EssentialsofManagementInformationSystemsFourteenthEditionChapter11ImprovingDecisionMakingandManagingArtificialIntelligenceCopyright?2021,2019,2017PearsonEducation,Inc.

AllRightsReservedLearningObjectives11.1

Whatarethedifferenttypesofdecisions,andhowdoesthedecision-makingprocesswork?11.2

Howdobusinessintelligenceandbusinessanalyticssupportdecisionmaking?11.3

Whatisartificialintelligence(AI)?Howdoesitdifferfromhumanintelligence?11.4

WhatarethemajortypesofAItechniquesandhowdotheybenefitorganizations?11.5

HowwillMIShelpmycareer?VideoCasesCase1:HowIBM’sWatsonBecameaJeopardyChampionCase2:BusinessIntelligenceHelpstheCincinnatiZooWorkSmarterInstructionalVideo1:IBMWatsonDemoOncologyDiagnosisandTreatmentMachineLearningHelpsAkershusUniversityHospitalMakeBetterTreatmentDecisionsProblemUnstructureddataVerylargevolumeofdataOpportunitiesfromnewtechnologySolutionsIBMWatsonExplorerMachinelearningNaturallanguageprocessingIllustrateshowinformationsystemsimprovedecisionmakingBusinessValueofImprovedDecisionMakingPossibletomeasurevalueofimproveddecisionmakingDecisionsmadeatalllevelsofthefirmSomearecommon,routine,andnumerousAlthoughvalueofimprovinganysingledecisionmaybesmall,improvinghundredsofthousandsof“small”decisionsaddsuptolargeannualvalueforthebusinessTable11.1BusinessValueofEnhancedDecisionMakingExampleDecisionValueDecisionMaker#ofAnnualDecisionsEstimatedValuetoFirmAnnualAllocatesupporttomostvaluablecustomersAccountsmanager12$100,000$1,200,000PredictcallcenterdailydemandCallCentermanagement4150,000600,000DecidepartsinventoryleveldailyInventorymanager3655,0001,825,000IdentifycompetitivebidsfrommajorsuppliersSeniormanagement12,000,0002,000,000ScheduleproductiontofillordersManufacturingmanager15010,0001,500,000TypesofDecisionsUnstructuredDecisionmakermustprovidejudgmenttosolveproblemNovel,important,nonroutineNowell-understoodoragreed-uponprocedureformakingthemStructuredRepetitiveandroutineInvolvedefiniteprocedureforhandlingthemsodonothavetobetreatedasnewSemi-structuredOnlypartofproblemhasclear-cutanswerprovidedbyacceptedprocedureFigure11.1InformationRequirementsofKeyDecision-MakingGroupsinaFirmTheDecision-MakingProcess1.IntelligenceDiscovering,identifying,andunderstandingtheproblemsoccurringintheorganization2.DesignIdentifyingandexploringvarioussolutions3.ChoiceChoosingamongsolutionalternatives4.ImplementationMakingchosenalternativeworkandmonitoringhowwellsolutionisworkingFigure11.2StagesinDecisionMakingHigh-VelocityAutomatedDecisionMakingHumanseliminatedDecision-makingprocesscapturedbycomputeralgorithmsPredefinedrangeofacceptablesolutionsDecisionsmadefasterthanmanagerscanmonitorandcontrolE.g.,TradingprogramsatelectronicstockexchangesQualityofDecisionsandDecisionMakingAccuracyComprehensivenessFairnessSpeed(efficiency)CoherenceDueprocessWhatIsBusinessIntelligence?InfrastructureformanagingdatafrombusinessenvironmentWarehousingIntegratingReportingAnalyzingHadoop,OLAP,analyticsProductsdefinedbytechnologyvendorsandconsultingfirmsTheBusinessIntelligenceEnvironmentSixelementsintheBIenvironment1.

Datafrombusinessenvironment2.Businessintelligenceinfrastructure3.Businessanalyticstoolset4.Managerialusersandmethods5.DeliveryplatformMSS,

DSS,

ESS6.

UserinterfaceFigure11.3BusinessIntelligenceandAnalyticsforDecisionSupportBusinessIntelligenceandAnalyticsCapabilitiesProductionreportsParameterizedreportsDashboards/scorecardsAd-hocquery/search/reportcreationDrill-downForecasts,scenarios,modelsLinearforecasting,what-ifscenarioanalysis,dataanalysisTable11.3ExamplesofPredefinedBusinessIntelligenceProductionReportsBusinessFunctionalAreaProductionReportsSalesSalesforecasts,salesteamperformance,crossselling,salescycletimesService/CallCenterCustomersatisfaction,servicecost,resolutionrates,churnratesMarketingCampaigneffectiveness,loyaltyandattrition,marketbasketanalysisProcurementandSupportDirectandindirectspending,off-contractpurchases,supplierperformanceSupplyChainBacklog,ful?llmentstatus,ordercycletime,billofmaterialsanalysisFinancialsGeneralledger,accountsreceivableandpayable,cash?ow,pro?tabilityHumanResourcesEmployeeproductivity,compensation,workforcedemographics,retentionInteractiveSession–Technology:SiemensMakesBusinessProcessesMoreVisibleClassdiscussionIdentifytheprobleminthiscasestudy.Whatpeople,organization,andtechnologyfactorscontributedtotheproblem?Describethecapabilitiesofprocessminingsoftware.Wasthisaneffectivesolution?Explainyouranswer.HowdidprocessminingchangedecisionmakingatSiemens?Whatpeople,organization,andtechnologyissuesneedtobeaddressedwhenimplementingprocessminingsystems?PredictiveAnalyticsUsesstatisticalanalytics,datamining,historicaldata;assumptionsoffutureconditionsExtractsinformationfromdatatopredictfuturetrendsandbehaviorpatternsResponsestodirectmarketingcampaignsBestpotentialcustomersforcreditcardsAt-riskcustomersCustomerresponsetopricechangesandnewservicesAccuraciesrangefrom65to90percentBigDataAnalyticsPredictiveanalyticscanusethebigdatageneratedfromsocialmedia,consumertransactions,sensorandmachineoutput,etc.CombiningwithcustomerdataBigdataanalyticsdrivingmovetoward“smartcities”UtilitymanagementTransportationoperationHealthcaredeliveryPublicsafetyOperationalIntelligenceandAnalyticsOperationalintelligenceDay-to-daymonitoringofbusinessdecisionsandactivityReal-timemonitoringSchneiderNationaltruckloadlogisticsservicesproviderDatadevelopedfromsensorsintrucks,trains,industrialsystemsTheInternetofThings(IoT)providinghugestreamsofdatafromconnectedsensorsanddevicesLocationAnalyticsandGISLocationanalyticsBigdataanalyticsthatuseslocationdatafrommobilephones,sensors,andmapsE.g.HelpingautilitycompanyviewcustomercostsasrelatedtolocationGIS–GeographicinformationsystemsHelpdecisionmakersvisualizeproblemswithmappingTielocationdataaboutresourcestomapFigure11.4BusinessIntelligenceUsersInteractiveSession–Organizations:PredictiveMaintenanceintheOilandGasIndustryClassdiscussionWhyispredictivemaintenancesoimportantintheoilandgasindustry?Whatproblemsdoesitsolve?WhatistheroleoftheInternetofThings(IoT)andBigDataanalyticsinpredictivemaintenance?HowdidBPandRoyalDutchShell’spredictivemaintenanceapplicationschangebusinessoperationsanddecisionmaking?Giveanexampleofhowpredictivemaintenancesystemscouldbeusedinanotherindustry.SupportforSemi-StructuredDecisionsDecision-supportsystems(DSS)BIdeliveryplatformfor“super-users”whowanttocreateownreports,usemoresophisticatedanalyticsandmodelsWhat-ifanalysisSensitivityanalysisBackwardsensitivityanalysisPivottables:SpreadsheetfunctionformultidimensionalanalysisIntensivemodelingtechniquesFigure11.5SensitivityAnalysisFigure11.6APivotTableThatExaminesCustomerRegionalDistributionandAdvertisingSourceDecisionSupportforSeniorManagement(1of2)ExecutivesupportsystemsBalancedscorecardmethodMeasuresfourdimensionsoffirmperformanceFinancialBusinessprocessCustomerLearningandgrowthKeyperformanceindicators(KPI)usedtomeasureeachdimensionFigure11.7TheBalancedScorecardFrameworkDecisionSupportforSeniorManagement(2of2)Businessperformancemanagement(BPM)Managementmethodologybasedonfirm’sstrategiesTranslatesstrategiesintooperationaltargetsUsessetofKPIstomeasureprogresstowardtargetsESScombineinternaldatawithexternalFinancialdata,news,etc.Drill-downcapabilitiesArtificialIntelligenceTechniquesArtificialintelligence:Grandvisionvs.narrowdefinitionEvolutionofAIBigdatadatabasesReductioninthepriceofprocessorsExpansionincapacityofprocessorsRefinementandexplosionofalgorithmsLargeinvestmentsinITandAIProgressinimagerecognitionandnaturallanguageE.g.:Siri,Alexa,facialrecognitionWhatAretheMajorTypesofAITechniquesandHowDoTheyBenefitOrganizations?(1of5)ExpertsystemsCapturehumanexpertiseinalimiteddomainofknowledgeExpressexpertiseasasetofrulesinasoftwaresystemKnowledgebaseInferenceengineFigure11.8RulesinanExpertSystemWhatAretheMajorTypesofAITechniquesandHowDoTheyBenefitOrganizations?(2of5)MachinelearningComputersimprovingperformancebyusingalgorithmstolearnpatternsfromdataandexamplesNeuralnetworksFindpatternsandrelationshipsinverylargeamountsofdataSensoringandprocessingnodes“DeepLearning”neuralnetworksFigure11.9HowaNeuralNetworkWorksFigure11.10ADeepLearningNetworkWhatAretheMajorTypesofAITechniquesandHowDoTheyBenefitOrganizations?(3of5)GeneticalgorithmsExaminelargenumberofsolutionsforaproblemBasedonmachinelearningtechniquesinspiredbyevolutionarybiologyFigure11.11TheComponentsofaGeneticAlgorithmWhatAretheMajorTypesofAITechniquesandHowDoTheyBenefitOrganizations?(4of5)NaturallanguageprocessingSoftwarethatcanprocessvoiceortextcommandsusingnaturalhumanlanguageComputervisionsystemsEmulatehumanvisualsystemtoviewandextractinformationfromreal-worldimagesRobotics

DesignanduseofmovablemachinesthatcansubstituteforhumansWhatAre