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Chapter 2

Chapter 2 . DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT. 8 th Edition. Learning Objectives. Understand the conceptual foundations of decision making Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation

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Chapter 2

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  1. Chapter 2 DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT 8th Edition 2nd semester 2010 Dr. Qusai Abuein

  2. Learning Objectives • Understand the conceptual foundations of decision making • Understand Simon’s four phases of decision making: intelligence, design, choice, and implementation • Recognize the concepts of rationality and bounded rationality, and how they relate to decision making 2nd semester 2010 Dr. Qusai Abuein

  3. Learning Objectives • Differentiate between the concepts of making a choice and establishing a principle of choice • Learn how DSS support for decision making can be provided in practice • Understand the systems approach 2nd semester 2010 Dr. Qusai Abuein

  4. (2.1) Opening Vignette • Illustrates how does Greenspan (the U.S federal Reserve chairman) decided to raise or lower interest rate. • He used statistical analysis to data collected from different systems, his 25 years experience, real-time information and knowledge systems. 2nd semester 2010 Dr. Qusai Abuein

  5. (2.2) Decision Making: Introduction and Definitions • Knowing: • How decision making is practiced, • Theories and models of decision making, • Traits of decision makers • Help in understanding the types of decision support tools that managers can use to make more effective decisions. 2nd semester 2010 Dr. Qusai Abuein

  6. (2.2) Decision Making: Introduction and Definitions • Characteristics of decision making • Groupthink • Decision makers are interested in evaluating what-if scenarios • Experimentation with the real system may result in failure • Experimentation with the real system is possible only for one set of conditions at a time and can be disastrous • Changes in the decision making environment may occur continuously, leading to invalidating assumptions about the situation 2nd semester 2010 Dr. Qusai Abuein

  7. (2.2) Decision Making: Introduction and Definitions • To determine how real decision makers make decisions, we : • Must understand the process of decision making • Understand appropriate methodologies for assisting decision makers • Contributions IS can make • Only then can we develop DSS to help decision makers. 2nd semester 2010 Dr. Qusai Abuein

  8. (2.2) Decision Making: Introduction and Definitions • A decision is selection the best alternative from two or more solutions. (as defined in ch1) • Decision making: The action of selecting among alternatives • Planning involves a series of decisions: What should be done? When? Where? Why? How? By whom? • Managers set goals or plan • Planning implies decision making 2nd semester 2010 Dr. Qusai Abuein

  9. (2.2) Decision Making: Introduction and Definitions • Decision making and problem solving • A problem occurs when: • A system does not meet its goals • Does not yield the predicted results, or • Does not work as planned • Problem solving may deal with identifying new opportunities. Q) How to differentiate between problem solving and decision making? • By examining the phases of decision process. 2nd semester 2010 Dr. Qusai Abuein

  10. (2.2) Decision Making: Introduction and Definitions • Phases of the decision process • Intelligence • Design • Choice • Problem solving A process in which one starts from an initial state and proceeds to search through a problem space to identify a desired goal. It includes the 4th phase of the decision process • Implementation • Some consider the phases (1-4) as problem solving, with the choice phase as the real decision-making process. • Others, consider phases (1-3) as the formal decision-making, with the implementation phase as the problem solving 2nd semester 2010 Dr. Qusai Abuein

  11. (2.2) Decision Making: Introduction and Definitions • In this book problem solving is used interchangeably with decision making. 2nd semester 2010 Dr. Qusai Abuein

  12. (2.2) Decision Making: Introduction and Definitions • Decision making disciplines are: • Behavioral • Scientific • What constitutes a successful decision in practice? • Effectiveness The degree of goal attainment. Doing the right things • Efficiency The ratio of output to input. Appropriate use of resources. Doing the things right Effectiveness is the concern of MSS (Management Support Sys.) Efficiency is the concern of TPS (Transaction Support System) 2nd semester 2010 Dr. Qusai Abuein

  13. (2.2) Decision Making: Introduction and Definitions • Decision style and decision makers • Decision style The manner in which a decision maker thinks and reacts to problems. It includes perceptions, cognitive responses, values, and beliefs. • Decision styles are: • Autocratic • Democratic • Consultative 2nd semester 2010 Dr. Qusai Abuein

  14. (2.2) Decision Making: Introduction and Definitions • Decision style and decision makers • Different decision styles require different types of support • Individual decision makers need access to data and to experts who can provide advice • Groups need collaboration tools • In small organizations, decisions are often made by individuals at lower managerial level. • In medium and large organizations, decisions are made by groups. 2nd semester 2010 Dr. Qusai Abuein

  15. (2.3) Models • The basic idea is to perform the DSS analysis on a model of reality rather than on real system. • A model is a simplified representation of reality. • Models are classified into: • Iconic model A scaled physical replica, such as an airplane model (3D) • Analog model An abstract, symbolic model of a system that behaves like the system but looks different, such as charts and figure represents water on mountains. (2D) 2nd semester 2010 Dr. Qusai Abuein

  16. (2.3) Models • Mental model The mechanisms or images through which a human mind performs sense-making in decision making • Mathematical (quantitative) model A system of symbols and expressions that represent a real situation 2nd semester 2010 Dr. Qusai Abuein

  17. (2.3) Models • The benefits of models • MMS uses models for the following reasons: • Model manipulation is much easier than manipulating a real system • Models enable the compression of time (simulation) • The cost of modeling analysis is much lower • The cost of making mistakes during a trial-and-error experiment is much lower when models are used than with real systems 2nd semester 2010 Dr. Qusai Abuein

  18. (2.3) Models • The benefits of models • With modeling, a manager can estimate the risks resulting from specific actions within the uncertainty of the business environment • Mathematical models enable the analysis of a very large number of possible solutions • Models enhance and reinforce learning and training • Models and solution methods are readily available on the Web • Many Java applets are available to readily solve models 2nd semester 2010 Dr. Qusai Abuein

  19. (2.4) Phases of the Decision-Making Process 2nd semester 2010 Dr. Qusai Abuein

  20. (2.4) Phases of the Decision-Making Process • It advisable to follow a systematic decision-making process: • Intelligence phase The initial phase of problem definition in decision making • Design phase The second decision-making phase, which involves constructing a model and finding possible alternatives in decision making and assessing their contributions 2nd semester 2010 Dr. Qusai Abuein

  21. (2.4) Phases of the Decision-Making Process • Choice phase The third phase in decision making, in which an alternative is selected • Implementation phase The fourth decision-making phase, involving actually putting a recommended solution to work 2nd semester 2010 Dr. Qusai Abuein

  22. (2.5) Decision Making: The Intelligence Phase • It includes problem (or opportunity) identification.Some issues that may arise during data collection: • Data are not available • Obtaining data may be expensive • Data may not be accurate or precise enough • Data estimation is often subjective • Data may be insecure • Important data that influence the results may be qualitative 2nd semester 2010 Dr. Qusai Abuein

  23. (2.5) Decision Making: The Intelligence Phase • Some issues that may arise during data collection • Information overload (too many data) • Outcomes (or results) may occur over an extended period • If future data is not consistent with historical data, the nature of the change has to be predicted and included in the analysis 2nd semester 2010 Dr. Qusai Abuein

  24. (2.5) Decision Making: The Intelligence Phase • Problem classification The conceptualization of a problem in an attempt to place it in a definable category, possibly leading to a standard solution approach • Problem decomposition Dividing complex problems into simpler subproblems may help in solving the complex ones • Problem ownership Is the process of assigning authority to solve a problem 2nd semester 2010 Dr. Qusai Abuein

  25. (2.6) Decision Making: The Design Phase • The design phase involves finding or developing and analyzing possible courses of action. These include: • Understanding the problem • Testing solutions for feasibility • A model of the decision-making problem is constructed, tested, and validated 2nd semester 2010 Dr. Qusai Abuein

  26. (2.6) Decision Making: The Design Phase • Modeling involves conceptualizing a problem and abstracting it to quantitative and/or qualitative form • Models have: • Decision variables • Principle of choice 2nd semester 2010 Dr. Qusai Abuein

  27. (2.6) Decision Making: The Design Phase • Decision variables A variable in a model that can be changed and manipulated by the decision maker. Decision variables correspond to the decisions to be made, such as quantity to produce, amounts of resources to allocate, and so on • Principle of choice The criterion that describes the acceptability of a solution approach. Q) What is the difference between Criterion and constraint? (See technology insight 2.1 page 59) 2nd semester 2010 Dr. Qusai Abuein

  28. (2.6) Decision Making: The Design Phase • Among the many principles of choice, normative and descriptive are important. • Normative models Models in which the chosen alternative is demonstrably (proven to be) the best of all possible alternatives • Optimization The process of examining all the alternatives and proving that the one selected is the best • Suboptimization An optimization-based procedure that does not consider all the alternatives for or impacts on an organization because a decision made in one area may affect other areas. (details P61,62 are important) 2nd semester 2010 Dr. Qusai Abuein

  29. (2.6) Decision Making: The Design Phase • Descriptive model A model that describes things as they are. These models are typically mathematically based. • Simulation Is an imitation ofreality and the most common descriptive modeling method. • Narrativeis another descriptive decision-making model, it is a story that helps a decision maker uncover the important aspects of the situation and leads to better understanding and framing 2nd semester 2010 Dr. Qusai Abuein

  30. (2.6) Decision Making: The Design Phase • Good enough or satisficing • Satisficing A process by which one seeks a solution that will satisfy a set of constraints. In contrast to optimization, which seeks the best possible solution, satisficing simply seeks a solution that will work well enough up to a predetermined level of performance. Q) Why accepting satisficing solution? • Due to time pressure (timely decision) • The ability to achieve optimization • Marginal benefit of a better decision does not worth the marginal cost to obtain it 2nd semester 2010 Dr. Qusai Abuein

  31. (2.6) Decision Making: The Design Phase • Good enough or satisficing • Satisficing is a form of suboptimization. • There may be a best solution (optimum), but it would be difficult (impossible) to attain: • With a normative model: too much computation may be involved • With descriptive model: it may not be possible to evaluate all alternatives • Q) Since rationality and the use of normative models lead to good decisions, why are so many bad decisions made in practice? H.W. 2nd semester 2010 Dr. Qusai Abuein

  32. (2.6) Decision Making: The Design Phase • Developing (generating) alternatives • In optimization models (linear programming) the alternatives may be generated automatically by the model • In most MSS situations it is necessary to generate alternatives manually (a lengthy, costly process); issues such as when to stop generating alternatives are very important • The search for alternatives usually occurs after the criteria for evaluating the alternatives are determined (this way it is easy to find alternatives) • The outcome of every proposed alternative must be established 2nd semester 2010 Dr. Qusai Abuein

  33. (2.6) Decision Making: The Design Phase • Measuring outcomes • The value of an alternative is evaluated in terms of goal attainment • Ex.(1): • Goal Profit maximization • Outcome  Profit • Ex.(2): • Goal Customer Satisfaction maximization • Outcome  Customer Satisfaction Q) What is an AHP? H.W. Measured by JD Measured by # of complaints, level of loyalty to product, or surveys 2nd semester 2010 Dr. Qusai Abuein

  34. (2.6) Decision Making: The Design Phase • Risk • One important task of a decision maker is to attribute a level of risk to the outcome associated with each potential alternative being considered • Decisions that lead to unacceptable risks in terms of success are discarded. • Risk is an important issue when developing BI/DSS 2nd semester 2010 Dr. Qusai Abuein

  35. (2.6) Decision Making: The Design Phase • Scenario Is a statement of assumptions about the operating environment of a particular system at a given time; that is, it is a narrative description of the decision-situation setting • Scenarios are especially helpful in simulations and what-if analyses • In simulation and what-if analysis, scenarios are changes and results are examined 2nd semester 2010 Dr. Qusai Abuein

  36. (2.6) Decision Making: The Design Phase • Scenarios play an important role in MSS because they: • Help identify opportunities and problem areas • Provide flexibility in planning • Identify the leading edges of changes that management should monitor • Help validate major modeling assumptions • Allow the decision maker to explore the behavior of a system through a model • Help to check the sensitivity of proposed solutions to changes in the environment 2nd semester 2010 Dr. Qusai Abuein

  37. (2.6) Decision Making: The Design Phase • Possible scenarios • The worst possible scenario • The best possible scenario • The most likely scenario • The average scenario • Scenario determines the context of analysis to be performed. 2nd semester 2010 Dr. Qusai Abuein

  38. (2.6) Decision Making: The Design Phase • Errors in decision making • The model is a critical component in the decision-making process • A decision maker may make a number of errors in its development and use • Validating the model before it is used is critical • Gathering the right amount of information, with the right level of precision and accuracy is also critical 2nd semester 2010 Dr. Qusai Abuein

  39. (2.7) Decision Making: The Choice Phase • The choice phase is the one in which the actual decision is made and involves following appropriate course of action Q) Why boundary between design and choice phases are not clear? H.W. • The choice phase includes the search for, evaluation of, and recommendation of an appropriate solution to a model. • Solving a model is not as solving the problem the model represents. • Solution to a model is considered a recommended solution to a problem. • When the recommended solution is successful, then the problem is solved. 2nd semester 2010 Dr. Qusai Abuein

  40. (2.7) Decision Making: The Choice Phase • Solving a model involves searching for appropriate course of action. Searching approaches include: • Analytical techniques (solving a formula) • Algorithms (step-by-step procedures) • Heuristics (rules of thumb) • Blind searches 2nd semester 2010 Dr. Qusai Abuein

  41. (2.7) Decision Making: The Choice Phase • Analytical techniques Methods that use mathematical formulas to derive an optimal solution directly or to predict a certain result, mainly in solving structured problems • Algorithm A step-by-step search in which improvement is made at every step until the best solution is found 2nd semester 2010 Dr. Qusai Abuein

  42. (2.7) Decision Making: The Choice Phase • Heuristics Informal, judgmental knowledge of an application area that constitutes the rules of good judgment in the field. Heuristics also encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth 2nd semester 2010 Dr. Qusai Abuein

  43. (2.7) Decision Making: The Choice Phase • Sensitivity analysis A study of the effect of a change in one or more input variables on a proposed solution • What-if analysis A process that involves asking a computer what the effect of changing some of the input data or parameters would be 2nd semester 2010 Dr. Qusai Abuein

  44. (2.8) Decision Making: The Implementation Phase • The implementation to a proposed solution is the introduction of change. • Change which includes user expectation must be managed. • Implementation phase involves putting a recommended solution to work. • Generic implementation issues important in dealing with MSS include: • Resistance to change • Degree of support of top management • User training 2nd semester 2010 Dr. Qusai Abuein

  45. (2.9) How Decisions Are Supported • The decision making process (Figure 2.2 next slide) can be improved with computer support. • Specific MSS technologies to decision making process include: • Database • Data warehouse 2nd semester 2010 Dr. Qusai Abuein

  46. (2.9) How Decisions Are Supported 2nd semester 2010 Dr. Qusai Abuein

  47. (2.9) How Decisions Are Supported • Support for the intelligence phase • The ability to scan external and internal information sources for opportunities and problems and to interpret what the scanning discovers • Web tools and sources are extremely useful for environmental scanning • Web browsers provide useful front ends for a variety of tools (OLAP, data mining, data warehouses) • Internal data sources may be accessible via a corporate intranet • External sources are many and varied 2nd semester 2010 Dr. Qusai Abuein

  48. (2.9) How Decisions Are Supported • Support for the design phase • The generation of alternatives for complex problems requires expertise that can be provided only by a human, brainstorming software, or an ES 2nd semester 2010 Dr. Qusai Abuein

  49. (2.9) How Decisions Are Supported • Support for the choice phase • DSS can support the choice phase through what-if and goal-seeking analyses • Different scenarios can be tested for the selected option to reinforce the final decision • KMS helps identify similar past experiences • CRM, ERP, and SCM systems are used to test the impacts of decisions in establishing their value, leading to an intelligent choice • An ES can be used to assess the desirability of certain solutions and to recommend an appropriate solution • A GSS can provide support to lead to consensus in a group 2nd semester 2010 Dr. Qusai Abuein

  50. (2.9) How Decisions Are Supported • Support for the implementation phase • DSS can be used in implementation activities such as decision communication, explanation, and justification. • DSS benefits are partly due to the vividness and detail of analyses and reports. • Decision implementation can also be supported by ES. • ES can be used as an advisory system and provide training that may smooth the implementation. 2nd semester 2010 Dr. Qusai Abuein

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