Introduction to MIS2 Models Data Model Decision Output Strategy Operations Tactics Company
Introduction to MIS3 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 MIS4 Decision Levels Business Operations Tactical Management Strategic Mgt. EIS ES DSS Transaction Processing Process Control Models
Introduction to MIS5 Choose a Stock Company As share price increased by 2% per month. Company Bs share price was flat for 5 months and then increased by 3% per month. Which company would you invest in?
Introduction to MIS6 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
Introduction to MIS7 Optimization Maximum Model: defined by the data points or equation Control variables Goal or output variables File: C08Fig08.xlsC08Fig08.xls Why Build Models? Understanding the Process Optimization Prediction Simulation or "What If" Scenarios Dangers
Introduction to MIS8 Prediction Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2 Time/quarters Output Moving Average Trend/Forecast Economic/ regression Forecast File: C08Fig09.xlsC08Fig09.xls
Introduction to MIS9 Simulation Goal or output variables Results from altering internal rules File: C08Fig10.xlsC08Fig10.xls
Introduction to MIS10 Object-Oriented Simulation Models Customer purchase order Order Entry Custom Manufacturing purchase order routing & scheduling Production Inventory Shipping Parts List Shipping Schedule Invoice
Introduction to MIS11 DSS: Decision Support Systems salesrevenueprofitprior Sales and Revenue 1994 JanFebMarAprMayJun Legend Sales Revenue Profit Prior Database Model Output data to analyze results File: C08Fig11.xlsC08Fig11.xls
Introduction to MIS12 Data Mining: Spotfire
Introduction to MIS13 Data Warehouse OLTP Database 3NF tables Operations data Predefined reports Data warehouse Star configuration Daily data transfer Interactive data analysis Flat files
Introduction to MIS14 Multidimensional OLAP Cube Time Sale Date Customer Location Category Pet Store Item Sales Amount = Quantity*Sale Price
Introduction to MIS15 Microsoft SQL Server Cube Browser
Introduction to MIS16 Microsoft Pivot Table
Introduction to MIS17 Digital Dashboard Stock market Exceptions Plant or management variables Equipment details Products Quality control Plant schedule
Introduction to MIS18 EIS: Executive 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.
Introduction to MIS19 Executive IS Production Distribution Sales Central Management Executives Data Sales Production Costs Distribution Costs Fixed Costs Production Costs South North Overseas Production: North Item# Data for EIS Data
Introduction to MIS20 Marketing Research Data
Introduction to MIS21 Marketing Sales Forecast forecast Note the fourth quarter sales jump. The forecast should pick up this cycle. File: C08-10 Marketing Forecast.xlsC08-10 Marketing Forecast.xls
Introduction to MIS22 Regression Forecasting Sales = b0 + b1 Time + b2 GDPModel: Data:Quarterly sales and GDP for 10 years. Analysis:Estimate model coefficients with regression. Forecast GDP for each quarter. Output: Compute Sales prediction. Graph forecast.
Introduction to MIS23 Human Resources File: C08-19 HRM.xlsC08-19 HRM.xls
Introduction to MIS24 Human Resources
Introduction to MIS25 Finance Example: Project NPV Rate = 7% Can you look at these cost and revenue flows and tell if the project should be accepted? File: C08-14 Finance NPV.xlsC08-14 Finance NPV.xls
Introduction to MIS26 Accounting Balance Sheet for 2003 Cash33,562 Accounts Payable32,872 Receivables87,341 Notes Payable54,327 Inventories15,983 Accruals11,764 Total Current Assets136,886 Total Current Liabilities98,963 Bonds14,982 Common Stock57,864 Net Fixed Assets45,673 Ret. Earnings10,750 Total Assets182,559 Liabs. + Equity182,559 File: C08-15 Accounting.xlsC08-15 Accounting.xls
Introduction to MIS27 Accounting Income Statement for 2003 Sales$97,655 tax rate 40% Operating Costs76,530 dividends 60% Earnings before interest & tax21,125 shares out Interest4,053 Earnings before tax17,072 taxes6,829 Net Income10,243 Dividends6,146 Add. to Retained Earnings4,097 Earnings per share$0.42
Introduction to MIS28 Accounting Analysis Results in a CIRCular calculation. Cash$36,918 Acts Receivable96,075 Inventories17,581 Net Fixed Assets45,673 Total Assets$196,248 Accts Payable$36,159 Notes Payabale54,327 Accruals12,940 Total Cur. Liabs.103,427 Bonds14,982 Common Stock57,864 Ret. Earnings14,915 Liabs + Equity191,188 Add. Funds Need5,060 Bond int. rate5% Added interest253 Balance Sheet projected 2004 Income Statement projected 2004 Sales$ 107,421 Operating Costs84,183 Earn. before int. & tax23,238 Interest4,306 Earn. before tax18,931 taxes 8,519 Net Income 10,412 Dividends 6,274 Add. to Ret. Earnings $ 4,165 Earnings per share$0.43 Tax rate45% Dividend rate60% Shares outstanding9763 Sales increase10% Operations cost increase10% Forecast sales and costs. Forecast cash, accts receivable, accts payable, accruals. Add gain in retained earnings. Compute funds needed and interest cost. Add new interest to income statement Total Cur. Assets150,576
Introduction to MIS29 Geographic Models File: C08-25 GIS.xlsC08-25 GIS.xls
Introduction to MIS30 Tampa Miami Fort Myers Jacksonville Tallahassee Gainesville Ocala Orlando Clewiston Perry 17,000 15,800 14,600 13,400 12, ,700 19,400 18,100 16,800 15,500- per capita income 2000 Hard Goods 2000 Soft Goods 1990 Hard Goods 1990 Soft Goods
Introduction to MIS31 Cases: Computer Hardware Industry
Introduction to MIS32 Cases: Dell Computer Gateway 2000, Inc. What is the companys current status? What is the Internet strategy? How does the company use information technology? What are the prospects for the industry?
Introduction to MIS34 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
Introduction to MIS35 Structural Equations Demand is a function of Price Income Prices of related products Q D = b0 + b1 Price + b2 Income + b3 Substitute Q D = Price Income Substitute Model Estimate Data Forecast33318 = (155) (20000) (160) Need to know (estimate) future price, income, and substitute price.
Introduction to MIS36 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.
Introduction to MIS37 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
Introduction to MIS38 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.
Introduction to MIS39 Smoothing with Trends Apply exponential smoothing and choose smoothing factor ( ). Apply exponential smoothing a second time to the smoothed data.
Introduction to MIS40 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  = 33,141(value at time 20, after second smoothing) Y 21 = (2.25)32,064 - (1.25)33,141 = 30,718
Introduction to MIS41 Trendline Linear captures the trend only. Moving average captures all elements, but lags the actual pattern.
Introduction to MIS42 Regression Analysis =$F$20+$F$21*B6 TimeSalesForecast Tools + Data Analysis + Regression Dependent = Sales Independent = Time
Introduction to MIS43 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, * (21) = 34,456 Result is the prediction on the trend, with no random factors and no cycles.