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Ch9_Model Bases Decision Making Turban.pptx
- 1. Decision Support and BusinessIntelligence Systems CHAPTER 9: MODEL BASES DECISION MAKING OPTIMIZATION DAN MULTI CRITERIA
- 2. Learning Objectives ■Understand thebasic concepts of analytical decision modeling ■Describe how prescriptive models interact with data and the user ■Understand some different, well-known model classes ■Understand how to structure decision making with a few alternatives ■Describe how spreadsheets can be used for analytical modeling and solution
- 3. Learning Objectives ■Explain thebasic concepts of optimization and when to use them ■Describe how to structure a linear programming model ■Describe how to handle multiple goals ■Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking ■Describe the key issues of multi-criteria decision making
- 4. Opening Vignette: ■ Introduction ■Presentation of Problem ■ Methodology/Solution ■ Results/Benefits
- 5. Decision Support Systems Modeling ■Making decisions using some kind of analytical model is what we call prescriptive analytics. ■ Although there are situations where one can use experience and intuition to make decisions, it is more likely that a decision supported by a model will help a decision maker make better decisions ■ Modeling is a key element in most DSS and a necessity in a model-based DSS. ■ DSS modeling contribute to organizational success.
- 6. Decision Support Systems ModelingExample ■ We could be facing a situation of deciding which prospective customers should receive what promotional campaign material so that our cost of promotion is not outrageous, while maximize the response rate. ■ We may be deciding how much to pay for different paid search keywords to maximize the return on investment of our advertising budget. ■ We may have to study the history of our customers’ arrival patterns and, and apply that to schedule an appropriate number of store employees optimize our labor costs. ■ We could be deciding where to locate our warehouses based on our analysis and prediction of demand for our products and the supply chain costs.
- 7. Current Modeling Issues ■Identification of the problem and environmental analysis ■ environmental scanning and analysis ■ Variable identification ■ influence diagram, ex : cognitive map ■ Forecasting ■ predicting the future ■ essential for construction and manipulating model ■ Model Categories ■ static or dynamic model ■ include spreadsheets, data mining systems, OLAP systems
- 8. Current Modeling Issues ■Model Management ■ Models like data must be managed to maintain their integrity ■ Knowledge-based modeling ■ predictive analytics techniques : classificaton, clustering,.. ■ Current trends in modeling ■ development of model libraries ■ using Web tools and software ■ multidimensional analysis ■ influence diagram
- 9. Model Categories Static andDynamic Models ■ Static ■ Single snapshot of the situation ■ Single interval ■ Steady state ■ Dynamic ■ Evaluate scenarios that change overtime ■ Time dependent ■ Represent trends and patterns over time
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- 11. Structure of Mathematical Modelsfor Decision Support ■ Quantitative models ■ components : result variables, decision variables, uncontrollable variables, intermediate result variables ■ Mathematical relationships link the components.
- 12. Structure of Mathematical Modelsfor Decision Support ■ The components of a quantitative model are linked together by mathematical (algebraic) expressions — equations or inequalities. ■ example : simple financial model : P = R-C where P = profit, R = revenue ■ example : determine the present value of a payment of $100,000 to be made 5 years from today, at a 10 percent (0.1) interest rate : P = 100,000/(1 + 0.1)5 = 62,092
- 13. Certainty, Uncertainty, and Risk ■The Zones of Decision Making
- 14. Decision Making Under Certainty,Uncertainty, and Risk ■ Under Certainty ■ assumed complete knowledge is available ■ all potential outcomes are known ■ optimal solutions ■ Under Uncertainty ■ several outcomes for each course of action ■ the probability of outcomes is unknown ■ there is unsufficient information ■ Under Risk ■ must consider several possible outcomes for each alternatives ■ calculated risk
- 15. Decision Modeling with Spreadsheet ■Most popular end-user modeling tool ■ Important tool for analysis, planning, and modeling ■ Flexibility and easy-to-use ■ Powerful function (add-in functions) ■ What-if analysis, goal seeking, data management, and programmability ■ Seamless integration ■ Can build static or dynamic model
- 16. Multiple Goals, SensitivityAnalysis, What- if Analysis, and Goal Seeking ■ Multiple Goals ■ Simple-goal vs. multiple goals ■ Management system are much more complex ■ attain simultaneous goal ■ Several methods of handling multiple goals : ■ Utility theory ■ Goal programming ■ Expression of goals as constraints ■ A points system
- 17. Multiple Goals, SensitivityAnalysis, What- if Analysis, and Goal Seeking ■ Certain difficulties may arise when analyzing multiple goals : ■ difficult to obtain an explicit statement of the organization’s goals ■ the importance assigned to specific goals over time ■ Goals and sub-goals are viewed differently at various levels ■ Goals change in response to changes ■ Complex problems are solved by groups of decision makers ■ ….
- 18. Multiple Goals, SensitivityAnalysis, What- if Analysis, and Goal Seeking ■ Sensitivity analysis ■ attempts to assess the impact of a change in the input data or parameters on the proposed solution ■ important in MSS -> allows flexibility and adaptation ■ used for : ■ eliminate too-large sensitivities ■ adding details about sensitive variables ■ obtaining better estimates ■ reduce actual sensitivities ■ continuous and close monitoring of actual results ■ Two types : automatic and trial-and-error
- 19. Multiple Goals, SensitivityAnalysis, What- if Analysis, and Goal Seeking ■ What-if analysis ■ structured as “What will happen to the solution if an input variable, an assumption, or a parameter value is changed?” ■ can perform multiple cases ■ Goal Seeking ■ calculates the values of the inputs necessary to achieve a desired level of an output (goal). ■ backward solution approach ■ example : computing break-even points
- 20. Decision Analysis withDecision Tables and Decision Trees ■ Decision analysis - involve a finite and usually not too large number of alternatives ■ Decision Tables - tabular representation of the decision situation ■ Investment example : ■ goal : maximizing the yield on the investment after one year ■ yield depends on the state of economy : ■ solid ■ stagnation ■ inflation
- 21. Decision Table - Investmentexamples ■ If there is solid growth in the economy, bonds will yield 12 percent, stocks 15 percent,and time deposits 6.5 percent. ■ If stagnation prevails, bonds will yield 6 percent, stocks 3 percent, and time deposits 6.5 percent. ■ If inflation prevails, bonds will yield 3 percent, stocks will bring a loss of 2 percent, and time deposits will yield 6.5 percent.
- 22. Decision Table - Investmentexamples ■ Treating Uncertainty ■ optimistic approach : best possible outcome of each alternative, select the best of best ■ pessimistic approach : worst possible outcome, select best of these ■ Treating Risk ■ Use known probabilities ■ expected values
- 23. Decision Table - Investmentexamples ■ Multiple goals ■ Yield, safety, liquidity
- 24. Decision Trees ■ Decisiontree - shows the relationships of the problem graphically, can handle complex situations ■ Multiple criteria approach ■ Tools include : ■ Mind tools ■ TreeAge Software ■ Palisade Corp
- 25. Multi-Criteria Decision Makingwith Pairwise Comparisons ■ One of the most effective approach : ■ use weights based on decision making priorities ■ Analytic Hierarchy Process (AHP) ■ excellent for representing multi-criteria ■ to decompose a decision making into relevant criteria ■ manipulates quantitative and qualitative decision making criteria ■ Popular tools - Expert Choice ■ Web-based tools - Web-HIPRE
- 26. End of theChapter ■Questions / Comments…
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