1. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-1 Chapter 2 Decision-Making Systems, Models, and Support Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-2 Learning Objectives Learn the basic concepts of decision making. Understand systems approach. Learn Simon’s four phases of decision making. Learn which factors affect decision making. Learn how DSS supports decision making in practice.

3. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-3 Standard Motor Products Shifts Gears Into Team-Based Decision-Making Vignette Team-based decision making –Increased information sharing –Daily feedback –Self-empowerment –Idea generation. –Time/Quality aspect of the decision. Shifting responsibility towards teams Elimination of middle management

4. 4 Typical Business Decision Aspects Decision may be made by a group Group member biases Groupthink Several, possibly contradictory objectives Many alternatives Results can occur in the future Attitudes towards risk // different behaviors to handle risks Need information Gathering information takes time and expense Too much information “What-if” scenarios Trial-and-error experimentation with the real system may result in a loss Experimentation with the real system - only once Changes in the environment can occur continuously Time pressure Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-5 Decision Making Process of choosing amongst alternative courses of action for the purpose of attaining a goal or goals. The four phases of the decision process are: –Intelligence –Design –Choice –implementation

6. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-6 What each Phase consists of? The Intelligence Phase consists of: - Organizational objectives. - Search and scanning procedures. - Data collection. - Problem identification. - Problem ownership. - Problem classification. - Problem statement. The Design Phase consists of: - Formulate a model. - Set criteria for choice. - Search for alternatives. - Predict and measure outcomes. The Choice Phase consists of: - Solution to the model. - Selection of the best (good) alternative (s). - Plan for implementation.

7. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-7 Managerial Decision Making is synonymous with the whole process of management Vertical structures A hierarchical organization structure in which management or supervisors pass information and orders from the top of the organizational pyramid down toward the bottom. Little communication or feedback flows from the bottom up or from side to side. This creates a high control, low autonomy environment. Also called top down management. Horizontal structures Employee has a less-defined chain of command, employees may work in teams, with everyone on the team having input. Employees may perform many different function and may report to several supervisors, rather than a single boss.

8. 8 Systems A SYSTEM is a collection of objects such as people, resources, concepts, and procedures intended to perform an identifiable function or to serve a goal System Levels (Hierarchy): All systems are subsystems interconnected through interfaces Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-9 Systems Structure –Inputs –Processes –Outputs –Feedback from output to decision maker Separated from environment by boundary Surrounded by environment InputProcessesOutput boundary Environment

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-10

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-11 –Inputs: are elements that enter the system. –Processes: are all the necessary to convert or transform inputs into outputs. –Outputs: are the finished products or the consequences of being in the system. –Feedback from output to decision maker; there is a flow of information from the output component to the decision-maker concerning the system’s output or performance. Based on the outputs, the decision-maker, may decide to modify the inputs, the processes, or both. the decision-maker compares the output to the expected output and adjusts the input and possibly the processes to move close to the output targets.

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-12 The environment: Is composed of several elements that lie outside in in the sense that they are not inputs, output, or processes. However they affect the system’s performance and consequently the attainment of its goals. Environmental elements can be social, political, legal, physical, or economic. The Boundary: A system is separated from its environment by boundary.

13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-13 Environmental Elements Can Be Social, people, traditions and customs. Political. Legal, regulation. Physical. Economical.

14. 14 The Boundary Separates a System From Its Environment Boundaries may be physical or nonphysical (by definition of scope or time frame) Information system boundaries are usually by definition! Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-15 System Types Closed system –Independent –Takes no inputs –Delivers no outputs to the environment –Black Box: is one which inputs and outputs are well defined, but the process itself is not specified. Such as transaction processing system (TPS). Open system –Very Dependant on it environment. –Accepts inputs from the environment. –Delivers outputs to environment

16. 16 An Information System Collects, processes, stores, analyzes, and disseminates information for a specific purpose Is often at the heart of many organizations Accepts inputs and processes data to provide information to decision makers and helps decision makers communicate their results Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

17. 17 System Effectiveness and Efficiency Two Major Classes of Performance Measurement Effectiveness is the degree to which goals are achieved Doing the right thing! Efficiency is a measure of the use of inputs (or resources) to achieve outputs Doing the thing right! MSS emphasize effectiveness General company status before using the MSS is several non-quantifiable, conflicting goals. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

18. 18 Models Major component of DSS Use models instead of experimenting on the real system A model is a simplified representation or abstraction of reality. Reality is generally too complex to copy exactly Much of the complexity is actually irrelevant in problem solving Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-19 Models Used for DSS Iconic –Small physical replication of system, it may be three dimensional such as that of an airplane, car, or production line. Or two-dimensional such as photographs. Analog –Behavioral representation of system –May not look like system Ex. Stock market charts that represent the price movements of stocks. Animations, videos, and movies. Quantitative (mathematical) –Demonstrates relationships between systems used in management science.

20. The benefits of Models Model manipulation is much easier. Models enable the compression of time. The cost of modeling is cheaper. The cost of making mistake over trial and error is much less. Risk could be estimated. Mathematical model use for massive products. Can model large and extremely complex systems with possibly infinite solutions Enhance and reinforce learning, and enhance training. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-20

21. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-21 Phases of Decision-Making Simon’s original three phases: –Intelligence –Design –Choice He added fourth phase later: –Implementation Book adds fifth stage: –Monitoring

22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-22 Decision-Making Intelligence Phase Scan the environment Analyze organizational goals Collect data Identify problem Categorize problem –Programmed and non-programmed (p55) –Decomposed into smaller parts (p55) Assess ownership and responsibility for problem resolution

23. 23 The Intelligence Phase Scan the environment to identify problem situations or opportunities Find the Problem Identify organizational goals and objectives Determine whether they are being met Explicitly define the problem. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

24. 1- Distinguish a problem from its symptoms. Problems arise out of dissatisfaction with the way things are going. It is the result of a difference between what we desire and what is (or is not) happening. One example was given earlier: excessive costs (problem) and improper inventory level (one symptom of the problem). Another example, is that of a high level of variance in a manufactured product (symptom) and the need to recalibrate the manufacturing equipment (problem). © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-24

25. 2- Define programmed (structured) versus non-programmed (unstructured) problems. Give one example in each of the following areas: accounting, marketing, and human resources. In programmed decisions, all phases are structured. In non- programmed decisions, no phases are structured. Accounting: A balance sheet is structured, preparing a tax shelter plan is not. Marketing: Designing a cover for a magazine is unstructured. Planning an appropriate distribution/transportation program is structured. Personnel administration: Hiring low-skilled employees is structured; hiring executives is unstructured. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-25

26. 26 Problem Classification Structured versus Unstructured Programmed versus Non-programmed Problems Simon (1977) Non-programmed ProgrammedProblems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

27. 27 Problem Decomposition: Divide a complex problem into (easier to solve) sub-problems Chunking (Salami) Some seemingly poorly structured problems may have some highly structured sub-problems Problem Ownership Outcome: Problem Statement Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-28 Decision-Making Design Phase Develop alternative courses of action Analyze potential solutions Create model Test for feasibility Validate results Select a principle of choice –Establish objectives –Risk assessment and acceptance –Criteria and constraints

29. 29 The Principle of Choice What criteria to use? Best solution? Good enough solution? Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

30. 30 Selection of a Principle of Choice Not the choice phase A decision regarding the acceptability of a solution approach القرار على مدى تقبل الحل المطروح Normative Descriptive // uses simulation technique in video games and computer. P.65 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

31. 31 The Modeling Process-- A Preview Solution Approaches Trial-and-Error Simulation Optimization Heuristics Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

32. 32 Mathematical Model Identify variables Establish equations describing their relationships Simplifications through assumptions Balance model simplification and the accurate representation of reality. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

33. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-33 Decision-Making Design Phase Principle of choice –Is a criterion that Describes acceptability of a solution approach while it has been seat in the design phase we are here to finalize choosing. Normative Models –Optimization A DS consider the impact of each alternative. Effect of each alternative –Rationalization More of good things, less of bad things Courses of action are known quantity Options ranked from best to worse –Sub-optimization Decisions made in separate parts of organization without consideration of whole.

34. 34 Suboptimization Narrow the boundaries of a system Consider a part of a complete system Leads to (possibly very good, but) non-optimal solutions Viable method Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

35. Normative Models are those in which the chosen alternative is demonstrably the best of all possible alternatives. To find it, one should examine all alternatives and prove that one selected is indeed the best, which is what one would normally want, Normative decision theory based on rational decision makers. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-35

36. Normative decision Normative decision theory is based on the following assumptions of rational decision makers: The main objective is to maximize the attainment of goals. For the Decision making situation, all viable course of action and their consequences are known. Decision makers have an order that enable them to rank the desirability of all consequences of the analysis. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-36

37. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-37 Descriptive Models Describe things as they are, or how things are believed to be These Model are Typically, mathematically based Applies single set of alternatives Examples: –Simulations –What-if scenarios –Cognitive map: understand issues better, focus better and reach closure –Narratives: is a story that, when told, helps a decision maker uncover the important aspects of the situation and leads to better understanding and framing.

38. Descriptive Models Describe things as they are, or as they are believed to be Extremely useful in DSS for evaluating the consequences of decisions and scenarios No guarantee a solution is optimal Often a solution will be good enough Simulation: Descriptive modeling technique Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

39. 39 Descriptive Models include Information flow Scenario analysis Financial planning Complex inventory decisions Mark analysis (predictions) Environmental impact analysis Simulation. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

40. 40 Satisficing (Good Enough) Most human decision makers will settle for a good enough solution Tradeoff: time and cost of searching for an optimum versus the value of obtaining one Good enough or satisficing solution may meet a certain goal level is attained (Simon, 1977) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

41. 41 Why Satisfice? Bounded Rationality (Simon) Humans have a limited capacity for rational thinking Generally construct and analyze a simplified model Behavior to the simplified model may be rational But, the rational solution to the simplified model may NOT BE rational in the real-world situation Rationality is bounded by –limitations on human processing capacities –individual differences Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

42. 42 Predicting the Outcome of Each Alternative Must predict the future outcome of each proposed alternative Consider what the decision maker knows (or believes) about the forecasted results Classify Each Situation as Under –Certainty –Risk –Uncertainty Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

43. 43 Decision Making Under Certainty Assumes complete knowledge available (deterministic environment) Typically for structured problems with short time horizons Sometimes DSS approach is needed for certainty situations Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

44. 44 Decision Making Under Risk (Risk Analysis) Probabilistic or stochastic decision situation Must consider several possible outcomes for each alternative, each with a probability Long-run probabilities of the occurrences of the given outcomes are assumed known or estimated Assess the (calculated) degree of risk associated with each alternative Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

45. 45 Risk Analysis Calculate the expected value of each alternative Select the alternative with the best expected value Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

46. 46 Decision Making Under Uncertainty Several outcomes possible for each course of action BUT the decision maker does not know, or cannot estimate the probability of occurrence More difficult - insufficient information Assessing the decision maker's attitude toward risk. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

47. 47 Measuring Outcomes Goal attainment Maximize profit Minimize cost Customer satisfaction level (minimize number of complaints) Maximize quality or satisfaction ratings (surveys) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

48. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-48 Developing Alternatives Generation of alternatives –May be automatic or manual –May be legion, leading to information overload –Scenarios –Evaluate with heuristics –Outcome measured by goal attainment

49. 49 Scenarios Useful in Simulation What-if analysis Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

50. 50 Importance of Scenarios in MSS Help identify potential opportunities and/or problem areas Provide flexibility in planning Identify leading edges of changes that management should monitor Help validate major assumptions used in modeling Help check the sensitivity of proposed solutions to changes in scenarios Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

51. 51 Possible Scenarios Worst possible (low demand, high cost) Best possible (high demand, high revenue, low cost) Most likely (median or average values) Many more The scenario sets the stage for the analysis Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

52. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-52 Problems Satisficing is the willingness to settle for less than ideal. –Form of suboptimization Bounded rationality –Limited human capacity. –Limited by individual differences and biases. Too many choices

53. 2-53 Decision-Making Choice Phase Decision making with commitment to act, Solving a decision making model involves searching for an appropriate course of action Searching approach include: –Analytical techniques ( solving a formula) –Algorithms( step-by-step procedures) –Heuristics (rules of thumb) –Blind searches ( shooting in the dark, ideally in a logical way) Analyze for robustness add p.70

54. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-54 Decision-Making Implementation Phase Putting solution to work Vague (unknown) boundaries which include: –Dealing with resistance to change –User training –Upper management support

55. How decisions are supported Here we relate specific MSS technologies to the decision making process. Database, data marts and data warehouses which is an important technology in supporting all phases of decision making. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-55

56. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-56 Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.

57. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-57 Decision Support Systems Intelligence Phase –Automatic Data Mining –Expert systems, CRM, neural networks –Manual OLAP KMS –Reporting Routine and ad hoc

58. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-58 Decision Support Systems Design Phase –Financial and forecasting models –Generation of alternatives by expert system –Business process models from CRM, ERP, and SCM

59. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-59 Decision Support Systems Choice Phase –Identification of best alternative –Identification of good enough alternative –What-if analysis –Goal-seeking analysis –May use KMS, GSS, CRM, ERP, and SCM systems

60. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-60 Decision Support Systems Implementation Phase –Improved communications –Collaboration –Training –Supported by KMS, expert systems, GSS

61. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-61 Decision-Making In Humans Temperament –Hippocrates’ personality types –Myers-Briggs’ Type Indicator –Kiersey and Bates’ Types and Motivations –Birkman’s True Colours Gender

62. 62 Myers-Briggs Dimensions Extraversion (E) to Intraversion (I) Sensation (S) to Intuition (N) Thinking (T) to Feeling (F) Perceiving (P) to Judging (J) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

63. 63 Birkman True Colors Types Red Blue Green Yellow Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ Green : like communicate directly Red also but they like to be group leader Yellow : like indirect communications Blue : like indirect communications they always need advise.

64. 64 Gender Sometimes empirical testing indicates gender differences in decision making Results are overwhelmingly inconclusive Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

65. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-65 Decision-Making In Humans Cognitive styles –What is perceived? –How is it organized? –Subjective Decision styles –How do people think? –How do they react? –Heuristic, analytical, autocratic, democratic, consultative

66. Through this chapter we tried to describe the support that DSS has add to the systems and the benefits, usage, type of models. In p. 63 a closer case study in this chapter showing that DSS had invade all the business sectors and even life concepts. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 2-66