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1、GenerativeAPsRoleintheFactoryoftheFutureDECEMBER08,2023ByDanielKupper,KristianKuhlmann,MonikaSaundersjJohnKnapp,Kai-FredericSeitzjJuIianEnglberger,TilmanBuchner,andMartinKleinhansREADINGTIME:15MINGenerativeAlisoneoftoday,shottestbusinesstopics,withcompaniesexploringitspotentialapplicationsandbenefit
2、sacrossindustriesandfunctions,includingmanufacturing.Butdespitetherecentbuzz,manufacturersshouldrecognizethatsimplyapplyingtoolslikeChatGPTontheirownwillnotrevolutionizefactoryoperations.InsteadofreplacingtraditionalAl,GenAIofferscomplementaryusecasesintheareasofassistance,recommendations,andautonom
3、ythatpavethewaytothefactoryofthefuture.Itdoessothroughitscapacitytogeneratecontent,suchastextandimages,tailoredtospecifictasksorinquiries.(SeeuHowGenAIWorks.n)Howgenaiworks-TodiscusstheapplicationsofGenAI,itisessentialtofirstdefinehowitdiffersfromttclassica,machinelearning(ML).ClassicalMLalgorithmsd
4、iscernpatternswithinobserveddata,enablingthemtogeneralizetheseinsightstonew,previouslyunseendata.Forinstance,anMLmodelmightbetrainedusingspecifictextfragmentssuchasoperatorincidentreportsinwhichmachinebreakdowndescriptionsareclassifiedintospecificrootcausessuchas,endoftoolinglifeoroperatorerror.Base
5、donthistraining,themodelcanprocesspreviouslyunseentextfragmentsofincidentreportsandjudgewhatcausedtheincident.Thebasisforsuchmodelsmaybedeepneuralnetworks,supportvectormachines,orothermethods.GenAItakesthisapproachfurther.Beyondmerelyclassifyingexistingtext,itcangeneratenewtextbasedonspecifiedcriter
6、ia-suchasoperatorinstructionsthatoutlineaprocesstoresolveaparticularrootcauseofamachinebreakdown.AlthoughtheprogressionfromclassicalMLtoGenAImightseemincremental,itposesafundamentaltechnicalchallenge.InclassicalML,themodelmerelyneedssuffcienttrainingtoconfidentlycategorizeatextfragment.Incontrast,Ge
7、nAImustconstructatextfragmentfromindividualwordsandletters,ensuringthatitisgrammaticallycorrect,comprehensible,andaccuratelyrepresentstheprocess.ThenumberofpotentialoutputsfromGenAIisvirtuallylimitless.Consideringthatthereareroughly170,000Englishincurrentuse,amerefive-wordtexthasmorethan140septillio
8、npotentialcombinations.Ontheotherhand,onlyafractionofthemwouldbegrammaticallycorrectandunderstandable.Amongthose,anevensmallerfractionwouldaccuratelydescribeagivenprocesstofixtherootcauseofamachinebreakdown.Consequently,themarginforerrorinGenAImodelsisincrediblynarrow,necessitatingextremelyprecisemo
9、dels.Toattainthisprecision,GenAImustuseufoudationalmodels5*insteadofbeingtrainedonlyoncontext-specificdata.Foundationalmodelsaretrainedonextensivedatasets,suchasallavailabletextorimagesonline,andaresubsequentlyfine-tunedforspecificapplications.Thesemodelscanbelargelanguagemodels(suchasOpenAsGPT-4orA
10、mazonQ)orimageorspeechmodels.Theyseemtogainanunderstandingofrealityfromtheextensivedatasets.However,foundationalmodelsareobservationallearnersthatdonotapplylogicorreasonashumansdo.ThismeansthatthereisnoguaranteeofplausibleoraccurateresultsfromGenAI.Inouroperatorinstructionexample,thefoundationalmode
11、lfirstlearnswhatconstitutescomprehensibleandaccuratetext,withprocessdescriptionsbeingasmallsubset.Next5themodelisfine-tunedbylearningwhatoperatorinstructionslooklikeandhowtheycorrelatewithgivenmachinebreakdownrootcauses.However,thereisnoassurancethatthemodelwillcreatecorrectorhigh-qualityoperatorins
12、tructions.Ergonomicsillustratestheproblem.BecausetheGenAImodellacksinsightintotheprocessthatfixestherootcauseandthepeoplewhoaretheoperators,itmightoverlookpotentiallimitations,suchasinfeasiblemovementsorinaccessiblespaces.Asaresult,aqualityassessmentisalwaysrequiredtoensurethattherecommendedremediat
13、ionispracticalfromanergonomicperspective.GenAsgreatertechnicalcomplexityelevatestheimportanceofestablishingarobusttechnologicalfoundationtoharnessitscapabilitieseffectively.Withanumberofarchetypespossible,manufacturersmustunderstandthefactorsthatdetermineanoptimalchoice.Theycanapplythisknowledgetoin
14、tegrateGenAIintofactoryoperations,consideringthevalue-addingapplications,change-andpeople-relatedinitiatives,andrequiredtechnologicalinfrastructure.ManufacturersArePrioritizingGenAIforitsDisruptivePotentialBCGrecentlysurveyedmanufacturerstounderstandtheirperspectiveontechnologydevelopments.(SeeAbout
15、theSurvey/*)Regardlessoftheiraffinityfordigitaltechnology,manufacturingexecutivesrankedAl(includingGenAI)firstamongtechnologiesthatcouldpositivelydisrupttheiroperations.(SeeExhibit1.)ThepotentialROIwarrantstheirenthusiasm.ABCGanalysisfoundthattheuseofAlcouldenhanceshop-floorproductivitybymorethan20%
16、.ABOUTTHESURVEYBCGconductedaglobalsurveyfromJanuarythroughMarch2023toassessthelatesttechnologiesinthemanufacturingindustry.Approximately1,800respondentsfrom15countriesspanningNorthandSouthAmerica,Europe,andAsiatookpartinthestudy,witheachcountrycontributingmorethan100completedsurveys.Theparticipantsrepresentabroadarrayofproductionindustries,includingautomotive,capitalgoods,consumergoods,energy,IT,healthcare,andmaterials.Exhibit1-ManufacturingExec
