DeployingArtificialIntelligencetechniquesfor SupportingDecisionsintheBusinessProcess Area

Abstract

Business Processes(BP)constitutetheheartofInformationSystems(IS)ofmodern organizations. Thus,theyareintensivelyutilized,bothinthemanagementofvarious companies’ resourcesandindecision-makingandstrategicalignmentactivities.The abstract specifications (or models) expressing thebusinesslogicbehindtheBPsarees- sentialconceptualtoolsusefulforvarioustasks,varyingformmodeling,analysis,moni- toring andmaintenance.However,withthespectacularincreaseinthevolumeofdata handled duringthelifecycleofBPs,whichisoftenheterogeneousinnature,conven- tional approachesformodelingandminingBPmodelsprovetobeineffective,hindering decision-making actions. Toovercometheselimitations,inthisthesisweleveragethelatestadvancementsachieved in theAIareainordertoimprovedecisionsupportsystemsinthefieldofBPsmanage- ment.Thefirstcontributionofthisthesisconsistsofaconceptualframework,calledDSS for BP(DSS4BP),whichallowsconstructingaKnowledgeGraph(KG)thatrepresents the datamanipulatedbytheBPsandtheirlinks.TheconstructedKGispoweredbya graphical capsuleneuralnetwork,anditspurposeistoenablepredictiveanalysisoffu- ture activitiesduringtheprogressionofaBP.ThisDSS4BPisbasedontheG-CAPS-NN architecturetrainedtodiscovertheKG-BP.ThisKGexcelsincapturingcomplexde- pendencieswithintheactivityflowscontainedinthedifferentBPsspecifications.Thus, the developedgraphpromotesahighpredictionoffutureeventsandadeepcontextual understanding ofBPvariationsandevolution.Oursecondcontributionisachat-bot named BPforDecisionSupportSystem(BP-DSS3),whichrefinestheGPT-3.5-turbo chat-bottoassistBPmanagedmakingmoreinformeddecisions.ThisBP-DSS3chat-bot leveragesdeeplearningtechniquestoprovidepersonalizedanddomain-specificdecision support.Afterthetrainingphase,itachievesahighlevelofprecisionandaccuracy when managingreal-worldscenarios,suchasAlignmentwithOrganizationalObjectives (AOO)andRiskManagementandContingencyPlanning(RMCP). The experimentsareconductedbasingonreal-worlddata,thetwoproposedframe- workshavedemonstratedsignificantimprovementsintermsofefficiency,adaptability, and performancecomparedtotraditionalapproaches.DSS4BPenablesorganizationsto proactivelyidentifyinefficienciesandpredictfutureoutcomesofdeployedbusinesspro- cesses, whileBP-DSS3significantlyimprovesdecision-makingbyprovidingactionable and domain-specificinformation.