In this chapter you will learn: How can information systems be used to support teams of workers? How many different ways are there to communicate with team members? Which method is best for each type of message? How can several people work on the same documents?,...
Introduction to MIS Chapter Models and Decision Support Copyright © 1998-2002 by Jerry Post Introduction to MIS Models Strategy Decision 100 80 60 40 20 1st Qtr 2nd Qtr Actual 3rd Qtr 4th Qtr Forecast Output f ( x) x exp 2 Model Data Tactics Operations Company Introduction to MIS 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 Introduction to MIS Tactical Models Management Business Operations Introduction to MIS Tr a Pr nsa Pr o oc c es ces tion s C sin on g t ro l Mgt DS S Strategic ES EI S Decision Levels Choose a Stock Stock Price 130 125 120 115 CompanyA 110 CompanyB 105 100 95 90 10 11 12 Month Company A’s share price increased by 2% per month Company B’s share price was flat for months and then increased by 3% per month Which company would you invest in? Introduction to MIS Data availability Selective perception Frequency Concrete information Illusory correlation 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 Introduction to MIS Output Processing Human Biases Acquisition/Input 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 File: C08Fig08.xls Understanding the Process Optimization Prediction Simulation or "What If" Scenarios Dangers Goal or output Why Build Models? Optimization Maximum variables 25 Output 20 Model: defined by the data points or equation 15 10 5 3 Input Levels 10 Control variables Introduction to MIS File: C08Fig09.xls Prediction 25 20 Economic/ regression Forecast Output 15 10 Moving Average Trend/Forecast Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Time/quarters Introduction to MIS File: C08Fig10.xls Simulation Goal or output variables 25 Output 20 15 Results from altering internal rules 10 5 10 Input Levels Introduction to MIS Object-Oriented Simulation Models Custom Manufacturing purchase order routing & scheduling purchase order Customer Order Entry Invoice Parts List Production Shipping Schedule Shipping Inventory Introduction to MIS 10 File: C08-25 GIS.xls Geographic Models City Clewiston Fort Myers Gainesville Jacksonville Miami Ocala Orlando Perry Tallahassee Tampa 1990 pop 2000 pop 1990 per capita 2000 income per capita 1990 income soft sales 1990 hard sales 2000 hard sales 2000 soft sa 6085 8549 13598 15466 562.5 452.0 367.6 525.4 45206 59491 16890 20256 652.9 535.2 928.2 1010.3 84770 101724 13672 19428 281.7 365.2 550.5 459.4 635230 734961 15316 19275 849.1 990.2 1321.7 1109.3 258548 300691 16874 18812 833.4 721.7 967.1 1280.6 42045 55878 12027 15130 321.7 359.0 486.2 407.3 164693 217889 16958 20729 509.2 425.7 691.5 803.5 7151 8045 11055 14144 267.2 300.1 452.9 291.0 124773 155218 14578 20185 489.7 595.4 843.8 611.7 280015 335458 15081 19062 851.0 767.4 953.4 1009.1 Introduction to MIS 29 Red Yellow Red 3.2 2.3 5.0 4.2 2.3 1.9 3.7 3.2 Green Tallahassee Blue Red Yellow Green 2.1 1.7 1.4 1.1 Green Red Blue Yellow Red 2000 Soft Goods 1990 Hard Goods 1990 Soft Goods 20,700 19,400 14,600 18,100 1.1 1.1 1.0 Gainesville 16,800 12,200 15,500 1990 1.8 1.5 1.4 1.2 Green Blue Red Orlando Red 3.6 3.8 2.9 3.2 Green Yellow 2.6 3.0 1.6 1.9 Yellow Tampa Green Blue Blue Red Red Yellow 3.5 3.8 2.0 2.5 Green 13,400 Yellow Blue Ocala per capita income 17,000 15,800 1.7 Green Blue Jacksonville Perry 2000 Hard Goods Yellow Fort Myers Clewiston Yellow 1.4 2.0 1.7 2.1 Green Blue Miami Blue Red 2000 3.7 4.8 2.7 3.2 Green Introduction to MIS Yellow Blue 30 Cases: Computer Hardware Industry Introduction to MIS 31 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? Introduction to MIS 32 Appendix: Forecasting Uses Marketing Future sales Consumer preferences/trends Sales strategies Finance Labor costs Absenteeism Turnover Strategy Interest rates Cash flows Financial market conditions Introduction to MIS HRM Rivals’ actions Technological change Market conditions 33 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 sales P trend S D’ D Increase in income Introduction to MIS Q time 34 Structural Equations Demand is a function of Price Income Prices of related products 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 Introduction to MIS 35 Time Series Components sales Seasonal Trend Dec Dec Trend Seasonal Cycle Random Introduction to MIS Dec Dec time A cycle is similar to the seasonal pattern, but covers a time period longer than a year 36 Exponential Smoothing Exponential Smoothing 1600 1500 1400 1300 Raw Data 1200 1100 1000 Smooth:0.20 900 800 11 13 15 17 19 21 St = Yt + (1 - ) St-1 S is the new data point is the smoothing factor Introduction to MIS Use Excel: Tools, Data Analysis Exponential Smoothing 37 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 929,916 Introduction to MIS Sum 848,686 Sum 769,265 38 Smoothing with Trends Double Exponential Smoothing 34000 32000 30000 28000 Raw Data 26000 Smooth:0.20 24000 22000 20000 11 13 15 17 19 Apply exponential smoothing and choose smoothing factor ( ) Apply exponential smoothing a second time to the smoothed data Introduction to MIS 39 Forecasting with Exponential Smoothing Forecast for time T+ yT ST 1 ST[ ] T = 20 =1 = 0.2 S20 = 32,064 last of the raw data forecast one period ahead smoothing factor (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 Introduction to MIS 40 Trendline Linear Trend Line 45000 y = 511.63x + 24099 40000 35000 30000 25000 Linear captures the trend only 20000 15000 10000 5000 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Moving Average(3) Trend Line Moving average captures all elements, but lags the actual pattern 45000 40000 35000 30000 25000 20000 15000 10000 5000 Introduction to MIS 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 41 Regression Analysis Time Sales Forecast =$F$20+$F$21*B6 Tools + Data Analysis + Regression Dependent = Sales Independent = Time Introduction to MIS 42 Time Quantity Trend Difference 24917 24484 432 26152 24983 1169 27297 25482 1816 26157 25980 177 26710 26479 231 26103 26977 -874 27981 27476 505 26327 27975 -1647 24913 28473 -3560 10 28524 28972 -448 11 29774 29470 303 12 29136 29969 -833 13 29332 30468 -1136 14 30306 30966 -660 15 32133 31465 669 16 33329 31963 1366 17 34522 32462 2060 18 34769 32961 1808 19 33355 33459 -104 20 32684 33958 -1274 21 34456 22 34955 23 35454 24 35952 Introduction to MIS Estimating Trend Yt = b0 + b1(t) Use regression to estimate b0 and b1 Intercept Time Coefficients Std Error t Stat P-value 23985.81 652.48 36.76 2.2E-18 498.60 54.47 9.15 3.4E-08 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 43 ... 990.2 1321.7 1109.3 2 585 48 300691 1 687 4 188 12 83 3.4 721.7 967.1 1 280 .6 42045 5 587 8 12027 15130 321.7 359.0 486 .2 407.3 164693 21 788 9 169 58 20729 509.2 425.7 691.5 80 3.5 7151 80 45 11055 14144 267.2... 80 3.5 7151 80 45 11055 14144 267.2 300.1 452.9 291.0 124773 1552 18 145 78 20 185 489 .7 595.4 84 3 .8 611.7 280 015 3354 58 15 081 19062 85 1.0 767.4 953.4 1009.1 Introduction to MIS 29 Red Yellow Red... 33,562 87 ,341 15, 983 136 ,88 6 Accounts Payable Notes Payable Accruals Total Current Liabilities 45,673 182 ,559 Bonds Common Stock Ret Earnings Liabs + Equity 32 ,87 2 54,327 11,764 98, 963 14, 982 57 ,86 4