Operations Management

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

2. 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)

3. Introduction • Decision Making Approaches • Structured • Unstructured

4. 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

5. Structured Process Applied (Comparing Options) • Problem: Make decision about value of a partnership • Goals: • Minimize cost of maintenance • Maximize financial benefits • Minimize risks to lifestyle • Measures: - Salary ($), cost for clothes, dating costs, IQ,family assets, health care costs • Alternatives & Data :How would youmake the decision?

6. Model for Decisions (Comparing Options) Problem: Make decision about value of a partnership 4. Certain Outcomes: • Maximize benefits (Whoever brings highest gains) (A) • Minimize costs (Whoever brings lowest costs) (B) • Minimize risks (C) 5. Resolution :How would you make the decision?A : B: C:

7. Structured Process Applied Careful: Worst approach is to add all the numbers together. Apples and oranges case!

8. Decision Making Environment Level of confidenceabout occurrenceof outcomes and consequences Decisions under Risk (I) Decisions under certainty Total Decisions under uncertainty Decisions under Risk (II) Partial Value or Size ofoutcomes and consequences Estimate-able Known

9. Differentiating Decisions based on Outcomes • There is one position free. One candidate was interviewed. Position must be filled. The decision regarding who will receive offer of position can be made with _______________. • Likelihood of winning is certain. • There is one position free. Three candidates were interviewed. Position must be filled. The decision regarding who will receive offer of position can be made with _______________. • Likelihood of winning known. • There may be between zero and one free position. Position would be filled if profit is good and not filled if market is bad. Six candidates were interviewed. The decision regarding who will receive offer of the position can be made with ____________________. • Likelihood of winning known. • There could be zero to two positions free. Several candidates would be interviewed. Position would be filled if profit is good and not filled if market is bad. The decision regarding the hiring of one of the candidates interviewed can be made with _________________________. Likelihood of winning unknown.

10. Differentiating Decisions based on Outcomes • There is one position free. One candidate was interviewed. Position must be filled. The decision regarding who will receive offer of position can be made with __CERTAINTY__. • Likelihood of winning is certain. • There is one position free. Three candidates were interviewed. Position must be filled. The decision regarding who will receive offer of position can be made with __RISK I___. • Likelihood of winning known. • There may be between zero and one free position. Position would be filled if profit is good and not filled if market is bad. Six candidates were interviewed. The decision regarding who will receive offer of the position can be made with _RISK II__. • Likelihood of winning known. • There could be zero to two positions free. Several candidates would be interviewed. Position would be filled if profit is good and not filled if market is bad. The decision regarding the hiring of one of the candidates interviewed can be made with _UNCERTAINTY_______. Likelihood of winning unknown.

11. 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

12. 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 10 Decision Making Under Certainty 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

13. 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

14. 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 0.30.7 Decision Making Under Risk 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 If A3 is excluded, The preferable option is A2

15. States of Demand Seasonal Ticket Occasional Alternatives Prob. Market Prob. Market Sell 100 tickets early (A1) 0.7 $200,000 0.3 $50,000 Sell 100 tickets later (A2) 0.7 $150,000 0.3 $300,000 Do nothing (A3) $0 $0 Decision Making Under Risk (2) Case: Selling 100 tickets each time early or later in markets. In some cases the states of nature expected are certain, however the values of each states are uncertain. EMV(A1) = (0.7)($200,000) + (0.3)($50,000) = $155,000 EMV(A2) = (0.7)($150,000) + (0.3)($300,000) = $195,000 EMV(A3) = (0)($0) + (0)($0) = $0 The preferable option is A2

16. In-Class Exercise • Two suppliers deliver a product to us. Supplier 1 charges $ 575 for the part. The part seldom fails but its probability of failing is 0.1. The amount that we loose if part fails is $100. • Supplier 2 charges $ 550 for the same part. The probability of having a good part for this supplier is 0.8. The amount that we loose if a part fails is $460. • Find the expected monetary value of defective part from supplier 1? • Find the expected monetary value of defective part from supplier 2? • Find the total expected monetary value for each supplier?

17. In-Class Exercise: Response • Two suppliers deliver a product to us. Supplier 1 charges $ 575 for the part. The part seldom fails but its probability of failing is 0.1. The amount that we loose if part fails is $100. • Supplier 2 charges $ 550 for the same part. The probability of having a good part for this supplier is 0.8. The amount that we loose if a part fails is $460. • Find the expected monetary value of defective part from supplier 1?EMV defective part = $100*0.1 = $10 • Find the expected monetary value of defective part from supplier 2?EMV defective part = $460*(1-0.8) = $92 • Find the total expected monetary value for each supplier?

18. States Good Parts Defective Parts Alternatives Prob. Cost Prob. Cost Supplier 1 (A1) 0.9 $575 0.1 $(100+575) Supplier 2 (A2) 0.8 $550 0.2 $(460+550) In-Class Exercise: Response Find the total expected monetary value for each supplier? EMV(A1) = (0.9)($575) + (0.1)($675) = $585 EMV(A2) = (0.8)($550) + (0.2)($1010) = $642 The preferable option is A1

19. Expected Monetary Value

20. States of Nature Favorable Unfavorable Maximum Minimum Row Alternatives Market Market in Row in Row Average Constructlarge plant$200,000 -$180,000 $200,000 -$180,000 $10,000 Constructsmall plant$100,000 -$20,000 $100,000 -$20,000 $40,000 Do nothing$0 $0 $0 $0 $0 Maximax Maximin Equally likely Decision Making Under Uncertainty Probability unknownunknown Maximax choice is to construct a large plant Maximin choice is to do nothing Equally likely choice is to construct a small plant

21. Decision Making • What is fixed cost? • What is variable cost? • Revenues = Selling price * Volume sold • Total cost = Fixed cost ($) + (Variable cost ($) * Demand or Sale units (V) • Profit (Payoff $) = Revenues ($) – Fixed cost ($) - (Variable cost ($) * Demand units (V)

22. Example: Decision Making • We desire to make a toy bicycle. We have two designs. Design A has fixed cost of $100,000, its variable unit cost is $5 and its demand units could be 25,000 or 150,000. • Design B has fixed cost of $300,000, its variable unit cost is $3 and its demand units could be 25,000 or 150,000. • Probability of heavy demand is 0.7. Selling price per unit is $10. • What is the payoff table for the decision? • Profit (Payoff $) = Revenues ($) – Fixed cost ($) - (Variable cost ($) * Demand units (V)

23. Example: Deriving Payoff data for Decision Table • We desire to make a toy bicycle. We have two designs. Design A has fixed cost of $100,000, its variable unit cost is $5 and its demand units could be either 25,000 (low demand) or 150,000 (high demand). • Design B has fixed cost of $300,000, its variable unit cost is $3 and its demand units could be 25,000 (low demand) or 150,000 (high demand). • Probability of heavy demand is 0.8. Price $10 per unit. • What is the payoff table for the decision? • Profit (Payoff $) = Revenues ($) – Fixed cost ($) - (Variable cost ($) * Demand units (V) • Design A • Payoff I = 25,000*10$ – $100,000 - ($5* 25,000)= $25,000 • Payoff II = 150,000*10$ – $100,000 - ($5* 150,000)= $650,000 • Design B • Payoff I = 25,000*10$ – $300,000 - ($3* 25,000)= -$125,000 • Payoff II = 150,000*10$ – $300,000 - ($3* 150,000)= $750,000

24. States of Nature Light High Alternatives Market Market Design A (A1) $25,000 $650,000 Design B (A2) -$125,000 $750,000 Probabilities 0.30.7 Example: Creating Decision Table Using Calculated Payoff Data EMV(A1) = EMV(A2) = The preferable option is AXX

25. Using Decision Trees to Solve Decision Making Under Risk 1. Decision trees are ideal for decisions with sequential relationships • 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

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

27. 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 Decision Table Example Table A.1

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

29. In-Class Decision Tables to Decision Tree Exercise

30. EMV for node 1 = $10,000 = (.5)($200,000) + (.5)(-$50,000) Payoffs Favorable market (.60) $80,000 1 Favorable market (0.5) -$50,000 Construct large plant Favorable market (-0.5) $40,000 Construct small plant 2 Unfavorable market ( -0.5) -$10,000 Do nothing EMV for node 2 = $40,000 = (-0.5)($40,000) + (-0.5)(-$10,000) Figure A.2 $0 Exercise 1: Find and Circle all Problems With This Decision Tree

31. Favorable market (.60) Favorable market (.60) $100,000 1 2 Unfavorable market Unfavorable market -$50,000 Favorable market (0.6) $60,000 3 Unfavorable market -$10,000 $0 In-class Exercise 2: Find the EMVs for all options of the Decision Tree $140,000 -$70,000 Construct large plant Construct medium plant Construct small plant Do nothing Figure A.2

32. Complex Decision Tree Example Figure A.3

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

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

35. 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, e.g. 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 a system to compare alternatives

36. Example: Priorities and Decision Making

37. Example: Priorities and Decision Making

38. 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

39. Exercise 3: Transform the Qualitative Scores into Quantitative and Determine the Better Candidate