1. 1 Copyright © 1998 by Jerry Post INFSY540.1 Information Resources in Management Lesson #4 Chapters 8 Models and Decision Support

2. 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. 3 What is an information system?

4. 4 Information System Transaction Processing System Decision Support System Model-Driven DSSData-Driven DSS

5. 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. 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. 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. 8 Front- and Back-Office Information Systems  Front-office information systems support business functions that reach out to customers (or constituents).  Marketing  Sales  Customer management  Back-office information systems support internal business operations and interact with suppliers (of materials, equipment, supplies, and services).  Human resources  Financial management  Manufacturing  Inventory control

9. 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. 10 Models and Analysis INPUTS MODEL OUTPUTS ASSUMPTIONS

11. 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. 12 GIGO INPUTS MODEL OUTPUTS ASSUMPTIONS INPUTS Constants Parameters Variables OUTPUTS Criteria or MOE Additional Statistics

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

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

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

16. 16 Models of Physical 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. 17 Statistical Decision Models Data Model Decision Output Strategy Operations Tactics Company

18. 18 File: C08Fig08.xlsC08Fig08.xls Why Build Models?  Understand the Process  Prediction  Optimization  Simulation  To conduct "What If" analysis  Dangers

19. 19 Human Biases  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

20. 20 Prediction 0 5 10 15 20 25 Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2 Time/quarters Output Moving Average Trend/Forecast Economic/ regression Forecast File: C08Fig09.xlsC08Fig09.xls

21. 21 Simulation Goal or output variables Results from altering internal rules File: C08Fig10.xlsC08Fig10.xls

22. 22 Maximum Model: defined by the data points or equation Control variables Goal or output variables File: C08Fig08.xlsC08Fig08.xls Optimization

23. 23 Figure 10.2

24. 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. 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. 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. 27 Modeling Limitations  Model complexity  Cost of building model  Errors in model  Data  Equations  Presentation and interpretation

28. 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. 29 EOQ Model

30. 30 Appendix: Forecasting Uses  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

31. 31 Forecasting Methods  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 Q P S D D’ Increase in income time sales trend

32. 32 Structural Equations  Demand is a function of  Price  Income  Prices of related products Q D = b0 + b1 Price + b2 Income + b3 Substitute Q D = 1114 - 0.1 Price + 1.2 Income - 1.0 Substitute Model Estimate Data Forecast33318 = 1114 - 0.1 (155) + 1.2 (20000) - 1.0 (160) Need to know (estimate) future price, income, and substitute price.

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

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

35. 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. Sum (A2-D2)*(A2-D2) 929,916848,686769,265 Larger factors place more importance on recent data, which results in less smoothing.

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

37. 37 Forecasting with Exponential Smoothing Forecast for time T+  T = 20last of the raw data  = 1forecast one period ahead  = 0.2smoothing factor S 20 = 32,064(value at time 20, after one smoothing) S [2] = 33,141(value at time 20, after second smoothing) Y 21 = (2.25)32,064 - (1.25)33,141 = 30,718

38. 38 Estimating Trend Y t = b 0 + b 1 (t) Use regression to estimate b 0 and b 1. Plug t into equation to estimate new value (on trend): Y 21 = 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. 40 DSS: Decision Support Systems salesrevenueprofitprior 154204.545.3235.72 163217.853.2437.23 161220.457.1732.78 173268.361.9347.68 143195.232.3841.25 181294.783.1967.52 Sales and Revenue 1994 JanFebMarAprMayJun 0 50 100 150 200 250 300 Legend Sales Revenue Profit Prior Database Model Output data to analyze results File: C08Fig11.xlsC08Fig11.xls

41. 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. 42 Figure 10.14

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

44. 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. 45 Table 10.3

46. Group Decision Support Systems

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

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

49. 49 Figure 10.18

50. 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.

51. 51 Capabilities of an ESS  Support for defining an overall vision  Support for strategic planning  Support for strategic organizing & staffing  Support for strategic control  Support for for crisis management

52. 52 EIS: Enterprise Information System  Easy access to data  Graphical interface  Non-intrusive  Drill-down capabilities EIS Software from Lightship highlights ease- of-use GUI for data look-up.

53. 53 Enterprise IS Production Distribution Sales Central Management Executives Data Sales Production Costs Distribution Costs Fixed Costs Production Costs South North Overseas Production: North Item#19951994 1234542.1442.3 2938631.3153.5 7319753.1623.8 Data for EIS Data