0.3 $150,000 0.7 $300,000 Do nothing (A3) $0 $0 EMV(A1) = (0.7)($200,000) + (0.3)($50,000) = $155,000 EMV(A2) = (0.3)($150,000) + (0.7)($300,000) = $255,000 EMV(A3) = (0)($0) + (0)($0) = $0 The preferable option is A2

Risk Each possible state of nature has an assumed probability States of nature are mutually exclusive Probabilities must sum to 1 Determine the expected monetary value (EMV) for each alternative

Expected Monetary Value EMV (Alternative i) = (Payoff of 1st state of nature) x (Probability of 1st state of nature) + (Payoff of 2nd state of nature) x (Probability of 2nd state of nature) +…+ (Payoff of last state of nature) x (Probability of last state of nature)

Decision Making Under Uncertainty States of Nature Favorable Unfavorable Maximum Minimum Row Alternatives Market Market in Row in 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 Do nothing $0 $0 $0 $0 $0 Maximax Equally likely Maximin Maximax choice is to construct a large plant Maximin choice is to do nothing Equally likely choice is to construct a small plant

Using Decision Trees to Solve Decision Making Under Risk Symbols used in a decision tree: —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

Decision Tree Example A decision node A state of nature node Favorable market Unfavorable market Construct large plant Favorable market Unfavorable market Construct small plant Do nothing Figure A.1

Decision Table Example State of Nature Alternatives Favorable Market Unfavorable Market Construct large plant $200,000 –$180,000 Construct small plant $100,000 –$ 20,000 Do nothing $ 0 $ 0 Table A.1

Decision Tree Example EMV for node 1 = (.5)($200,000) + (.5)(-$180,000) EMV for node 1 = $10,000 Payoffs $200,000 -$180,000 $100,000 -$20,000 $0 Favorable market (.5) Unfavorable market (.5) 1 Construct large plant Construct small plant Do nothing Favorable market (.5) Unfavorable market (.5) 2 EMV for node 2 = $40,000 = (.5)($100,000) + (.5)(-$20,000) Figure A.2

Complex Decision Tree Example Figure A.3

Decision Trees in Ethical Decision Making Maximize shareholder value and behave ethically Technique can be applied to any action a company contemplates

Decision Trees in Ethical Decision Making Yes No Yes Is it ethical? (Weigh the affect on employees, customers, suppliers, community against shareholder benefit) Do it Don’t do it Do it, but notify appropriate parties Yes Does action maximize company returns? No Is it ethical not to take action? (Weigh the harm to shareholders vs. the benefits to other stakeholders) Is action legal? No Figure A.4

Qualitative Decision Making: Factoring Priorities Operations decisions often involve selection of suppliers, vendors, markets, products and so on. In most of these cases, one has to define priorities. Steps Identify attributes to rank or compare, skill, age Have a scale to use for ranking, e.g. 1, 2, 3, 4, 5 Choose a scale for priorities, e.g. 0.1, 0.3, 0.9… Use system to compare alternatives

Example: Priorities and Decision Making Employee A Employee B Weight (W) Score (S1 ) W x S1 Score (S2 ) W x S2 Language 0.1 10 6 Analytical 0.25 8 Technical 0.30 7 9 Salary Expect. 0.20 5 Degree 0.15 TOTAL

Example: Priorities and Decision Making Employee A Employee B Weight (W) Score (S1 ) W x S1 Score (S2 ) W x S2 Language 0.1 10 1 6 0.6 Analytical 0.25 1.5 8 2 Technical 0.30 7 2.1 9 2.7 Salary Expect. 0.20 5 1.2 Degree 0.15 0.75 TOTAL 1.0 7.1 7.25

Qualitative Decision Making: Location Strategies Review Chapter 8, Example 1, page 253-254 & 258 Solved problem 8.1, page 263 See problem 8.1 in Excel spreadsheet