Introduction to MIS

1. Introduction to MIS Chapter 8 Models and Decision Support

2. Models Strategy Decision Output Model Tactics Data Operations Company

3. Outline • Biases in Decisions • Introduction to Models • Why Build Models? • Decision Support Systems: Database, Model, Output • Data Warehouse • Data Mining and Analytical Processing • Digital Dashboard and EIS • DSS Examples • Geographical Information Systems • Cases: Computer Hardware Industry • Appendix: Forecasting

4. Choose a Stock Company A’s share price increased by 2% per month. Company B’s share price was flat for 5 months and then increased by 3% per month. Which company would you invest in?

5. 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

6. Understanding the Process Optimization Prediction Simulation or "What If" Scenarios Dangers 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 Why Build Models? Optimization

7. 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

8. 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

9. purchase order purchase order routing & scheduling Invoice Parts List Shipping Schedule Object-Oriented Simulation Models Custom Manufacturing Customer Order Entry Production Shipping Inventory

10. 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

11. Data Mining: Spotfire http://www.spotfire.com

12. Data Warehouse Predefined reports Interactive data analysis Operations data Daily data transfer OLTP Database 3NF tables Data warehouse Star configuration Flat files

13. Multidimensional OLAP Cube Pet Store Item Sales Amount = Quantity*Sale Price Category Customer Location Time Sale Date

14. Microsoft SQL Server Cube Browser

15. Microsoft Pivot Table

16. Digital Dashboard Stock market Equipment details Exceptions Quality control Plant or management variables Products Plant schedule http://www.microsoft.com/business/casestudies/dd/honeywell.asp

17. Executive IS Sales Production Costs Distribution Costs Fixed Costs Production Costs South North Overseas Executives Data for EIS Central Management Production: North Item# 1995 1994 1234 542.1 442.3 2938 631.3 153.5 7319 753.1 623.8 Data Data Sales Data Production Distribution Data

18. Marketing Research Data

19. File: C08-10 Marketing Forecast.xls Marketing Sales Forecast forecast Note the fourth quarter sales jump. The forecast should pick up this cycle.

20. Regression Forecasting Data: Quarterly sales and GDP for 10 years. Model: Sales = b0 + b1 Time + b2 GDP Analysis: Estimate model coefficients with regression. Forecast GDP for each quarter. Compute Sales prediction. Graph forecast. Output:

21. File: C08-19 HRM.xls Human Resources

22. Human Resources

23. File: C08-14 Finance NPV.xls Finance Example: Project NPV Rate = 7% Can you look at these cost and revenue flows and tell if the project should be accepted?

24. File: C08-15 Accounting.xls Accounting Balance Sheet for 2003 Cash 33,562 Accounts Payable 32,872 Receivables 87,341 Notes Payable 54,327 Inventories 15,983 Accruals 11,764 Total Current Assets 136,886 Total Current Liabilities 98,963 Bonds 14,982 Common Stock 57,864 Net Fixed Assets 45,673 Ret. Earnings 10,750 Total Assets 182,559 Liabs. + Equity 182,559

25. Accounting Income Statement for 2003 Sales $97,655 tax rate 40% Operating Costs 76,530 dividends 60% Earnings before interest & tax 21,125 shares out. 9763 Interest 4,053 Earnings before tax 17,072 taxes 6,829 Net Income 10,243 Dividends 6,146 Add. to Retained Earnings 4,097 Earnings per share $0.42

26. Accounting Analysis Balance Sheet projected 2004 Income Statement projected 2004 Sales $ 107,421 Cash $36,918 Acts Receivable 96,075 Inventories 17,581 Accts Payable $36,159 Notes Payabale 54,327 Accruals 12,940 1 2 2 Operating Costs 84,183 Earn. before int. & tax 23,238 Interest 4,306 5 Total Cur. Liabs. 103,427 Total Cur. Assets 150,576 Earn. before tax 18,931 Bonds 14,982 Common Stock 57,864 Ret. Earnings 14,915 taxes 8,519 Net Fixed Assets 45,673 3 Net Income 10,412 Total Assets $196,248 Liabs + Equity 191,188 Dividends 6,274 Add. Funds Need 5,060 Add. to Ret. Earnings $ 4,165 Bond int. rate 5% 4 Earnings per share $0.43 Added interest 253 Tax rate 45% Dividend rate 60% Shares outstanding 9763 1 Forecast sales and costs. Sales increase 10% Operations cost increase 10% Forecast cash, accts receivable, accts payable, accruals. 2 Add gain in retained earnings. 3 Compute funds needed and interest cost. 4 Results in a CIRCular calculation. Add new interest to income statement. 5

27. File: C08-25 GIS.xls Geographic Models

28. Tampa 17,000 20,700 15,800 19,400 14,600 18,100 13,400 16,800 12,200- 15,500- Tallahassee Jacksonville Perry Gainesville 2000 Hard Goods 2000 Soft Goods Ocala 1990 Hard Goods 1990 Soft Goods Orlando per capita income Clewiston Fort Myers Miami 1990 2000

29. Cases: Computer Hardware Industry

30. Cases: Dell Computer Gateway 2000, Inc. www.dell.com www.gateway.com What is the company’s current status? What is the Internet strategy? How does the company use information technology? What are the prospects for the industry?

31. 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

32. 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

33. 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.

34. 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.

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

36. Regression Analysis Time Sales Forecast =$F$20+$F$21*B6 Tools + Data Analysis + Regression Dependent = Sales Independent = Time