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Decision Making

Decision Making. Decision Making : a process of choosing among alternative courses of action for the purpose of attaining a goal or goals Managerial Decision Making Ex: Planning involves a series of decisions: what should be done?, When?, How?, Where?, By whom?. Decision Aspects

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Decision Making

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  1. Decision Making • Decision Making: a process of choosing among alternative courses of action for the purpose of attaining a goal or goals • Managerial Decision MakingEx: Planning involves a series of decisions: what should be done?, When?, How?, Where?, By whom? Turban CH 2

  2. Decision Aspects • Decision may be made by a group • Several, possibly contradictory objectives • Hundreds or thousands of alternatives • Results of the decision often occur in the future ( and no one is a perfect predictor of the future) • Different attitudes towards risk • “What-if” scenarios • Trial-and-error (experimentation with the real system): may result in a loss • Experimentation with the real system can only be done once • Changes in the environment can occur continuously Turban CH 2

  3. Systems • A SYSTEM is a collection of objects such as people, resources, concepts, and procedures intended to perform a specific function or to serve a goal • System Levels (Hierarchy): All systems are subsystems interconnected through interfaces Turban CH 2

  4. The Structure of a System Three Distinct Parts of Systems • Inputs • Processes • Outputs Systems • Are surrounded by an environment • Frequently include a feedback mechanismA human, the decision maker, is usually considered part of the system Turban CH 2

  5. System & Environment Customer Government Banks Vendors Inputs Processes Outputs Procedures Programs Tools Activities Decisions Raw materials Costs Resources Performance Products services suppliers Boundary Decision Maker Competitors Weather conditions Turban CH 2

  6. Inputs are elements that enter the system • Processes convert or transform the inputs into outputs • Outputs describe the finished products or the consequences of being in the system • Feedback is the flow of information from the output to the decision maker, who may modify the inputs or the processes (closed loop) • The Environment contains the elements that lie outside but impact the system's performance Turban CH 2

  7. Answer Two Questions (Churchman [1975]) 1. Does the element matter relative to the system's goals? [YES]2. Is it possible for the decision maker to significantly manipulate this element? [NO] Environmental Elements Can Be Social Political Legal Physical Economical Often Other Systems How to Identify the Environment? Turban CH 2

  8. 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 Directly Defined! Turban CH 2

  9. Closed and Open Systems • A Closed System is totally independent of other systems and subsystems • An Open System is very dependent on its environment Turban CH 2

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  11. 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 Turban CH 2

  12. System Effectiveness and Efficiency • Effectiveness is the degree to which goals are achievedDoing the right thing! • Efficiency is a measure of the use of inputs (or resources) to achieve outputsDoing the thing right! DSS emphasizes effectivenessOften: several non-quantifiable, conflicting goals Turban CH 2

  13. Models • A Major Component of any 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 Turban CH 2

  14. Degrees of Model Abstraction (Least to Most) • Iconic (Scale) Model: Physical replica of a system • Analog Model behaves like the real system but does not look like it (symbolic representation) • Mathematical (Quantitative) Modelsuse mathematical relationships to represent complexityUsed in most DSS analyses Turban CH 2

  15. Benefits of Models A DSS employs models because 1. Time compression2. Model manipulation is much easier3. Low cost of construction4. Low cost of execution 5. Can model risk and uncertainty6. Can model large and extremely complex systems with possibly infinite no. of solutions7. Enhance and reinforce learning, and training. Computer graphics advances: more iconic and analog models complement math models (e.g. visual simulation) Turban CH 2

  16. Example: How Much to Order for a small Grocery? • The Question: How much bread to stock each day? • T&E difficult if: • Too many trials to explore • High cost of making errors • Rapidly Changed Environment Several Solution Approaches • Trial-and-Error (with the real system) • Simulation • Optimization • Heuristics Turban CH 2

  17. The Decision-Making Process Systematic Decision-Making Process (Simon) • Intelligence • Design • Choice • Implementation Modeling is Essential to the Process Turban CH 2

  18. Intelligence phase • Reality is examined • The problem is identified and defined • Design phase • Representative model is constructed • The model is validated and evaluation criteria are set • Choice phase • Includes a proposed solution to the model • If reasonable, move on to the next step • Implementation phase • Solution to the original problem Often Backtrack / Cycle Throughout the Process Turban CH 2

  19. The Intelligence Phase Scan the environment to identify problem situations or opportunities1- Find the Problem Problems arise out of dissatisfaction with the way things are going • Identify organizational goals and objectives • Determine whether they are being met • Search and scanning procedures • Collecting data • Identify problem/opportunity • Explicitly define the problemreal-world problems are usually complicated by many interrelated factors. Turban CH 2

  20. 2- Problem Classification According to the Degree of Structuredness Programmed versus Nonprogrammed Problems Nonprogrammed Programmed Problems Problems Extreme Extreme Turban CH 2

  21. 3- Problem Decomposition • Divide a complex problem into (easier to solve) sub-problems (called Chunking) Some poorly structured problems may have some highly structured sub-problems 4- Problem OwnershipThe decision makers have the capability to solve the problem Outcome: Problem Statement Turban CH 2

  22. The Design Phase Generating, developing, and analyzingpossible courses of actionIncludes the activities: • Understanding the problem • Testing solutions for feasibility • A model of the problem situation is constructed, tested, and validatedModeling • Conceptualization of the problem • Abstraction to quantitative and/or qualitative forms Turban CH 2

  23. Mathematical Modeling • Identify Variables • Establish Equations describing their Relationships • Simplifications through Assumptions (e.g. linearing what is nonlinear) • Balance Model Simplification Vs the Accurate Representation of Reality(Simplified model leads to easier manipulation and faster solution, but is less representative of the problem reality) Turban CH 2

  24. Uncontrollable Variables Decision Variables Result Variables Mathematical Relationships 1- Components of Quantitative Models • Decision Variables • Uncontrollable Variables (and/or Parameters) • Result (Outcome) Variables • Mathematical Relationships or • Symbolic or Qualitative Relationships Turban CH 2

  25. Result Variables • Reflect the level of effectiveness of the system • Dependentvariables • Examples – Total transportation cost Customer satisfaction Decision Variables • Describe alternative courses of action • The decision maker controls them • Examples – shipment qnty, Staffing level Turban CH 2

  26. Uncontrollable Variables or Parameters(const) • Factors that affect the result variables • Not under the control of the decision maker • Generally part of the environment • Some constrain the decision maker and are called constraints • Examples – transportation cost per unit demand for service Intermediate Result Variables • Reflect intermediate outcomes Turban CH 2

  27. Area Decision Variables Result Variables Uncontrollable Variables and Parameters Financial investment Investment alternatives and amounts How long to invest When to invest Total profit Rate of return (ROI) Earnings per share Liquidity level Inflation rate Prime rate Competition Marketing Advertising budget Where to advertise Market share Customer satisfaction Customers' income Competitors' actions Manufacturing What and how much to produce Inventory levels Compensation programs Total cost Quality level Employee satisfaction Machine capacity Technology Materials prices Components of Models Turban CH 2

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  29. 2- The Structure of Quantitative Models • Mathematical expressions (e.g., equations or inequalities) connect the components • Simple financial-type model P = R - C • Present-value modelP = F / (1+i)n Turban CH 2

  30. 2- The Structure of Quantitative Models • Mathematical expressions (e.g., equations or inequalities) connect the components • Simple financial-type model P = R - C • Present-value modelP = F / (1+i)n Turban CH 2

  31. Example The Product-Mix Linear Programming Model • MBI Corporation • Decision: How many computers to build next month? • Two types of computers • Labor limit • Materials limit • Marketing lower limitsConstraint CC7 CC8 Rel Limit Labor (days) 300 500 <= 200,000 / moMaterials $ 10,000 15,000 <= 8,000,000/mo Units 1 >= 100 Units 1 >= 200 Profit $ 8,000 12,000 Max Objective: Maximize Total Profit / Month Turban CH 2

  32. Linear Programming Model • ComponentsDecision variablesResult variableUncontrollable variables (constraints) • SolutionX1 = 333.33X2 = 200Profit = $5,066,667 Turban CH 2

  33. 3- The Principle of Choice • What criteria to use? • Best solution? Or Good enough solution? • High or low-risk approach? Selection of a Principle of Choice : decision regarding the acceptability of a solution approach 2 prime principles of choice: • Normative • Descriptive Turban CH 2

  34. A- Normative Models • In which the chosen alternative is the best of all • Optimization process: • Find the highest level of goal attainment • Find the highest productivity (profit per $ invested) • Find the alternative with the lowest resources (cost) • Normative decision theory is based on rational decision makers Turban CH 2

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  36. Suboptimization • Optimization without considering the entire organization • Narrow the boundaries of a system • Leads to (possibly very good, but) non-optimal solutions Ex. For production dept: few products in large quantities may reduce the production cost • Get the sub-optimization solution: • Propose the solution to other subsystems • If no significant negative effects then adopt it Turban CH 2

  37. B- 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 • Checks the performance of the system for a given set of alternatives (not all) • No guarantee a solution is optimal • Often a solution will be "good enough” • Simulation: Well-known descriptive modeling technique Turban CH 2

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  39. 4- Developing (Generating) Alternatives • In Optimization Models: Alternatives are generated automatically by the Model!Not Always So! • Issue: When to Stop? Turban CH 2

  40. 5- 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 Turban CH 2

  41. Decision Making Under Certainty • Assumes that complete knowledge is available (deterministic environment) • Example: Bank Savings • Typically for structured problems with short time horizons Increasing Knowledge RISK RISK Certainty Uncertainty Turban CH 2 Decreasing Knowledge

  42. Decision Making Under Risk (Risk Analysis) (Probabilistic or stochastic decision situation) • Decision maker must consider several possible outcomes for each alternative (not only one), each with a given probability of occurrence • Long-run probabilities of the occurrences of the given outcomes are assumed known or can be estimated • Decision maker can assess the degree of risk associated with each alternative (calculated risk) Turban CH 2

  43. Decision Making Under Uncertainty • Situations in which several outcomes are possible for each course of action • BUT the decision maker does not know, or cannot estimate, the probability of occurrence of the possible outcomes • More difficult - insufficient information • Modeling involves assessing the decision maker's (and/or the organizational) attitude toward risk Turban CH 2

  44. 6- Measuring Outcomes • Value of an alternative is judged in terms of goal attainment • Maximize profit • Minimize cost • Customer satisfaction level (Minimize number of complaints) Turban CH 2

  45. 7- Scenarios Describe the decision and uncontrollable variables and parameters for a specific modeling situation Useful in • Simulation • What-if analysis Turban CH 2

  46. Importance of Scenarios in MSS • Help identify potential opportunities and/or problem areas • Provide flexibility in planning • Help validate major assumptions used in modeling • Help check the sensitivity of proposed solutions to changes in scenarios Turban CH 2

  47. Possible Scenarios • Many, but … • Worst possible (Low demand, High costs) • Best possible (High demand, High Revenue, Low Costs) • Most likely (Typical or average values) • The scenario sets the context of the analysis to be performed Turban CH 2

  48. The Choice Phase • Includes: Search, evaluation, and recommending an appropriate solution to the model • A solution to the model is a specific set of values for the decision variables in a selected alternativeThe problem is considered solved after the recommended solution to the model is successfully implemented Search Approaches • Analytical Techniques • Algorithms (Optimization) • Blind and Heuristic Search Techniques Turban CH 2

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  50. Evaluation: Multiple Goals, Sensitivity Analysis, "What-If," and Goal Seeking • Evaluation (coupled with the search process) leads to a recommended solution • Multiple Goals Complex systems have multiple goalsSome may conflict • Typical quantitative models have a single goal • Can transform a multiple-goal problem into a single-goal problem Turban CH 2

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