Bài giảng Introduction to MIS - Chapter 9: Business Decisions

Outline How do businesses make decisions? How do you make a good decision? Why do people make bad decisions? How do you find and retrieve data to analyze it? How can you quickly examine data and view subtotals without writing hundreds of queries? How does a decision support system help you analyze data? How do you visualize data that depends on location? Is it possible to automate the analysis of data? Can information technology be more intelligent? Can it analyze data and evaluate rules? How do you create an expert system? Can machines be made even smarter? What technologies can be used to help managers? What would it take to convince you that a machine is intelligent? What are the differences between DSS, ES, and AI systems? How can more intelligent systems benefit e-business? How can cloud computing be used to analyze data?

pptx64 trang | Chia sẻ: baothanh01 | Lượt xem: 860 | Lượt tải: 0download
Bạn đang xem trước 20 trang tài liệu Bài giảng Introduction to MIS - Chapter 9: Business Decisions, để xem tài liệu hoàn chỉnh bạn click vào nút DOWNLOAD ở trên
Introduction to MISChapter 9Business DecisionsJerry PostTechnology Toolbox: Forecasting a TrendTechnology Toolbox: PivotTableCases: Financial ServicesOutlineHow do businesses make decisions?How do you make a good decision? Why do people make bad decisions?How do you find and retrieve data to analyze it?How can you quickly examine data and view subtotals without writing hundreds of queries?How does a decision support system help you analyze data?How do you visualize data that depends on location?Is it possible to automate the analysis of data?Can information technology be more intelligent? Can it analyze data and evaluate rules?How do you create an expert system?Can machines be made even smarter? What technologies can be used to help managers?What would it take to convince you that a machine is intelligent?What are the differences between DSS, ES, and AI systems?How can more intelligent systems benefit e-business?How can cloud computing be used to analyze data?Making DecisionsDataSales and OperationsModelsAnalysis and OutputDecisionsDecision ChallengesBy guessing, people make bad decisions.You need to develop a processObtain dataBuild a modelAnalyze the dataWhich means you need toolsSome tools require background and experienceSome can be automated to various pointsBeware of decisions after-the-fact: Someone can have “amazing” results that are random.If you look at a sample of 1,000 people and one does substantially better than the others is it random?Stock-picking competitions/resultsSample ModelAverage totalcostMarginal cost$QuantitypriceQ*Determining Production Levelsin Perfect CompetitionEconomic, financial, and accounting models are useful for examining and comparing businesses.Decision 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?Does More Data Help?Thousands of stocks, funds, and derivatives.How do you find a profitable investment?Working for a manufacturing company (e.g., cars)What features do you place in your next design?Data exists:SurveysSalesCompetitor salesFocus groupsGM (Fortune Magazine cover: August 22, 1983)Olds Cutlass CieraPontiac J-2000Buick CenturyChevrolet CelebrityGeneral Motors 1984 ModelsBuick CenturyOldsmobile Cutlass CieraChevrolet CelebrityPontiac 6000All photos from WikipediaSee Fortune August 22, 1983 cover for photos new.Why is it bad that all four divisions produced the same car?How is it possible that designers would produce the same car?A-body carsWSJ 2008 VersionHuman 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 biasBarabba, Vincent and Gerald Zaltman, Hearing the Voice of the Market, Harvard Business Press: Cambridge, MA, 1991Model BuildingUnderstand the ProcessModels force us to define objects and specify relationships. Modeling is a first step in improving the business process.OptimizationModels are used to search for the best solutions: Minimizing costs, improving efficiency, increasing profits, and so on.PredictionModel parameters can be estimated from prior data. Sample data is used to forecast future changes based on the model.SimulationModels are used to examine what might happen if we make changes to the process or to examine relationships in more detail.Optimization123456789101350510152025OutputInput LevelsMaximumModel: definedby the data pointsor equationControl variablesGoal or outputvariablesFile: C10Optimum.xlsWhy Build Models?Understanding the ProcessOptimizationPredictionSimulation or "What If" ScenariosPrediction0510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Time/quartersOutputMoving AverageTrend/ForecastEconomic/regressionForecastFile: C10Fig05.xlsSimulation051015202512345678910Input LevelsOutputGoal or outputvariablesResults from alteringinternal rulesFile: C08Fig10.xlsObject-Oriented Simulation ModelsCustomerOrder EntryCustom ManufacturingProductionInventory & PurchasingShippingPurchase OrderPurchase OrderRouting & SchedulingInvoiceParts ListShipping ScheduleData WarehouseOLTP Database3NF tablesOperationsdataPredefinedreportsData warehouseStar configurationDaily datatransferInteractivedata analysisFlat filesMultidimensional OLAP CubeTimeSale MonthCustomer LocationCategoryCAMINYTXJanFebMarAprMayRaceRoadMTBFull SHybrid88075093568499310111257985874125643757968387374514201258118410981578Microsoft Pivot TableMicrosoft Pivot ChartDSS: 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: C10DSS.xlsSample DSSThe following slides illustrate some simple DSS models that managers should be able to create (with sufficient background in the discipline courses).Regression or time series forecast (marketing)Employee evaluation (HRM)Present value determination (finance)Basic accounting spreadsheetsMarketing Research Data Internal Purchase GovernmentSalesWarranty cardsCustomer service linesCouponsSurveysFocus groupsScanner dataCompetitive market analysisMailing and phone listsSubscriber listsRating services (e.g., Arbitron)Shipping, especially foreignWeb site tracking, social networksLocationCensusIncomeDemographicsRegional dataLegal registrationDrivers licenseMarriageHousing/constructionMarketing Sales ForecastforecastNote the fourth quarter sales jump. The forecast should pick up this cycle.File: C09 Marketing Forecast.xlsxRegression ForecastingSales = b0 + b1 Time + b2 GDPModel:Data:Quarterly sales and GDP for 16 years.Analysis:Estimate model coefficients with regression.Forecast GDP for each quarter.Output:Compute Sales prediction.Graph forecast.CoefficientsStandard ErrorT StatIntercept-68.449913.4699-5.0817Time-1.281380.27724-4.6219GDP0.0811720.0103457.8467With appropriate data, the system could also statistically evaluate for non-discriminationInteractive: HR RaisesFile: C09 HRM Raises.xlsxFinance Example: Project NPVRate = 7%Can you look at these cost and revenue flows and tell if the project should be accepted? File: C09 Finance NPV.xlsxAccountingBalance 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: C09 Accounting.xlsxAccountingIncome 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: C09 GIS.xlsx  City  2000 Pop  2009 Pop2000 per-capitaincome2007 per-capitaincome2000 hardgood sales(000)2000 softgood sales(000)2009 hardgood sales(000)2009 softgood sales(000)Clewiston8,5497,10715,46615,487452.0562.5367.6525.4Fort Myers59,49164,67420,25630,077535.2652.9928.21010.3Gainesville101,724116,61619,42824,270365.2281.7550.5459.4Jacksonville734,961813,51819,27524,828990.2849.11321.71109.3Miami300,691433,13618,81223,169721.7833.4967.11280.6Ocala55,87855,56815,13020.748359.0321.7486.2407.3Orlando217,889235,86020.72923,936425.7509.2691.5803.5Perry8,0456,66914,14419,295300.1267.2452.9291.0Tallahassee155,218172,57420,18527,845595.4489.7843.8611.7Tampa335,458343,89019,06225,851767.4851.0953.41009.1TampaMiamiFort MyersJacksonvilleTallahasseeGainesvilleOcalaOrlandoClewistonPerry20,70019,40018,10016,80015,500-2000200730,10027,20024,20021,30021,300-per capita income2010HardGoods2010SoftGoods2000HardGoods2000SoftGoodsGIS: Shading (RT Sales in 2008)Data MiningAutomatic analysis of dataStatisticsCorrelationRegression (multiple correlation)ClusteringClassificationNonlinear relationshipsMore automated methodsMarket basket analysisPatterns: neural networksNumerical dataCommonly search for how independent variables (attributes or dimensions) influence the dependent (fact) variable.Non-numerical dataEvent and sequence studiesLanguage analysisHighly specialized—leave to discipline studiesCommon Data Mining GoalSalesLocationDependent VariableFactIndependent VariablesDimensions/AttributesAgeIncomeTimeMonthCategoryDirect effectsIndirect effectsData Mining: ClustersData Mining Tools: Spotfire Basket AnalysisWhat items do customers buy together?Data Mining: Market Basket AnalysisGoal: Measure association between two itemsWhat items do customers buy together?What Web pages or sites are visited in pairs?Classic examplesConvenience store found that on weekends, people often buy both beer and diapers.Amazon.com: shows related purchasesInterpretation and UseDecide if you want to put those items together to increase cross-sellingOr, put items at opposite ends of the aisle and make people walk past the high-impulse itemsExpert System Example: Exsys: Dogs SystemKnowledge BaseSymbolic & Numeric KnowledgeIf income > 20,000or expenses k cluster-size) do (for (j 0 (+ 1 j )) exit when (= j k) do (connect unit cluster k output o -A to unit cluster j input i - A )) . . . )Maintained by expert system shellProgrammerCustom program in LISPES DevelopmentES ShellsGuruExsysCustom ProgrammingLISPPROLOGSome Expert System ShellsCLIPSOriginally developed at NASAWritten in CAvailable free or at low cost in JavaGood for Web applicationsAvailable free or at low cost system with many featureswww.exsys.comLimitations of ESFragile systemsSmall environmental. changes can force revision. of all of the rules.MistakesWho is responsible?Expert?Multiple experts?Knowledge engineer?Company that uses it?Vague rulesRules can be hard to define.Conflicting expertsWith multiple opinions, who is right?Can diverse methods be combined?Unforeseen eventsEvents outside of domain can lead to nonsense decisions.Human experts adapt.Will human novice recognize a nonsense result?AI Research AreasComputer ScienceParallel ProcessingSymbolic ProcessingNeural NetworksRobotics ApplicationsVisual PerceptionTactilityDexterityLocomotion & NavigationNatural LanguageSpeech RecognitionLanguage TranslationLanguage ComprehensionCognitive ScienceExpert SystemsLearning SystemsKnowledge-Based SystemsOutput CellsSensory Input CellsHidden LayerSome of the connections3-274Input weightsIncompletepattern/missing inputs.Neural Network: Pattern recognition6Machine Vision Example teams passed the second DARPA challenge to create autonomous vehicles. Although Stanford won the challenge, Team TerraMax had the most impressive entry.Language RecognitionLook at the user’s voice command:Copy the red, file the blue, delete the yellow mark.Now, change the commas slightly.Copy the red file, the blue delete, the yellow mark.I saw the Grand Canyon flying to New York.EmergencyVehiclesNoParkingAny TimeThe panda enters a bar, eats, shoots, and leaves.Natural Language: IBM Watson Practice match 4 min.February 14-16, 2011: Watson beat two top humans in Jeopardy.Natural language parsing and statistical searching.Multiple blade servers and 15 terabytes of RAM!Subjective Definitionstemperaturereference pointe.g., averagetemperaturecoldhotMoving farther from the reference pointincreases the chance that the temperature isconsidered to be different (cold or hot).Subjective (fuzzy) DefinitionsDSS and ESDSS, ES, and AI: Bank ExampleDecision Support SystemExpert SystemArtificial IntelligenceName Loan #Late AmountBrown 25,000 5 1,250Jones 62,000 1 135Smith 83,000 3 2,435...DataIncomeExisting loansCredit reportModelLend in all but worst casesMonitor for late and missing payments.OutputES RulesWhat is the monthly income?3,000What are the total monthly payments on other loans? 450How long have they had the current job? 5 years. . .Should grant the loan since there is only a 5% chance of default.Determine Rulesloan 1 data: paidloan 2 data: 5 lateloan 3 data: lostloan 4 data: 1 lateData/Training CasesNeural Network WeightsEvaluate new data,make recommendation.Loan OfficerVacation ResortsSoftware agentResort DatabasesLocate &book trip.Software AgentsIndependentNetworks/ CommunicationUsesSearchNegotiateMonitorAI QuestionsWhat is intelligence?Creativity?Learning?Memory?Ability to handle unexpected events?More?Can machines ever think like humans?How do humans think?Do we really want them to think like us?Cloud ComputingMany analytical problems are hugeRequiring large amounts of dataMassive amounts of processing time and multiple processorsNeed to lease computing timePossibly supercomputer time (science)Otherwise, cloud computing such as Amazon EC2Technology Toolbox: Forecasting a TrendC10TrendForecast.xlsRolling Thunder query for total sales by year and monthUse Format(OrderDate, “yyyy-mm”)In Excel: Data/Import/New Database QueryCreate a line chart, right-click and add trend lineIn the worksheet, add a forecast for six monthsQuick Quiz: Forecasting1. Why is a linear forecast usually safer than nonlinear?2. Why do you need to create a new column with month numbers for regression instead of using the formatted year-month column?3. What happens to the trend line r-squared value on the chart when you add the new forecast rows to the chart?Technology Toolbox: PivotTableExcel: Data/PivotTable, External Data sourceFind Rolling Thunder, choose qryPivotAllDrag columns to match example. Play.C10PivotTable.xlsQuick Quiz: PivotTable1. How is the cube browser better than writing queries?2. How would you display quarterly instead of monthly data?3. How many dimensions can you reasonably include in the cube? How would you handle additional dimensions? Cases: Financial Services
Tài liệu liên quan