1. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Supplement A - Decision Making

2. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Break-Even Analysis Definitions p = Price c = Variable cost per unit F = Fixed costs per year Q = Quantity Total Revenue = p Q Total Costs = F + c Q What is the break-even equation? Total Profit (TP) = TR – TC = 0 or TR = TC

3. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Break-Even Analysis Total annual revenues Total annual costs Patients (Q) Dollars (in thousands) 400 – 300 – 200 – 100 – 0 – |||| 500100015002000 Fixed costs Break-even quantity (2000, 400) (2000, 300) Profits Loss Example A.1 QuantityTotal AnnualTotal Annual (patients)Cost ($)Revenue ($) (Q)(100,000 + 100Q)(200Q) 0100,0000 2000300,000400,000

4. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Preference Matrix PerformanceWeightScoreWeighted Score Criterion(A)(B)(A x B) Market potential Unit profit margin Operations compatibility Competitive advantage Investment requirement Project risk Threshold score = 800 Example A.4

5. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Preference Matrix PerformanceWeightScoreWeighted Score Criterion(A)(B)(A x B) Market potential308240 Unit profit margin2010200 Operations compatibility206120 Competitive advantage1510150 Investment requirement10220 Project risk5420 Weighted score =750 Threshold score = 800 Example A.4

6. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Decision Theory Determine the optimal strategies when a decision maker is faced with several alternatives (with uncertainty) State of Nature: future events which are not under decision-maker’s control Payoff: the outcome resulting from making a decision and having a state of nature occurs. Approaches:a) Decision matrix (payoff table) b) Decision Tree

7. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Decision Making Environments Type 1. D-making under certainty: state of nature is known –Criteria: simply choose the alternative with highest payoff Type 2. D-making under uncertainty: –Criteria: Maximin: determine the worst possible payoff for each alternative, then choose the alternative that has the best “worst” Maximax: choose the alternative with highest possible payoff LaPlace: determine the average payoff for each alternative, choose the one with highest average payoff Minimax regret: determine the worst regret for each alternative, choose the alternative with the best “worst” (least regret) Type 3. D-making under risk: –Criteria: determine the Expected Monetary Value (EMV) or payoff for each alternative, choose the one with highest expected payoff.

8. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Decision Theory Examples Suppose the firm is faced with the following capacity expansion problem with payoff table: State-of-nature (demand) LowMiddleHigh Small facility$10 M1010 Medium facility71212 Large facility-4216

9. To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved. Q1. Determine the best decision, if it is known with certainty that demand will be (a) low, (b) moderate,(c) high Q2. Determine which alternative would be chosen under each of these criterion: maximin, maximax, Laplace, and minimax regret? Q3. Use EMV criterion for these probabilities: p(low)=0.3, p(moderate)=0.5, and p(high)=0.2 Q4.Using the above information, determine the expected value of perfect information (EVPI), where EVPI= (expected payoff under certainty) – (expected payoff under risk)