世界經(jīng)濟論壇-在制造業(yè)中從人工智能中釋放價值（英）-2022.12-26正式版

IncollaborationwithMEXTTechnologyCenterUnlockingValuefromArtificialIntelligenceinManufacturingWHITEPAPERDECEMBER 2022Cover:JianFan,GettyImages–Inside:GettyImagesContentsForewordExecutivesummaryIntroduction1UnlockingvalueinmanufacturingthroughAI2SheddinglightoncommonbarrierstoindustrialAIadoption3AcollectionofAIapplicationsinmanufacturing4Astep-by-stepapproachtoimplementingscalableindustrialAIapplicationsConclusionContributorsEndnotesDisclaimerThisdocumentispublishedbytheWorldEconomicForumasacontributiontoaproject,insightareaorinteraction.Thefindings,interpretationsandconclusionsexpressedhereinarearesultofacollaborativeprocessfacilitatedandendorsedbytheWorldEconomicForumbutwhoseresultsdonotnecessarilyrepresenttheviewsoftheWorldEconomicForum,northeentiretyofitsMembers,Partnersorotherstakeholders.?2022WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,includingphotocopyingandrecording,orbyanyinformationstorageandretrievalsystem.UnlockingValuefromArtificialIntelligenceinManufacturing 2December2022 UnlockingValuefromArtificialIntelligenceinManufacturingForeword?zgürBurakAkkolChairman,TurkishEmployers’AssociationofMetalIndustriesTürkiyehasestablisheditselfasakeyglobalplayerinadvancedmanufacturingandaimstoboostitspositionthroughFourthIndustrialRevolutiontechnologies.Inrecentdecades,thecountryhasmadesignificanteffortstopositionitselfasaglobalinnovationhub,excellingindevelopingstate-of-the-arttechnologiesinground-breakingcompaniesinvariousfields.Artificialintelligence(AI)technologyapplicationsarepartofthiseffort.Inprinciple,AIcouldunlockmorethan$13trillionintheglobaleconomyandboostGDPby2%peryear.1However,companiesstruggletotapintothevaluethatAIapplicationscancreate.ThispaperseekstouncoverthehiddenpotentialofAIinthemanufacturingsectorandtherespectiveend-to-endsystemsbyprovidingpracticalusecasesandcriticalenablerstohelpharnessitspotential.Coupledwiththeenergycrisisandmaterialshortagesfacingtheworld,manufacturingplayersneedtogobeyondtraditionaloperatingmethodstodriveefficiencyandsustainability.Thetwinchallengesoftechnologicalprogressandsocio-politicaldistresscallfornewformsofcooperationthatrespondtoheighteneddemandforlocalizationwhilerecognizingthedriversofconnectivitythatshapeglobalimpact.Acknowledgingthis,theCentrefortheFourthIndustrialRevolutioninTürkiye–mandatedbythe

JeremyJurgensManagingDirector,WorldEconomicForumMinistryofIndustryandTechnologyandestablishedbytheTurkishEmployers’AssociationofMetalIndustries(MESS)–joinedtheWorldEconomicForum’sCentrefortheFourthIndustrialRevolutionNetwork,theforemostplatformhelpingleadersanticipateemergingtechnologiesanddrivetheirinclusiveandsustainableadoption.Thenetworklinkson-the-groundexperienceandactionwithglobalnetwork-basedcollaboration,learningandscaling.ThiswhitepaperisanoutputoftheongoingpartnershipbetweentheForum’sPlatformforShapingtheFutureofAdvancedManufacturingandValueChainsandPlatformforShapingtheFutureofTechnologyGovernance:ArtificialIntelligenceandMachineLearning,theCentrefortheFourthIndustrialRevolutionAffiliateinTürkiyeandMESS.Ithighlightscasestudiesfromorganizationsontheimpact,feasibilityandscalabilityofAIinmanufacturing.Itidentifiesseveralopportunitiesandlessonsfromthecommunityonhowtoincreaseoperationalefficiency,sustainabilityandworkforceengagementinmanufacturingandvaluechainsbyusingAI.Wehopethisreportwillprovidedecision-makerswithabetterunderstandingofhowtounlocktheuntappedpotentialofindustrialartificialintelligence(AI).Welookforwardtocollaboratingwithyoutodeploythesetechnologiesresponsibly.UnlockingValuefromArtificialIntelligenceinManufacturing 3ExecutivesummaryRecentglobaldevelopmentsandanever-growinglistofshocksanddisruptionshaveputfurtherstrainonalreadyshakenglobalvaluechains.Thecomplexityofcurrentchallengesimpactingmanufacturingandvaluechainscallsfortheneedtogobeyondthetraditionalmeansofdrivingproductivitytouncoverthenextwaveofvalueforbusinesses,theworkforceandtheenvironment.Artificialintelligence(AI)isacrucialenablerofindustrytransformation,openingnewwaystoaddressbusinessproblemsandunlockinnovationwhiledrivingoperationalperformance,sustainabilityandinclusion.EventhoughtheimpactofAIapplicationsonmanufacturingprocessesisknown,thefullopportunityfromtheirdeploymentisstilltobeuncoveredduetoanumberoforganizationalandtechnicalroadblocks.Recognizingthisneed,theCentrefortheFourthIndustrialRevolutionTürkiye,togetherwiththeWorldEconomicForum’sPlatformforShapingtheFutureofAdvancedManufacturingandValueChainsandPlatformforShapingtheFutureofTechnologyGovernance:ArtificialIntelligenceandMachineLearning,convenedindustry,technologyandacademicexpertstoshedlightonthesechallengesandproposeastep-by-stepapproachtoovercomethem.TheconsultationsrevealedsixmainchallengeshinderingtheadoptionandscalingofAIapplicationsinmanufacturing:AmismatchbetweenAIcapabilitiesandoperationalneedsTheabsenceofastrategicapproachandleadershipcommunicationInsufficientskillsattheintersectionofAIandoperationsDataavailabilityandtheabsenceofadatagovernancestructureAlackofexplainableAImodelsinmanufacturingSignificantcustomizationeffortsacrossmanufacturingusecases

Theconsultationsshowthatleadingmanufacturershavesuccessfullyovercomethechallengesmentionedabove,implementingavarietyofAIapplicationsandachievingapositiveimpactonoperationalperformance,sustainabilityandworkforceengagement,mainlyinsixareas:healthandsafety,quality,maintenance,productionprocesses,thesupplychain,andenergymanagement.WhileopportunitiesenabledbyAIinmanufacturingarepromisingandattractingmanyleaders,organizationsarelookingforacommonframeworkthatoutlineshowtoimplementAIsolutionsandensureasuccessfulreturnoninvestment.Basedontheconsultations,thiswhitepaperpresentsonestep-by-stepprocessasanexampleofhowitispossibletoovercomebarriers,usingtheAINavigator2developedbytheINCInventionCenterasareference:Phase0:Initiationtobuildthefundamentals–strategy,dataandworkforcePhase1:Ideationtoidentifypotentialusecasesandconductapre-selectionPhase2:AssessmenttoselectusecasesandidentifyprioritiesviagapanalysisPhase3:FeasibilitytocompleteallrequiredtestsandstudiesPhase4:Implementation,whichrequiresiterationandpilotingusingagileprojectmanagementMovingforward,theWorldEconomicForumandtheCentrefortheFourthIndustrialRevolutionTürkiyewillcontinuetoworkcloselywithstakeholdersintheCentrefortheFourthIndustrialRevolutionNetworkandacrossindustriestoacceleratethejourneytocapturevaluefromAIinmanufacturingglobally.ItwilloffertheTurkishEmployers’AssociationofMetalIndustries(MESS)TechnologyCentreasauniquetestingandcollaborationsystemforbusinessestopilotnewAIapplicationsandfosteracollaborativeapproachamongadiversegroupofstakeholderstoensuretherightAIcapabilitiesarebuiltinmanufacturingandrolledoutworldwide.UnlockingValuefromArtificialIntelligenceinManufacturing 4IntroductionCompaniesacrossvaluechainsarenowfacinganenergycrisisandmaterialandkeycomponentshortages,evenastheyarestillrecoveringfromandadaptingtoCOVID-19impacts.Thecomplexityofthechallengesimpactingoperationscallsfortheneedtogobeyondthetraditionalmeansofdrivingproductivitytouncoverthenextwaveofvalueandaddresssustainabilityandworkforcechallenges.Artificialintelligence(AI)canenableanewerainthedigitaltransformationjourney,offeringtremendouspotentialtotransformindustriestogaingreaterefficiency,sustainabilityandworkforceengagementbygeneratingnewinsightsfromlargeamountsofdata.However,despitethispromisingvaluecreationpotential,thedeploymentofAIinmanufacturingandvaluechainsisstillbelowexpectedlevels.Basedonaglobalsurveyconductedoverthelastfouryearsofmorethan3,000companiesacrossindustriesandgeographies,agrowingnumberofcompaniesrecognizethebusinessimperativetoimprovetheirAIcompetencies:–70%ofrespondentsunderstandhowAIcangeneratebusinessvalue–59%haveanAIstrategyinplace–57%affirmthattheircompaniesarepilotingordeployingAI.Despitethesetrends,only1in10companiesbelievetheygeneratesignificantfinancialbenefitswithAI.3

WhilemanufacturersacknowledgetheimportanceandurgencyofembeddingAIintheirprocessesandwhileleadingcompanieshavealreadyinternalizeditintheirbusinessprocesses,manyarebecomingdisillusionedwiththeireffortstocapturevaluefromitandlagindevelopingtherightAIcapabilities.UnderstandingthepurposeandroleofAIiskeytosolvingmanufacturingchallenges.Withaproblem-orientedapproach,AIeffortscanbelinkedtoclearbusinesstargets,givingbusinessunitsandbusinessfunctionsajointinterestinmakingthetransformationsuccessful.4ThiswhitepapershedslightonthebenefitsthatcanbeachievedthroughindustrialAIandthesuccessfulAIapplicationsimplementedacrossindustries,lessonslearnedandtangibleimpacts.ConsultationsconductedwiththemultistakeholderinitiativecommunityfindthatindustrialAIhelpspeopleworkinasmarter,saferandmoreefficientway.However,tounlockitsfullpotential,companiesrequireanunderstandingofcurrentbarrierstoadoptionandastructuredapproachtoovercomethem.Therefore,thispaperalsopresentsoneexampleofastep-by-stepguidetosuccessfullyimplementingscalableindustrialAIusecases.UnlockingValuefromArtificialIntelligenceinManufacturing 5UnlockingvalueinmanufacturingthroughAIAIapplicationsinmanufacturinghelpincreaseoperationalperformance,drivethesustainabilityagendaandempowertheworkforce.Theartificialintelligence(AI)revolutionallowstheconversionoflargeamountsofdataintoactionableinsightsandpredictionsthatcanprovideimpetustodata-drivenprocesses.ManufacturingcompaniescapturevaluefromAIusingdifferentmechanisms,themostcommonbeingeliminatingredundantwork,solvingexistingproblemsandrevealinghiddenvaluebyanalysingandrecognizingpatternsindata.AIisappliedtoaugmenttaskssuchasclassification,continuousestimation,clustering,optimization,anomalydetection,rankings,recommendationsanddatagenerationtosolveindustrialproblems.5ConsultationswithseniorexecutivesfromtheWorldEconomicForum’sPlatformforShapingtheFutureofAdvancedManufacturingandValueChainsandPlatformforShapingtheFutureofTechnologyGovernance:ArtificialIntelligenceandMachineLearning,aswellasmembersandpartnersoftheCentrefortheFourthIndustrialRevolution

Türkiye,findthatAIcanhelpdriveastep-changeinmanufacturing,yieldingsignificantbenefitsinthreecategories(figure1):–Operationalperformancebyautomatingandoptimizingroutineprocessesandtasks,increasingproductivityandoperationalefficiencies,improvingquality(e.g.reducingdefects,forecastingunwantedfailures)andoptimizingproductionparameters–Sustainabilitybyoptimizingmaterialandenergyusage,increasingenergyefficiencies,reducingscrapratesandextendingmachinelifespans–Workforceaugmentationbyguidingthedecision-makingprocessandparametersetting,enhancingtheaccuracyofpredictionsandforecasting,reducingrepetitivetasksandincreasinghuman-robotinteractionsUnlockingValuefromArtificialIntelligenceinManufacturing 6FIGURE1 DimensionsofvaluecreationwithAIinmanufacturingOperationalperformancePerformance(e.g.yieldoptimization)Throughput(e.g.fewerunwantedbreakdowns,decreasedleadtime)Quality(e.g.fewerprocessdefectsandfailurerates)Businessuptime(ductivetimeandcapacity)WorkforceaugmentationDecision-makingandplanningsupportCollaborationPredictionandforecastingaccuracyTaskautomationRisk(e.g.feedbackmechanismtoavoidincidentsandalarms)SustainabilityMaterialefficiencyEnergyefficiency(e.g.energysavingsandthermalefficiency)MachinelifetimeScraprateandusedmaterialUnlockingValuefromArtificialIntelligenceinManufacturing 7SheddinglightoncommonbarrierstoindustrialAIadoptionImplementingAIsolutionsrequirescontinuousprojectmanagementefforts,expectationmanagementandthenecessaryresources.Despitethispotential,companieshavenotyetfullyrealizedthevisionofAI-poweredmanufacturingsystems.TounlocktheuntappedvalueofindustrialAI,pinpointingthesourceofacompany’sstrugglesanddefiningtheroadblocksopenanewpathtothinkthroughandderivetherightsolutionstoovercomethem.AsthebarrierstoAIadoptionstemmainlyfromorganizational,strategicandtechnicalFIGURE2 BarrierstoAIadoptioninmanufacturing

components,understandingthemwillhelpidentifyapathwaytoimplementscalableAIapplications.Consultationswiththecommunityofover35senioroperationsexecutives,technologyexpertsandacademicshaveidentifiedsixchallengeshinderingtheadoptionofAIinmanufacturingandvaluechains(figure2).MismatchbetweenAIAbsenceofastrategicInsufficientskillsatthecapabilitiesandoperationalapproachandleadershipintersectionofAIandneedscommunicationoperationsDataavailabilityandLackofexplainableAISignificantcustomizationabsenceofadatamodelsinmanufacturingeffortsacrossgovernancestructuremanufacturingusecasesUnlockingValuefromArtificialIntelligenceinManufacturing 8MismatchbetweenAIcapabilitiesandoperationalneedsManufacturershaveoftenselectedAIprojectsbasedonexistingtechnicalcapabilitiesinsteadoffocusingontheimpactonbusinessoperations.ThematchbetweenbusinesspainpointsandAItechnologiesisnotalwaysthoroughlyconsidered.Therefore,AIsolutionsmaybetechnicallyfeasiblebutfailtosolvearelevant,impactfulproblemin

operations.Thiscausesamismatchofexpectationsandhinderstheirwideradoptioninmanufacturing.Buildingasolidbusinesscasewithaproblem-orientedapproachthatclearlydefinesbusinessneedsandevaluatingthevalueofanAIsolutioncomparedtoalternativesolutionsarethefirststepsinovercomingthatbarriertoadoptionandscale.AbsenceofastrategicapproachandleadershipcommunicationAclearcompany-wideAIstrategyandcommunicationplanareoftenignored.Withouttherightsponsorsandcommittedleaderstostartthedialogueandcollectthebuy-infromend-users,theonboardingofAIapplicationsacrossthecompanycan’toccurdue

toworkforcereluctance.AsAIischangingthewaysofworking,communicatingthestrategicapproach,benefitsandnewprocessescanhelpincreaseend-users’willingnesstoembraceitintheirroutines.InsufficientskillsattheintersectionofAIandoperationsExternalconsultantsorinformationtechnology(IT)expertswhohavealimitedunderstandingofthemanufacturingrequirementsontheshopflooroftenleadAIprojects.However,tobesuccessful,AIapplicationsrequiredevelopment

andimplementationbycross-functionalteamswithdiverseexpertiseattheconvergenceofIT,operationaltechnology(OT),dataandAItechnologies.Thisrequiresupskillingtheworkforceandattractingnewtalentinmanufacturing.UnlockingValuefromArtificialIntelligenceinManufacturing 9DataavailabilityandtheabsenceofadatagovernancestructureApplyingmachinelearningmodelsrequirestrainingonlargeamountsofdatatorecognizepatternsandrelationships.6However,manufacturingcompaniesoftenrelyonsmalldatasetsandfragmenteddata,hinderingtheaccuracyoftheresultinginsights.Evenwhenavailable,thesedatasetsmaynotrepresentappropriatefailurecasesorrelevantprocesssituationsandaremostlynotinteroperable.

Creatingasinglesourceofinformationensuresthatbusinessesoperatebasedonstandardized,relevantdataacrosstheorganization.Toovercomethischallenge,sharingdataacrosscompanies’boundariescansupportjointeffortstoadoptartificialintelligencetechniquesinthemanufacturingsectorandrely,inturn,onasetoforganizationalandtechnologicalsuccessfactors.7LackofexplainableAImodelsinmanufacturingTheperceptionofAImodelsascomplex,non-transparentanduninterpretablesystemshinderstheirdeployment.ManufacturersneedAImodelsthatareeitheropenandtransparenttobuildtrustinthepredictionsandspecificresultsorinterpretablefordomainexpertstoacceptthem.AI-providedpredictionsneedtobemeaningful,explainable

andaccurateandhaveawarningmechanisminplacetominimizerisks.ExplainableAItoolsandtechniquesallowexpertstoobtainjustificationsfortheirresultsinaformatthatmanufacturinguserscanunderstand.ThegreatertheconfidenceintheAI-poweredoutput,thefasterandmorewidelyAIdeploymentcanhappen.SignificantcustomizationeffortsacrossmanufacturingusecasesFactoriesarecomplexengineeredsystemsandAImodelsneedconfigurationtobeadaptedtoeachprocessandconformtoitsconstraints.Hence,itisnotpossibletosimplyapplytrainedAImodelsorpipelinesfromonemanufacturingusecasetoanother.Thedesignofthemachinelearningpipelineandthepre-processing,trainingand

testingofAImodelsstillneedmanualinterventionforcustomization,whichisnotyetfullyautomated.Additionally,industrialcompaniesstruggletofindcommerciallyavailablehardwareandsoftwarewithoff-the-shelfAIfeaturesthatrequireminorcustomization.Sheddinglightonthesechallengesandunderstandingthemcanhelpidentifytherightsolutionsandapproachestoovercomethem.UnlockingValuefromArtificialIntelligenceinManufacturing 10AcollectionofAIapplicationsinmanufacturingAIapplicationscanboostoperationalperformanceandleadtoapositiveimpactonsustainabilityandworkforceengagement.Consultationswithover35senioroperationsexecutivesandtechnologyexpertsfindthatleadingmanufacturingcompanieshavesuccessfullymanagedtoapproachandovercomethechallengesmentionedabovebystartingwiththeirbusinessneeds,outliningaclearstrategy,buildingcross-functionalcapabilitiesandputtingastrongerfocusondatagovernance,andselectingAImodelsthatmeettheirneeds.TheyhaveimplementedavarietyofAIapplicationsthathaveboostedtheiroperationalperformanceandledtoapositiveimpactonsustainabilityandworkforceengagement.

ToillustratethepotentialandfeasibilityofAIinmanufacturing,thecreationofanindustrialAIusecaselibrarywithinputfromthecommunityhasstarted.The23usecasescollectedacrossdifferentindustriescoversixmainapplicationareas:healthandsafety,quality,maintenance,productionprocess,supplychains,andenergymanagement(figure3).UnlockingValuefromArtificialIntelligenceinManufacturing 11FIGURE3 LeadingmanufacturersareimplementingavarietyofAIapplications1AIinmanufacturingusecases65Source:CompanyinterviewsEnergymanagementSupplychains–Energyoptimization–Futuredemandandprice–Electricitydemandforecastingforecasting–Heatingandcoolingoptimization–Supplychaincontroltower–Warrantyandservicemanagement

234Productionprocess–Processoptimization–Linebalancing–Productdesignanddevelopment–Processparameteroptimization–Productionplanning/decisionsupport

Healthandsafety–Employeehealth&safety:incidentprevention–Processsafety:advancedalarmanalyticsQuality–Qualityinspectioninassembly–Qualityassurance/defectinspection–Qualitytesting–QualitypredictionMaintenance–Machinehealthmonitoring:predictivemaintenance–MaintenanceplanningTheusecasescollectedprovidevaluableinsightsindicatingthebusinessneed,thesolutionimplementedandtheimpactachieved.Theapplicationsshowthatthereturnoninvestment(ROI)ispositiveandthepaybackperiodofthe

investmentsisusuallytangiblewithin1-2years.AfterpilotingtheAIapplicationsinonedivision,manufacturingcompanieseitherhavealreadydeployedtomultipledivisionsorhavethevisiontoscale.UnlockingValuefromArtificialIntelligenceinManufacturing 12TABLE1AcollectionofAIinmanufacturingusecasesUsecaseCompanySectorAIapplicationImpactModeldesignedasanexperiencedoperator/engineer–Totaltimeofalarmfloodsincontinuousestimationanddecreasedby40%classificationofalarms,detection–Numberofalarmsofnuisancealarms,alarmfloodProcesssafety:decreasedby50%Tüpra?,analysisandrecommendationadvancedEnergy–Timeefficiency:AlarmTürkiyeofbetterconfigurations.Rootalarmanalyticsrationalizationmeetingscauses,next-bestactionsandshortenedfrom4hourssafetysetpointsextractedfromtheto30minuteshistoricaldatathroughbasic&descriptiveanalyticsanddatasciencepre-processtechniquesHealthImagerecognitionbymonitoring–UnsafesituationsandEmployeetheshopfloorwithexistingactionsreducedby70-cameras,receivingreal-time80%health&Intenseye,Manufacturingalertnotificationsandenhancingsafety:incidentUSA–Withasaferenvironment,employeehealthandsafetypreventionamoreproductive(EHS)toeliminatelife-alteringworkforcewithincreasedinjuriesbusinessuptimecreatedExaminingtheeffective–Upto40%savingsachievedinenergyuseparametersontheframes–ScrapratereducedwhileReal-timespotMarturbeingweldedinroboticspotensuringsustainabilityinweldqualityFompak,Automotiveweldstations(weldquality)productionpredictionTürkiyeandpredictingthespotnugget–Costsreducedby60%diameterrealizedinlineinrealbypreventingtheuseoftimeexcessweldingmaterialsVisualinspectiontoensure–ProductivityincreasedDetectionofthecoatingqualityisgoodbyby11%Bosch,checkingpartsandsearchingfor–15millionpartscheckedcarboncoatingAutomotiveTürkiyecoatingdefectsinfourdifferenthadnoincidentsdefectsclasses:scratches,damages,blackinblack,silverQualityOptimizingqualityinspectionofcustomizedproductsbyQualitydeployingcloudservicesand–ProductivityincreasedbyassuranceafederatedlearningapproachHuawei,30-40%withfederatedProduction(localdatacollected,globalChina–Leadtimereducedlearninginoptimuminterpolatedandinturncontrolsharedbacktoalllocalfacilitieswithoutdisclosingsensibleproductorprocessdata)ExplainablecomputervisionmethodsusedtosupportfactoryQualityworkersindetectingassemblyinspectionEthonAI,ElectronicserrorsonprintedcircuitboardsinassemblySwitzerland(e.g.missing,faulty,orwrongverificationcomponents)viaahuman-AIinterface(camerasystemwithlivefeedback)

–10xlessimplementationeffortexpended–TrustworthinessofthesystemincreasedwiththeexplainablemodelUnlockingValuefromArtificialIntelligenceinManufacturing 13UsecaseCompanySectorAIapplicationImpactVisualinspectionoffibreratioin–ReportpreparationtimeforcustomercomplaintsyarncontentusingmicroscopicKarsu,andanalysisexpectedtoQualitytestingTextileimagestocheckproductionTürkiyedecreaseby90%qualityandtoanalysecustomer–ExpertrequirementcomplaintsforthesubjectwillbeeliminatedQuality

VisualinspectionofthequalityQualityK?rberofpharmaceuticalswhileAIinspectionrecognizespatternsinsteadofDigital,Pharmaceuticalsindrug-andmeasuringphysicalimagevalues,Germanypatientsafetywhichdecreasesthefalse-rejectofproducts

–Reductionoffalse-rejectratebyanaverageof88%–Detectionrateincreasedbyanaverageof38%–Approximately2xfastertime-to-marketachieved(transferability)invisionsetupAnAIenginethatpredictsthe–MachinecapacityPredictiveSchneiderdemagnetizationvoltagetoincreasedElectric,Electronicsreducethenumberofiterations–CapexinvestmentreducedqualityFranceduringrelaytestsinresidual–RejectionsreducedcurrentdeviceproductrangeThroughcombinationofdigitaltwinandinnovativeAI,processObeikananomalyconditionsanddriversQualityDigitalChemicalsdetectedpredictionSolutions,SaudiArabiaStatisticalprocesscontrolalgorithm,aprovenapproachofqualitycontrol,used

–Productivityandqualitysustainabilityincreased–OverallequipmenteffectivenessinPETlinesimprovedby20%–CustomercomplaintsreducedProductionprocess

Providingautomatedsoftwareto–Alloyusereducedby9%takepreventiveactionsearlyintheatsteelmillsProcessFeroLabs,SteelproductionprocesswithexplainableoptimizationUSAAImodelstoreducerawmaterial–FailurerateeliminateduseandminimizecostsandemissionsduringsteelproductionAI-basedvideoanalyticstolabel–Productivityincreasedby25%theactionsofmanualtasks–ByincreasingqualityandKhenda,toeliminateoperator-relatedLinebalancingAutomotiveefficiency,errorcostsTürkiyeerrorsandimprovemanualeliminatedandwastemanufacturingprocessesandanddefectiveproductsoptimizelinebalancingavoidedGeneratinginsightsintothecomplexinteractionsbetweenhundredsofprocessparametersProductionDataprophet,andtheirimpactonfinalqualityparameterFoundrybyusingdeeplearningalgorithmsSouthAfricaoptimizationApplicationthenprescribesnext-beststeptooptimizeproductionwithoutpoorquality

–Defectsreducedto0%froma6%ofhistoricaldefectrate–Numberofqualitystopsreducedfrom81to20perweekUnlockingValuefromArtificialIntelligenceinManufacturing 14UsecaseCompanySectorAIapplicationImpactProductionprocess

AdvanceddecisionsupportAr?elik,HomeAppliancesystemonTürkiyeperformancetestProcessGEP,USAChemicalsmanagement

Improvingcoolingtestperformanceindifferentanddynamicallychangingclimaticconditionstoshortenthetestdurationbyanin-housedecision-makingsystembasedonAIandmachinelearning(ML)ImplementingAI-enabledprocesscontrolstomanagecatalystingestionbasedonpressureandtemperaturechangesinthereactorandtomanagethetransferrates

–Servicecallrateimprovedby15.3%.–17.8%oftestcapacityincreasedbydecreas