Introduction to MIS Chapter 1

Introduction to MISChapter 9Business Decisions

Jerry PostTechnology Toolbox: Forecasting a TrendTechnology Toolbox: PivotTableCases: Financial Services1Technology Toolbox: Forecasting a Trend

C10TrendForecast.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 monthsTechnology Toolbox: PivotTableExcel: Data/PivotTable, External Data sourceFind Rolling Thunder, choose qryPivotAllDrag columns to match example. Play.C10PivotTable.xls

Cases: Financial ServicesSample ModelAverage totalcostMarginal cost$QuantitypriceQ*Determining Production Levelsin Perfect CompetitionEconomic, financial, and accounting models are useful for examining and comparing businesses.5Decision LevelsBusiness OperationsTacticalManagementStrategicMgt. EIS ES DSS Transaction ProcessingProcess ControlModels6Choose a StockCompany 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?7Left to themselves, people rarely make good decisions.We try to make decisions based on simple data and observed correlations.Examples of human biases are on the next screen.Statistics and other tools can reduce the effects of some of these biases.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 Models

Buick Century

Oldsmobile Cutlass Ciera

Chevrolet Celebrity

Pontiac 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, 199110Humans make many mistakes when evaluating data and trying to make decisions.This is a partial list from work done by Barabba and Zaltmanwhich was largely triggered by the GM design decisions.These traits emphasize the importance of examining data using formal models and methodologies.Model 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" Scenarios12Prediction0510152025Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Time/quartersOutputMoving AverageTrend/ForecastEconomic/regressionForecastFile: C10Fig05.xls13Simulation051015202512345678910Input LevelsOutputGoal or outputvariablesResults from alteringinternal rulesFile: C08Fig10.xls14Object-Oriented Simulation ModelsCustomerOrder EntryCustom ManufacturingProductionInventory & PurchasingShipping

Purchase OrderPurchase OrderRouting & SchedulingInvoiceParts ListShipping Schedule15Several OO simulation tools exist to help model operations.Basic mathematical models are defined at each key step and the process is simulated to show potential outcomes. Parameters can be tweaked in the models to show the effect of changes. For example, hiring an additional order-entry clerk (or building a Web site) might process orders more efficiently, which might lead to a backlog in inventory and purchasing. The point is that managers can experiment with changes in the simulated model that are difficult or impossible to do in a real company.Data WarehouseOLTP Database3NF tablesOperationsdataPredefinedreportsData warehouseStar configurationDaily datatransferInteractivedata analysisFlat files

Multidimensional OLAP CubeTimeSale MonthCustomer LocationCategoryCAMINYTXJanFebMarAprMayRaceRoadMTBFull SHybrid88075093568499310111257985874125643757968387374514201258118410981578Think of a cube browser (or PivotTable in Microsoft terms) as a way to explore the data. It quickly computes and displays subtotals, filters data, and lets you drill down to see more detail. It often pulls data from a data warehouse using queries, but managers can explore the data interactively without having to write new queries. Think of the tool as computing multiple levels of GROUP BY statements in SQL queries.17Microsoft Pivot Table

Microsoft Pivot Chart

DSS: 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.xls20Sample 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 DataInternalPurchaseGovernmentSalesWarranty 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-1.281380.27724-4.6219GDP0.0811720.0103457.8467

With appropriate data, the system could also statistically evaluate for non-discriminationInteractive: HR RaisesFile: C09 HRM Raises.xlsxFinance Example: Project NPV

Rate = 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 2003Cash33,562 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: C09 Accounting.xlsx27AccountingIncome Statement for 2003Sales$97,655 tax rate 40%Operating Costs76,530 dividends 60%Earnings before interest & tax21,125 shares out. 9763 Interest4,053 Earnings before tax17,072 taxes6,829 Net Income10,243 Dividends6,146 Add. to Retained Earnings4,097 Earnings per share$0.42 28Accounting AnalysisResults in a CIRCular calculation.Cash$36,918Acts Receivable96,075Inventories17,581

Net Fixed Assets45,673Total Assets$196,248 Accts Payable$36,159Notes Payabale54,327Accruals12,940Total Cur. Liabs.103,427Bonds14,982Common Stock57,864Ret. Earnings14,915Liabs + Equity191,188Add. Funds Need5,060Bond int. rate5%Added interest253Balance Sheet projected 2004Income Statement projected 2004Sales$ 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 rate45%Dividend rate60%Shares outstanding9763Sales 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.12345124235Total Cur. Assets150,57629Geographic ModelsFile: C09 GIS.xlsxCity2000 Pop2009 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 Myers59,49164,67420,25630,077535.2652.9928.21010.3Gainesville101,724116,61619,42824,270365.2281.7550.5459.4Jacksonville734,961813,51819,27524,828990.2849.11321.71109.3Miami300,691433,13618,81223,169721.7833.4967.11280.6Ocala55,87855,56815,13020.748359.0321.7486.2407.3Orlando217,889235,86020.72923,936425.7509.2691.5803.5Perry8,0456,66914,14419,295300.1267.2452.9291.0Tallahassee155,218172,57420,18527,845595.4489.7843.8611.7Tampa335,458343,89019,06225,851767.4851.0953.41009.130TampaMiamiFort MyersJacksonvilleTallahasseeGainesvilleOcalaOrlandoClewistonPerry20,70019,40018,10016,80015,500-2000200730,10027,20024,20021,30021,300-per capita income

2010HardGoods2010SoftGoods2000HardGoods2000SoftGoods31GIS: 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 specializedleave to discipline studiesCommon Data Mining GoalSalesLocationDependent VariableFactIndependent VariablesDimensions/AttributesAgeIncomeTimeMonthCategoryDirect effectsIndirect effectsData Mining: Clusters

Data Mining Tools: Spotfire

http://www.spotfire.comMarket Basket Analysis

What 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: Dogshttp://www.exsys.com/demomain.html

Expert SystemKnowledge Base

Symbolic & Numeric KnowledgeIf income > 20,000or expenses < 3000and good credit historyor . . .Then 10% chance of defaultRules

Expert decisionsmade bynon-expertsExpert40ES Example: bank loanWelcome to the Loan Evaluation System.What is the purpose of the loan? carHow much money will be loaned? 15,000For how many years? 5

The current interest rate is 7%.The payment will be $297.02 per month.

What is the annual income? 24,000

What is the total monthly payments of other loans? Why?

Because the payment is more than 10% of the monthly income.

What is the total monthly payments of other loans? 50.00

The loan should be approved, there is only a 2% chance of default.Forward Chaining41Payments< 10%monthly income?Other loanstotal < 30%monthly income?CreditHistoryJobStability

Approvethe loanDenythe loanNoYesGoodYesNoBadSo-soGoodPoorDecision Tree (bank loan) 42Customer Data

Name ____Address ____Years at address__Co-applicant___

Job History

Employer, Salary, Date Hired......Job History

Employer, Salary, Date Hired......Loan Details

Purpose BoatLoan Amount _____Time _____Data for Boat Loans

Length:Engine:Cost New:Cost Used:Recommendation

Lend $$$$at ___ interest ratefor ___ months,with ___ initial costs.RulesFrame-Based ES43Early ES ExamplesUnited AirlinesGADS: Gate AssignmentAmerican ExpressAuthorizer's AssistantStanfordMycin: MedicineDECOrder Analysis + moreOil exploration Geological survey analysisIRS Audit selectionAuto/Machine repair(GM:Charley) Diagnostic44ES Problem SuitabilityCharacteristicsNarrow, well-defined domainSolutions require an expertComplex logical processingHandle missing, ill-structured dataNeed a cooperative expertRepeatable decisionTypes of problemsDiagnosticSpeedConsistencyTraining45ES screensseen by userRulesanddecisiontreesenteredby designer

ExpertForwardandbackwardchainingby ES shell

KnowledgeengineerKnowledgedatabase(for (k 0 (+ 1 k) ) exit when ( ?> 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 shellProgrammer

Custom program in LISPES DevelopmentES ShellsGuruExsysCustom ProgrammingLISPPROLOG46Some Expert System ShellsCLIPSOriginally developed at NASAWritten in CAvailable free or at low costhttp://clipsrules.sourceforge.net/JessWritten in JavaGood for Web applicationsAvailable free or at low costhttp://herzberg.ca.sandia.gov/jess/ExSysCommercial 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?48AI Research AreasComputer ScienceParallel ProcessingSymbolic ProcessingNeural NetworksRobotics ApplicationsVisual PerceptionTactilityDexterityLocomotion & NavigationNatural LanguageSpeech RecognitionLanguage TranslationLanguage ComprehensionCognitive ScienceExpert SystemsLearning SystemsKnowledge-Based Systems49Output CellsSensory Input CellsHidden LayerSome of the connections3-274Input weightsIncompletepattern/missing inputs.Neural Network: Pattern recognition650Machine Vision Examplehttp://www.terramax.com/Several 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 users 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.52Natural Language: IBM Watson

http://www.youtube.com/watch?v=12rNbGf2Wwo 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) Definitions54DSS and ES

55DSS, ES, and AI: Bank ExampleDecision Support SystemExpert SystemArtificial IntelligenceNameLoan#LateAmountBrown25,000 51,250Jones62,000 1 135Smith83,000 32,435...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 Officer

Vacation ResortsSoftware agentResort DatabasesLocate &book trip.Software AgentsIndependentNetworks/CommunicationUsesSearchNegotiateMonitor

57AI 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?58Cloud 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 EC2Quick 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?Quick 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? Chart4-200000015000025000035000046000056000066

Costs-ARevenue-AYearProject A NPV=$18,475

Sheet1interest rate0.07Project ANPV$18,475.41YearCostsRevenue0-200000150000250000350000460000560000Project BNPV$6,064.06YearCostsRevenue0-1000001-1000350002-2000450003-20000550004-200065000Project CNPV($529.95)YearCostsRevenue0-85000110000215000315000420000525000625000

Sheet2interest rate0.07NPV$18,475.41NPV$6,064.06NPV($3,814.04)YearCosts-ARevenue-ACosts-BRevenue-BCosts-CRevenue-C0-200000-100000-85000150000-100035000-100015000250000-200045000-100020000350000-2000055000-100025000460000-200065000-100025000560000-100035000635000

Sheet2

Costs-ARevenue-AYearProject A NPV=$18,475

Sheet3

Costs-BRevenue-BYearProject B NPV=$6,064

Costs-CRevenue-CYearProject C NPV = -$3,814

Chart5-1000000-100035000-200045000-2000055000-2000650005566

Costs-BRevenue-BYearProject B NPV=$6,064

Sheet1interest rate0.07Project ANPV$18,475.41YearCostsRevenue0-200000150000250000350000460000560000Project BNPV$6,064.06YearCostsRevenue0-1000001-1000350002-2000450003-20000550004-200065000Project CNPV($529.95)YearCostsRevenue0-85000110000215000315000420000525000625000

Sheet2interest rate0.07NPV$18,475.41NPV$6,064.06NPV($3,814.04)YearCosts-ARevenue-ACosts-BRevenue-BCosts-CRevenue-C0-200000-100000-85000150000-100035000-100015000250000-200045000-100020000350000-2000055000-100025000460000-200065000-100025000560000-100035000635000

Sheet2

Costs-ARevenue-AYearProject A NPV=$18,475

Sheet3

Costs-BRevenue-BYearProject B NPV=$6,064

Costs-CRevenue-CYearProject C NPV = -$3,814

Chart6-850000-100015000-100020000-100025000-100025000-100035000635000

Costs-CRevenue-CYearProject C NPV = -$3,814

Sheet1interest rate0.07Project ANPV$18,475.41YearCostsRevenue0-200000150000250000350000460000560000Project BNPV$6,064.06YearCostsRevenue0-1000001-1000350002-2000450003-20000550004-200065000Project CNPV($529.95)YearCostsRevenue0-85000110000215000315000420000525000625000

Sheet2interest rate0.07NPV$18,475.41NPV$6,064.06NPV($3,814.04)YearCosts-ARevenue-ACosts-BRevenue-BCosts-CRevenue-C0-200000-100000-85000150000-100035000-100015000250000-200045000-100020000350000-2000055000-100025000460000-200065000-100025000560000-100035000635000

Sheet2

Costs-ARevenue-AYearProject A NPV=$18,475

Sheet3

Costs-BRevenue-BYearProject B NPV=$6,064

Costs-CRevenue-CYearProject C NPV = -$3,814

DSSESgoalhelp user make decisionprovide expert advicemethoddata - model - presentationasks questions, applies rules, explainstype of

problemsgeneral, limited by user modelsnarrow domain