Introduction to MIS - Chapter 8: Models and Decision Support

Introduction to MISChapter 8Models and Decision SupportModelsDataModelDecisionOutputStrategyOperationsTacticsCompanyOutlineBiases in DecisionsIntroduction to ModelsWhy Build Models?Decision Support Systems: Database, Model, OutputData WarehouseData Mining and Analytical ProcessingDigital Dashboard and EISDSS ExamplesGeographical Information SystemsCases: Computer Hardware IndustryAppendix: ForecastingDecision LevelsBusiness OperationsTacticalManagementStrategicMgt. EIS ES DSS Transaction ProcessingProcess ControlModelsChoose a StockCompany 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?Human BiasesAcquisition/InputData availabilitySelective perceptionFrequencyConcrete informationIllusory correlationProcessingInconsistencyConservatismNon-linear extrapolationHeuristics: Rules of thumbAnchoring and adjustmentRepresentativenessSample sizeJustifiabilityRegression biasBest guess strategiesComplexityEmotional stressSocial pressureRedundancyOutputQuestion formatScale effectsWishful thinkingIllusion of controlFeedbackLearning on irrelevanciesMisperception of chanceSuccess/failure attributionLogical fallacies in recallHindsight biasOptimization123456789101350510152025OutputInput LevelsMaximumModel: definedby the data pointsor equationControl variablesGoal or outputvariablesFile: C08Fig08.xlsWhy Build Models?Understanding the ProcessOptimizationPredictionSimulation or "What If" ScenariosDangers Prediction0510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Time/quartersOutputMoving AverageTrend/ForecastEconomic/regressionForecastFile: C08Fig09.xlsSimulation051015202512345678910Input LevelsOutputGoal or outputvariablesResults from alteringinternal rulesFile: C08Fig10.xlsObject-Oriented Simulation ModelsCustomerpurchaseorderOrder EntryCustom Manufacturingpurchaseorderrouting& schedulingProductionInventoryShippingPartsListShippingScheduleInvoiceDSS: Decision Support Systemssalesrevenueprofitprior154204.545.3235.72163217.853.2437.23161220.457.1732.78173268.361.9347.68143195.232.3841.25181294.783.1967.52Sales and Revenue 1994JanFebMarAprMayJun050100150200250300LegendSalesRevenueProfitPriorDatabaseModelOutputdata to analyzeresultsFile: C08Fig11.xlsData Mining: Spotfire WarehouseOLTP Database3NF tablesOperationsdataPredefinedreportsData warehouseStar configurationDaily datatransferInteractivedata analysisFlat filesMultidimensional OLAP CubeTimeSale DateCustomerLocationCategoryPet StoreItem SalesAmount = Quantity*Sale PriceMicrosoft SQL Server Cube BrowserMicrosoft Pivot TableDigital Dashboard marketExceptionsPlant or management variablesEquipment detailsProductsQuality controlPlant scheduleEIS: ExecutiveInformation SystemEasy access to dataGraphical interfaceNon-intrusiveDrill-down capabilitiesEIS Softwarefrom Lightshiphighlights ease-of-use GUI fordata look-up.Executive ISProductionDistributionSalesCentral ManagementExecutivesDataDataSalesProduction CostsDistribution CostsFixed CostsProduction CostsSouthNorthOverseasProduction: NorthItem# 1995 19941234 542.1 442.32938 631.3 153.57319 753.1 623.8Data for EISDataDataMarketing Research DataMarketing Sales ForecastforecastNote the fourth quarter sales jump. The forecast should pick up this cycle.File: C08-10 Marketing Forecast.xlsRegression ForecastingSales = 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.Human ResourcesFile: C08-19 HRM.xlsHuman ResourcesFinance Example: Project NPVRate = 7%Can you look at these cost and revenue flows and tell if the project should be accepted? File: C08-14 Finance NPV.xlsAccountingBalance 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 File: C08-15 Accounting.xlsAccountingIncome 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 Accounting AnalysisResults in a CIRCular calculation.Cash $36,918Acts Receivable 96,075Inventories 17,581Net Fixed Assets 45,673Total Assets $196,248 Accts Payable $36,159Notes Payabale 54,327Accruals 12,940Total Cur. Liabs. 103,427Bonds 14,982Common Stock 57,864Ret. Earnings 14,915Liabs + Equity 191,188Add. Funds Need 5,060Bond int. rate 5%Added interest 253Balance Sheet projected 2004Income Statement projected 2004Sales$ 107,421Operating Costs84,183Earn. before int. & tax23,238Interest4,306Earn. before tax18,931taxes 8,519Net Income 10,412Dividends 6,274Add. to Ret. Earnings $ 4,165Earnings per share$0.43Tax rate 45%Dividend rate 60%Shares outstanding 9763Sales increase 10%Operations cost increase 10%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.12345124235Total Cur. Assets 150,576Geographic ModelsFile: C08-25 GIS.xlsTampaMiamiFort MyersJacksonvilleTallahasseeGainesvilleOcalaOrlandoClewistonPerry17,00015,80014,60013,40012,200-1990200020,70019,40018,10016,80015,500-per capita income2000HardGoods2000SoftGoods1990HardGoods1990SoftGoodsCases: Computer Hardware IndustryCases: Dell Computer Gateway 2000, Inc.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?www.dell.comwww.gateway.comAppendix: Forecasting UsesMarketingFuture salesConsumer preferences/trendsSales strategiesFinanceInterest ratesCash flowsFinancial market conditionsHRMLabor costsAbsenteeismTurnoverStrategyRivals’ actionsTechnological changeMarket conditionsForecasting MethodsStructural ModelsDerive underlying modelsEstimate parametersEvaluate modelFocus on explanation and causeTime SeriesCollect data over timeIdentify trendsIdentify seasonal effectsForecast based on patternsQPSDD’Increase in incometimesalestrendStructural EquationsDemand is a function ofPriceIncomePrices of related productsQD = b0 + b1 Price + b2 Income + b3 SubstituteQD = 1114 - 0.1 Price + 1.2 Income - 1.0 SubstituteModelEstimateDataForecast33318 = 1114 - 0.1 (155) + 1.2 (20000) - 1.0 (160)Need to know (estimate) future price, income, and substitute price.Time Series ComponentstimesalesDecDecDecDec1. Trend2. Seasonal3. Cycle4. RandomTrendSeasonalA cycle is similar to the seasonal pattern,but covers a time period longer than a year.Exponential SmoothingSt = Yt + (1 - ) St-1S is the new data point is the smoothing factorUse Excel: Tools, Data Analysis Exponential SmoothingExponential SmoothingChoosing the smoothing factor ():It is usually between 0.01 and 0.20Test multiple values and compare errors:(actual - smooth) * (actual - smooth)Compute the sum. Choose the factor with the least total sum-of-squared error.SumSumSum(A2-D2)*(A2-D2)929,916848,686769,265Larger factors placemore importance onrecent data, which results in less smoothing.Smoothing with TrendsApply exponential smoothing and choose smoothing factor ().Apply exponential smoothing a second time to the smoothed data.Forecasting with Exponential SmoothingForecast for time T+T = 20 last of the raw data = 1 forecast one period ahead = 0.2 smoothing factorS20 = 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 = 30,718TrendlineLinear captures the trend only.Moving average captures all elements, but lags the actual pattern.Regression Analysis=$F$20+$F$21*B6Time Sales ForecastTools + Data Analysis + RegressionDependent = SalesIndependent = TimeEstimating TrendYt = 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) = 34,456Result is the prediction on the trend, with norandom factors and no cycles.