CHAPTER 5 Modelling and Analysis 1

Modelling and Analysis Major DSS component Model base and model management CAUTION Familiarity with major ideas Basic concepts and definitions Tool--influence diagram Model directly in spreadsheets Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Modelling and Analysis Structure of some successful models and methodologies Decision analysis Decision trees Optimization Heuristic programming Simulation New developments in modelling tools / techniques Important issues in model base management

Modelling and Analysis Topics Modelling for MSS Static and dynamic models Treating certainty, uncertainty, and risk Influence diagrams MSS modelling in spreadsheets Decision analysis of a few alternatives (decision tables and trees) Optimization via mathematical programming Heuristic programming Simulation Multidimensional modelling -OLAP Visual interactive modelling and visual interactive simulation Quantitative software packages - OLAP Model base management

Modelling for MSS Key element in most DSS Many classes of models Specialized techniques for each model Allows for rapid examination of alternative solutions Multiple models often included in a DSS Trend toward transparency Necessity in a model-based DSS Can lead to massive cost reduction / revenue increases

Good Examples of MSS Models DuPont rail system simulation model (opening vignette) Procter & Gamble optimization supply chain restructuring models (see presentation pgscredesign.ppt) Scott Homes AHP select a supplier model IMERYS optimization clay production model

Dupont Simulates Rail Transportation System and Avoids Costly Capital Expense Vignette Promodel simulation created representing entire transport system Applied what-if analyses Visual simulation Identified varying conditions Identified bottlenecks Allowed for downsized fleet without downsizing deliveries

Major Modelling Issues Problem identification Environmental analysis Variable identification Forecasting Multiple model use Model categories or selection (Table 5.1) Model management Knowledge-based modelling Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Static and Dynamic Models Static Analysis Single snapshot Dynamic Analysis Dynamic models Evaluate scenarios that change over time Time dependent Trends and patterns over time Extend static models

Treating Certainty, Uncertainty, and Risk Certainty Models Uncertainty Risk Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Influence Diagrams Graphical representations of a model Model of a model Visual communication Some packages create and solve the mathematical model Framework for expressing MSS model relationships Rectangle = a decision variable Circle = uncontrollable or intermediate variable Oval = result (outcome) variable: intermediate or final Variables connected with arrows Example (Figure 5.1) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Analytica Influence Diagram of a Marketing Problem: The Marketing Model http://www.youtube.com/watch?v=dSzvuMGJTlk

MSS Modelling in Spreadsheets Spreadsheet: most popular end-user modelling tool Powerful functions Add-in functions and solvers Important for analysis, planning, modelling Programmability (macros) (More) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

MSS Modelling in Spreadsheets What-if analysis Goal seeking Simple database management Seamless integration Microsoft Excel Lotus 1-2-3 Excel spreadsheet static model example of a simple loan calculation of monthly payments (Figure 5.3) Excel spreadsheet dynamic model example of a simple loan calculation of monthly payments and effects of prepayment http://www.youtube.com/watch?v=z7pjvTwoz8I&feature=related

Decision Analysis of Few Alternatives (Decision Tables and Trees) Single Goal Situations Decision tables Decision trees Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Decision Tables Investment example One goal: maximize the yield after one year Yield depends on the status of the economy (the state of nature) Solid growth Stagnation Inflation Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Possible Situations 1. If solid growth in the economy, bonds yield 12%; stocks 15%; time deposits 6.5% 2. If stagnation, bonds yield 6%; stocks 3%; time deposits 6.5% 3. If inflation, bonds yield 3%; stocks lose 2%; time deposits yield 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

View Problem as a Two-Person Game Payoff Table 5.2 Decision variables (alternatives) Uncontrollable variables (states of economy) Result variables (projected yield) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Table 5.2: Investment Problem Decision Table Model States of Nature Solid Stagnation Inflation Alternatives Growth Bonds 12% 6% 3% Stocks 15% 3% -2% CDs 6.5% 6.5% 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Treating Uncertainty Optimistic approach Pessimistic approach Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Treating Risk Use known probabilities (Table 5.3) Risk analysis: compute expected values Can be dangerous Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Table 5.3: Decision Under Risk and Its Solution Solid Stagnation Inflation Expected Growth Value Alternatives .5 .3 .2 Bonds 12% 6% 3% 8.4% * Stocks 15% 3% -2% 8.0% CDs 6.5% 6.5% 6.5% 6.5% Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Other methods of treating risk Decision Trees Other methods of treating risk Simulation Certainty factors Fuzzy logic Multiple goals Yield, safety, and liquidity (Table 5.4)

Copyright 2001, Prentice Hall, Upper Saddle River, NJ Table 5.4: Multiple Goals Alternatives Yield Safety Liquidity Bonds 8.4% High High Stocks 8.0% Low High CDs 6.5% Very High High Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Table 5.5: Discrete vs. Continuous Probability Distribution Daily Discrete Continuous Demand Probability 5 .1 Normally distributed with 6 .15 a mean of 7 and a 7 .3 standard deviation of 1.2 8 .25 9 .2 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition Copyright 2001, Prentice Hall, Upper Saddle River, NJ