의사결정론의 개요

1. 의사결정론의 개요 중앙대학교 경영대학 박해철

2. The Classical Areas of Management • Finance • Marketing • Organizational Behavior/Personnel Management • Accounting • Management Information System • Management science/Operations Management • Strategy • International Business

3. The Importance of Management Science • Management science • The discipline of applying advanced analytical methods to help make better decisions. • Devoted to solving managerial-type problems using quantitative models • Applications of Management Science • Forecasting, capital budgeting, portfolio analysis, capacity planning, scheduling, marketing, inventory management, project management, and production planning.

4. Successful Applications of Management Science

5. Successful Applications of Management Science (cont’d)

6. Managers tend to use a qualitative approach to problem solving when The problem is fairly simple. The problem is familiar. The costs involved are not great. Managers tend to use a quantitative approach when The problem is complex. The problem is not familiar. The costs involved are substantial. Enough time is available to analyze the problem. Problem Solving Approaches

7. Advantages of the Quantitative Approach • Directs attention to the essence of an analysis: to solve a specific problem. • Results in more objective decisions than purely qualitative analysis. • Incorporates advances in computational technologies to managerial problem-solving.

8. Models • A Model • An abstraction of reality. It is a simplified, and often idealized, representation of reality. • Examples : an equation, an outline, a diagram, and a map • By its very nature a model is incomplete. • Mathematical models • Use numbers and algebraic symbols • Decision variables • Uncontrollable variables

9. Deterministic versus Probabilistic Models • Deterministic models • Used for problems in which information is known with a high degree of certainty. • Used to determine an optimal solution to the problem. • Probabilistic models • Used when it cannot be determined precisely what values (requiring probabilities) will occur (usually in the future).

10. The Management Science Approach

11. Example - Breakeven Analysis • Breakeven analysis (cost-volume analysis) • Is concerned with the interrelationship of costs, volume (quantity of output or sales), and profit. • The Break-Even Point (BEP) • The volume for which total revenue and total cost are equal. • The dividing line between profit and loss; sales higher than the break-even point will result in a profit, while sales that is lower than the break-even point will result in a loss. • Where you get “out of the red.”

12. Breakeven Analysis • Components of Break-Even Analysis • Volume: the level of output of a machine, department, or organization, or the quantity of sales. • Revenue: the income generated by the sale of a product. Total revenue = revenue per unit (selling price per unit) multiplied by units (volume) sold. • Costs: costs that must be taken into account • Fixed costs are not related to the volume of output. • Variable costs increase and decrease with output.

13. Total Revenue Increases Linearly as Volume Increases

14. Fixed Costs

15. Total Variable Cost

16. Total Cost

17. Profit and the Break-Even Point Profit

18. Example

19. The Classical Areas of Management Science • Linear Program • Network Model • Decision Analysis • Queuing Theory • Simulation • Project Management

20. Linear Program :

21. Example x1 = quantity of server model 1 to producex2 = quantity of server model 2 to produce maximize Z = 60x1+50x2Subject to:

22. Example - Cost per Ounce and Dietary Requirements for Diet Problem

23. Example - Diet Problem (continued)

24. Example - Workforce Scheduling

25. Other Applications • Transportation problems • Developing distribution plans that will minimize total distribution costs given the capacities of the various factories and the needs of the warehouses. • Assignment problems • Assigning jobs to machines in such a way that the total cost of performing the jobs is minimized.

26. Network Model :

27. A Network Diagram of a Transshipment Problem

28. A Network Diagram of Harley’s Sand and Gravel PitTransshipment Example

29. Decision Analysis :

30. Decision Analysis • Decision analysis problems are characterized by the following: • A list of alternatives. • A list of possible future states of nature. • Payoffs associated with each alternative/state of nature combination. • An assessment of the degree of certainty of possible future events. • A decision criterion.

31. Example Suppose that a real estate developer must decide on a plan for developing a certain piece of property. After careful consideration, the developer has ruled out “do nothing” and is left with the following list of acceptable alternatives: 1. Residential proposal. 2. Commercial proposal #1. 3. Commercial proposal #2. Suppose that the developer views the possibilities as 1. No shopping center. 2. Medium-sized shopping center. 3. Large shopping center.

32. General Format of a Decision Table

33. Payoff Table for Real Estate Developer

34. Decision Making under Risk • Decision making under partial uncertainty • Distinguished by the present of probabilities for the occurrence of the various states of nature under partial uncertainty. • The term risk is often used in conjunction with partial uncertainty. • Sources of probabilities • Subjective estimates • Expert opinions • Historical frequencies

35. Real Estate Payoff Table with Probabilities Expected Monetary Value (EMV) approach Provides the decision maker with a value that represents an average payoff for each alternative. The best alternative is, then, the one that has the highest expected monetary value. The average or expected payoff of each alternative is a weighted average: the state of nature probabilities are used to weight the respective payoffs.

36. Approaches to Incorporating Probabilities in the Decision Making Process • Expected Monetary Value (EMV) approach • Provides the decision maker with a value that represents an average payoff for each alternative. • Expected Opportunity Loss (EOL) • The opportunity losses for each alternative are weighted by the probabilities of their respective states of nature to compute a long-run average opportunity loss, and the alternative with the smallest expected loss is selected as the best choice. • Expected Value of Perfect Information (EVPI) • A measure of the difference between the certain payoff that could be realized under a condition of certainty and the expected payoff under a condition involving risk.

37. Decision Tree Format Decision trees are used by decision makers to obtain a visual portrayal of decision alternatives and their possible consequences.

38. Decision Tree for Real Estate Developer Problem

39. Real Estate Problem with a Second Possible Decision

40. Format of Graph for Sensitivity Analysis Sensitivity Analysis enables decision makers to identify a range of probabilities over which a particular alternative would be optimal.

41. The Expected Value Line for Alternative a.

42. Example of Finding the Expected Value for Alternative awhen P(#2) Is .50

43. All Three Alternatives Are Plotted on a Single Graph

44. The Line with the Highest Expected Profit Is Optimal for a Given Value of P(#2)

45. Queuing Models :

46. Major Elements of Waiting-Line Systems First come, first served (FCFS)Priority Classification Waiting lines are commonly found in a wide range of production and service systems that encounter variable arrival rates and service times.

47. The Total Cost Curve Is U-Shaped The most common goal of queuing system design is to minimize the combined costs of providing capacity and customer waiting. An alternative goal is to design systems that attain specific performance criteria (e.g., keep the average waiting time to under five minutes

48. Operating Characteristics Lq = the average number waiting for service L = the average number in the system (i.e.,waiting for service or being served) P0 = the probability of zero units in the system r = the system utilization (percentage of time servers are busy serving customers) Wa = the average time customers must wait for service W = the average time customers spend in the system (i.e., waiting for service and service time) M = the expected maximum number waiting for service for a given level of confidence

49. Line and Service Symbols for Average Number Waiting, and Average Waiting and Service Times

50. Formulas for Poisson Arrivals, Any Service Distribution