INFSY540.1 Information Resources in Management

1. INFSY540.1Information Resources in Management Lesson #4 Chapters 8 Models and Decision Support

2. Information Systems & Technology An information system (IS) is an arrangement of people, data, processes, communications, and information technology that interact to support and improve day-to-day operations in a business as well as support the problem-solving and decision making needs of management and users. Information technology is a contemporary term that describes the combination of computer technology (hardware and software) with telecommunications technology (data, image, and voice networks). A practical way of making data useful.

3. What is an information system?

4. What is an information system? Information System Transaction Processing System Decision Support System Data-Driven DSS Model-Driven DSS

5. Information Systems • Transaction Processing Systems • aka Data Processing Systems • Decision Support Systems • Executive Information Systems • Management Information Systems • Expert Systems • Office, Workgroup, Personal Information Systems Our text does not have any of these being DSS subsets

6. Data-Driven Decision Support • Using Transaction Processing Systems for anything but processing transactions is hard: • Not easily accessible • Mainframes Cost • Mainframe Complexity • Mainframes open to many users is risky • Data spread to many databases and computers • But users now have powerful PCs with user friendly analysis tools & they want to use them

7. Data-Driven Decision Support • History: • On Line Transaction Processing (OLTP) • DataBase Management System (DBMS) Indexed Sequential Access Method (ISAM) • Relational DataBase Management System (RDBMS) Structured Query Language (SQL) Executive Information Systems (EIS) • Data Warehouse On Line Analytical Processing (OLAP)

8. Front- and Back-Office Information Systems • Front-officeinformation systems support business functions that reach out to customers (or constituents). • Marketing • Sales • Customer management • Back-officeinformation systems support internal business operations and interact with suppliers (of materials, equipment, supplies, and services). • Human resources • Financial management • Manufacturing • Inventory control

9. What is a model? • Webster’s New American Dictionary (1995) • One who poses for an artist. • An example for imitation or emulation • A miniature representation • A structural design • Model ( verb): to shape, fashion, construct • “A model is a simplification of something else.” Bob Kilmer

10. MODEL INPUTS OUTPUTS ASSUMPTIONS Models and Analysis

11. Assumptions and Conclusions The aviation instructor had just delivered a lecture on the use of parachutes. “And if it doesn’t open?” someone asked. “If it doesn’t open?” replied the instructor, “Well, ... that is what’s known as jumping to a conclusion.”

12. MODEL INPUTS OUTPUTS ASSUMPTIONS GIGO INPUTS Constants Parameters Variables OUTPUTS Criteria or MOE Additional Statistics

13. Types of Models • Mental • Symbolic • Mathematical • Computer • Physical

14. Sample Model Determining Production Levels in Perfect Competition $ Marginal cost Average total cost price Q* Quantity

15. Order Model vice-presidents Decide if we should produce accounting manager production manager warehouse manager summarize sales orders review costs add fixed costs marketing manager check stock to match order decide steps to produce sales manager review sales orders compute costs to produce engineers receive sales orders bill customers sales staff Simple Model of Evaluating Custom Orders customer

16. Models ofPhysical Items:CAD Computer-aided design. Designers traditionally build models before attempting to create a physical product. CAD systems make it easier to create diagrams and share them with multiple designers. Portions of drawings can be stored and used in future products. Sample products can be evaluated and tested using a variety of computer simulations.

17. Statistical Decision Models Strategy Decision Output Model Tactics Data Operations Company

18. Understand the Process Prediction Optimization Simulation To conduct "What If" analysis Dangers File: C08Fig08.xls Why Build Models?

19. Acquisition/Input Data availability Selective perception Frequency Concrete information Illusory correlation Processing Inconsistency Conservatism Non-linear extrapolation Heuristics: Rules of thumb Anchoring and adjustment Representativeness Sample size Justifiability Regression bias Best guess strategies Complexity Emotional stress Social pressure Redundancy Output Question format Scale effects Wishful thinking Illusion of control Feedback Learning on irrelevancies Misperception of chance Success/failure attribution Logical fallacies in recall Hindsight bias Human Biases

20. 25 20 Economic/ 15 regression Forecast Output 10 5 Moving Average Trend/Forecast 0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Time/quarters File: C08Fig09.xls Prediction

21. Goal or output variables 25 20 15 Results from altering internal rules Output 10 5 0 1 2 3 4 5 6 7 8 9 10 Input Levels File: C08Fig10.xls Simulation

22. Maximum Goal or output variables 25 20 Model: defined by the data points 15 or equation Output 10 5 5 3 0 1 2 3 4 1 5 6 7 8 9 10 Input Levels Control variables File: C08Fig08.xls Optimization

23. Figure 10.2

24. Simulation • Webster’s New American Dictionary (1995) • An object that is not genuine • The imitation by one system or process of the way in which another system or process works. • Simulate (verb): imitate, create the effect or appearance of • Handbook of Systems Analysis (1985), E. S. Quade • “The process of representing item by item and step by step the essential features of whatever it is we are interested in.”

25. Bob Kilmer’s Simple Definitions: • Model: simplified representation of something else.* • Simulation: means of using or operating a model.** * Something else = a real or proposed entity or system ** Must have inputs and outputs.

26. Building Models Process Equation: output = f(input,time) Input Output Define System Input - Process - Output Simplifying assumptions System boundary Build Equations Identify parameters (variables you can control) Identify variables you cannot control Define equations for the variables Estimate parameters from data Use Model to transform Inputs into Outputs

27. Modeling Limitations • Model complexity • Cost of building model • Errors in model • Data • Equations • Presentation and interpretation

28. Models are for... • “Models are for thinking with.” -- Sir M. G. Kendall • “Models are for experimenting with.” • “Models are for communicating with.” • “Models always have assumptions.” (Even though they might not be stated) • “Models are always wrong. They always have error.” (Question: Is the level of error acceptable?)

29. EOQ Model

30. Marketing Future sales Consumer preferences/trends Sales strategies Finance Interest rates Cash flows Financial market conditions HRM Labor costs Absenteeism Turnover Strategy Rivals’ actions Technological change Market conditions Appendix: Forecasting Uses

31. Structural Models Derive underlying models Estimate parameters Evaluate model Focus on explanation and cause Time Series Collect data over time Identify trends Identify seasonal effects Forecast based on patterns Forecasting Methods sales P trend S D’ D time Q Increase in income

32. Demand is a function of Price Income Prices of related products Structural Equations Model QD = b0 + b1 Price + b2 Income + b3 Substitute Data Estimate QD = 1114 - 0.1 Price + 1.2 Income - 1.0 Substitute Forecast 33318 = 1114 - 0.1 (155) + 1.2 (20000) - 1.0 (160) Need to know (estimate) future price, income, and substitute price.

33. Time Series Components sales Seasonal Trend time Dec Dec Dec Dec 1. Trend 2. Seasonal 3. Cycle 4. Random A cycle is similar to the seasonal pattern, but covers a time period longer than a year.

34. Exponential Smoothing St = Yt + (1 - ) St-1 Use Excel: Tools, Data Analysis Exponential Smoothing S is the new data point  is the smoothing factor

35. Exponential Smoothing Choosing the smoothing factor (): It is usually between 0.01 and 0.20 Test multiple values and compare errors: (actual - smooth) * (actual - smooth) Compute the sum. Choose the factor with the least total sum-of-squared error. Larger factors place more importance on recent data, which results in less smoothing. (A2-D2)*(A2-D2) Sum Sum Sum 929,916 848,686 769,265

36. Smoothing with Trends Apply exponential smoothing and choose smoothing factor (). Apply exponential smoothing a second time to the smoothed data.

37. Forecasting with Exponential Smoothing Forecast for time T+ T = 20 last of the raw data  = 1 forecast one period ahead  = 0.2 smoothing factor S20 = 32,064 (value at time 20, after one smoothing) S[2] = 33,141 (value at time 20, after second smoothing) Y21 = (2.25)32,064 - (1.25)33,141 = 30,718

38. Estimating Trend Yt = b0 + b1(t) Use regression to estimate b0 and b1. Plug t into equation to estimate new value (on trend): Y21 = 23,986 + 498.6 * (21) = 34,456 Result is the prediction on the trend, with no random factors and no cycles.

39. An Overview of Decision Support Systems

40. File: C08Fig11.xls DSS: Decision Support Systems Sales and Revenue 1994 300 Model 250 Legend 200 Sales Revenue Profit 150 Prior results sales revenue profit prior 100 154 204.5 45.32 35.72 50 163 217.8 53.24 37.23 0 161 220.4 57.17 32.78 Jan Feb Mar Apr May Jun 173 268.3 61.93 47.68 Output 143 195.2 32.38 41.25 181 294.7 83.19 67.52 data to analyze Database

41. Characteristics of Decision Support Systems • Handle lots of data from various sources • Report & presentation flexibility • Text and graphics capabilities • Support drill down analysis • Complex analysis, statistics, and forecasting • Optimization, satisficing, heuristics • Simulation • What-if analysis • Goal-seeking analysis

42. Figure 10.14

43. Capabilities of a DSS • Support all problem-solving phases • Support different decision frequencies • Support different problem structures • Support various decision-making levels

44. The Model Base • Financial models • Cash flow • Internal rate of return • Statistical analysis models • Averages, standard deviations • Correlations • Regression analysis • Graphical models • Project management models

45. Table 10.3

46. Group Decision Support Systems

47. Characteristics of a GDSS • Special design • Ease of use • Flexibility • Decision-making support

48. Characteristics of a GDSS • Anonymous input • Reduction of negative group behavior • Parallel communication • Automated record keeping

49. Figure 10.18

50. Executive Support Systems (ESS) • Tailored to individual executives • Easy to use • Drill down capabilities • Access to external data • Can help when uncertainty is high • Future-oriented • Linked to value-added processes.