CAR-汽车行业智能制造：落地应用与行业影响 Smart Manufacturing in Automotive Deployment and Impact

SmartManufacturinginAutomotive:

Deploymentandimpact

CenterforAutomotiveResearch|May2026

Executivesummary

TheautomotiveindustryisenteringanewphaseofAI/machinelearning(ML)andautomation.Thequestionformanufacturersservingtheautomotive,tire,andbatterymarketsisnolongerwhethertoadopt,buthowquicklyandwheretoputsmartmanufacturingtowork,accordingtoCenterforAutomotiveResearch

(CAR)analysis.

Automakersandsuppliersalreadyoperatewithindustry-leadingautomation,particularlyinbody,paint,

andwelding.Whatischangingiswhereit’sapplied.Manufacturersgloballyaremovingintoareasthathavehistoricallybeenhardertoautomate,includingelectronicsassembly,validation,productioncoordination,andlogistics.AI/MLissimultaneouslyimprovingpredictivemaintenance,inspectionaccuracy,andsystemperformanceacrossexistingoperations.

Thedriversareclear:morecomplexproductionenvironments,persistentwarrantyissues,rising

commoditycosts,andglobalcompetitionareleavinglessroomforlate-stagefixesandreactive

management.Automationisalsoenablingonshoringbysupportingcost-competitiveproductionundertightlabormarketconditions.

Theresultsaremeasurable.Manufacturersreportedreductionsinunplanneddowntimeofupto50%

inselectapplications,overallequipmenteffectiveness(OEE)improvementsofapproximately5%,and

throughputgainsof5–7%fromreal-timeproductionanalytics.Autoliv’sproductivityacceleration,from

roughly4%in2023toover9%in2025,isoneofthemoreconcreteindicatorsofwhatsustainedinvestmentcandeliver,inCAR’sview.Puttingthisintoperspective,DurableGoodsManufacturingaveragedjust2.7%

productivitygrowthin2025,whileMotorVehicleParts(NAICS3363)datathrough2024rangedfrom2.6%to5.9%annually.

Theimpactisalreadyvisibleontheplantfloor.TeamsusingadvancedAI/MLtechnologiesareidentifying

issuesearlier,reducingdowntime,andimprovingprocessconsistencyacrossplants.Thesegains,however,arenotuniform.Differencesinhowcompaniesembracesmartmanufacturing,particularlyinquality,uptime,andprocesscontrols,arebeginningtoseparatehigherandlower-performingmanufacturersandsuppliers,accordingtoCARresearch.

Leadingcompaniesareextendingthesecapabilitiesacrossplantsandprocessfunctions,andtheyare

increasinglyexpectingthesameoftheirsuppliers.Theindustryandsupplybasearealsodevelopinggapsthatcarrystrategicimplicationsforsourcing,programexecutionandlong-termcompetitiveness.

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RockwellAutomation•SmartManufacturinginAutomotive:Deploymentandimpact|03

Automotive:Industry-leadingSmartManufacturing

The2026RockwellAutomationStateofSmartManufacturingsurveyplacesautomotivealongsidehigh-

performing,hi-techandlifesciencesinoveralldeploymentandfutureinvestmentintent.Thematrixbelow

mapsindustriesbycurrentdeploymentlevelandplannedinvestment;automotivesitsintheleadingquadrantonbothdimensions.

ManufacturingindustrySmartManufacturingmatrix

Futureinvestment/Strategicintent

—

—

—

—

—

—

Pulp&PaperWater/Wastewater

Warehouse

Home&PersonalCareChemical

20406080100

Currentdeployment/Digitalmaturity

80—

60—

40—

20—

0—

0

AerospaceFood&Beverage

Metals

Hi-Tech

Automotive

LifeSciences

Energy

Mining

Oilandgas

100—

Figure1.Manufacturingindustrysmartmanufacturingmatrix

Source:11thAnnualStateofSmartManufacturingReport,RockwellAutomation

Methodology:Thematrixcomparesindustriesbasedontwocompositemeasuresaggregatedacrossallsmartmanufacturingtechnologycategories.

TheDigitalMaturityIndex(X-axis)measurescurrentadoptionusingthepercentageofrespondentsreportingthattheyhave“alreadyinvested”inrelevanttechnologies.TheFutureIntenttoInvestIndex(Y-axis)measuresplannedadoptionusingthecombinedpercentageofrespondentsplanningtoinvest

withinthenext12monthsandwithinthenextfiveyears.

RockwellAutomation•SmartManufacturinginAutomotive:Deploymentandimpact|04

Theautomotiveindustryalreadyoperateswithextensiveautomationacrossstamping,body,paint,welding,andfinalassembly—builtforrepeatability,throughput,andprecision.Whatthesesystemswerenotoriginallydesignedtodo,however,istomanagereal-timevariability,anticipateequipmentfailures,orcoordinate

acrosscomplexproductionsequences.

CARresearchidentifiesthenextphaseforautomationsystemswithinautomotiveasextension–not

replacement.AIandexpandedautomationarereachingintoareasforautomakersandsuppliersthathavehistoricallydependedonoperatorexperienceandjudgment.Thefollowingillustrateswherethosegaps

persistandwhatisnowpossible.

HISTORICALCHALLENGE

SMARTMANUFACTURINGPATHFORWARD

Quality&inspection

Manualchecksandpost-processvalidationcreateinconsistencyatscale

ProcessControl

Weld,torqueandcalibrationparametersrelyonoperatorjudgement

Inlinesensorsandvisionsystemsenablecontinuous,automateddefectdetection

Automatedprocesscontrolreplacesoperator

judgementwithdefined,measureablestandards

Electronicsintegration

Legacyautomationwasnotdesignedtoaddressnewfailuremodes

AI-assistedvalidationadaptstoevolvingvehiclearchitecturesandfailuresignatures

Productioncoordination

Disruptionresponsedependsonmanualintervention

Maintenance

Reactiveandschedule-baseddespiteavailablereal0timeequiopmentdata

Connectedplatformsenabledynamic

re-sequencingwithminimalhumaninput

Predictivemodelsshiftoperationsfrom

time-basedschedulestocondition-basedaction

Flexiblepowertrainstrategies,whereICE,hybrid,andBEVvariantsareproducedonthesameassemblylines,havecompoundedthesechallenges.Sohasthesurgeinelectroniccontent,whichbringsgreatersoftware

complexity,moredemandingcalibration,andadditionalvalidationsteps.Theneteffectisaproductionenvironmentfarmorecomplexthanevenadecadeago,onethatincreasinglydemandssystem-level

managementandsmartmanufacturingsolutionsovertask-levelautomation.

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AutomotiveIndustry–SmartManufacturing:Whynow

ThepushtoexpandautomationanddeployAI/MLisbeingdrivenbyaconvergenceofoperationalandcompetitivepressures,whichhaveintensifiedoverthepastseveralyears:

Productioncomplexity

Mixedpowertrainproductionhasincreasedthenumberofvariablesthatmustbemanagedduringdaily

vehicleassemblyoperations.Whereasingle-powertrainlinemightrequiremanagingdozensofprocess

parameters,amixed-lineproducingICE,hybrid,andBEVvariantssimultaneouslyrequiresmanagingseveraltimesthatnumber.Higherelectronicscontentaddsadditionalcomplexity,too,thatis,morecalibration

steps,morevalidationrequirements,morepotentialfailurepoints.

Flexiblemanufacturingsystemsintroduceadditionalvariability.Eachconfigurationaddsparametersto

monitor,thresholdstoset,anddecisionstomake.ThisiswhereAI/MLismostuseful:identifyingpatternsinhigh-dimensionalprocessdatathatoperatorsandengineerscannotmonitorcontinuously.

Operationalcostpressure

Commodityinflation,vehicleaffordabilityconstraints,andpersistentmargin(cost)pressurehaveincreasedfocusonyield,scrap,throughput,andunplanneddowntime.Inthisenvironment,late-stagequalityfixes,

emergencymaintenancecalls,andproductiondisruptionsthatcouldhavebeenanticipatedcarrypotentiallyseverefinancialconsequences.Forexample,asingleproductionstoppagecancosttenstohundredsof

thousandsofdollarsperhourathigh-volumeassemblyfacilities.Predictivemaintenancesystemsthatreduceunplanneddowntimebyevenafewpercentcandelivermeaningfulcostimprovement.

Globalcompetition

CompetitionfromChinaisincreasingexpectationsaroundspeed,cost,andmanufacturingintegration.

Furthermore,ChineseOEMsarereportingfasterdevelopmentcyclesandtightercoststructuresinpart

becauseofhighlyintegrated,automatedproductionenvironments.Theexpectationthatdomestic

manufacturerscanmatchthesecoststructureswhileproducingmorecomplexvehiclesandsimultaneouslyimprovingquality,isincreasingpressureonbothOEMsandsupplierstoimprovemanufacturingperformance.

Onshoringandlabor

Automakers’onshoring(reshoring)commitmentsarerunningdirectlyintopersistentlaborshortagesacrosspartsofthesupplybase.Automationisenablingcost-competitiveproductionunderconditionswhere

laborsupplyisconstrained.Thejobsthatreturnfromreshoringwillbemoreautomatedthanthejobsthatleft,requiringadifferentskillsetandadifferentmanufacturingmodel.System-guidedprocessesreducedependenceonexperiencedoperatorsandcanmaintainproductionconsistencyevenwhenexperiencedpersonnelareunavailable.

Limitsofexistingsystems

Existingautomationisstronginrepeatabilitybutlesseffectiveinmanagingvariation,disruption,and

complexity.Theautomotiveindustrymaybepressingthelimitsofwhattask-levelautomationcandeliver.Thenextphaseofperformanceimprovement,inquality,uptime,productivityandresponsiveness,requiressystemsthatcanlearn,adapt,andsupportdecisionsinrealtimeratherthanexecutingfixedinstructions.

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SmartManufacturingwithintheAutomotiveIndustry

Figure2below,mapsautomotivetechnologydeploymentandfutureinvestmentagainsttheindustryaverage,using2026RockwellAutomationsurveydata.Automotiveinvestmentintentissofar

concentratedinquality,analytics,andautomationsystems,consistentwiththeoperationalprioritiesdescribedinthispaper.

ManufacturingindustrySmartManufacturingmatrix

Futureinvestment/Strategicintent

—

—

—

—

—

—

Simulation

Clound/SaaS

Robotics

CybersecurityNLP/Voice

Cobots

AI/ML

Wearables

GenerativeAI

VisionSystems

AMRs/AGVs

DigitalTwins

020406080100

Currentdeployment/Digitalmaturity

100—

80—

60—

40—

20—

0—

Figure2.Automotivetechnologydeploymentandfutureinvestmentmatrix

Source:11thAnnualStateofSmartManufacturingReport,RockwellAutomation

Methodology:Thematrixcomparessmartmanufacturingtechnologycategorieswithintheautomotiveindustryusingtwocompositemeasures.The

DigitalMaturityIndex(X-axis)measurescurrentadoptionusingthepercentageofautomotiverespondentsreportingthattheyhave“alreadyinvested”ineachtechnologycategory.TheFutureIntenttoInvestIndex(Y-axis)measuresplannedadoptionusingthecombinedpercentageofrespondentsplanningtoinvestineachtechnologycategorywithinthenext12monthsandwithinthenextfiveyears.

RockwellAutomation•SmartManufacturinginAutomotive:Deploymentandimpact|07

Qualityprocesses

Inspectionhashistoricallyincludedmanualchecksandpost-processvalidation.Automationisexpandingintoin-lineinspectionandanomalyidentificationduringproduction.AI/MLsupportsdetectionand

classification,buttheprimaryshiftismovingqualityprocessesclosertothepointofproduction,thatis,catchingproblemsbeforetheypropagatethroughassembly.

ExamplesincludeAI-enabledvisionsystemsforpaintandsurfaceinspection,automatedelectronics

validation,in-lineanomalydetectiononbodypanels,andtraceabilitysystemsthatlinkprocessparameterstospecificvehicleorcomponentbuilds.AccordingtoCARresearch,whenafieldqualityissueappears,

manufacturerswithfulltraceabilitycanidentifytherootcauseandaffectedpopulationinhoursratherthanweeks.

Processmonitoringandadjustment

Coreprocessesareautomatedbutmonitoringandadjustmenthaveoftenreliedonoperatorsandengineersreviewingdataafterthefact.Systemsareincreasinglybeingdeployedtostandardizemonitoringand

automateparameteradjustments,reducingrelianceonshift-to-shiftjudgmentandimprovingrepeatabilityacrossfacilities.

Examplesincludeautomatedadjustmentofweldparameterswhenprocessdriftisdetected,torquesettingverificationandcorrectionduringassembly,calibrationroutineautomationforelectronics-intensive

vehicles,andpaintapplicationparameteradjustmentbasedonreal-timeenvironmentalconditions.Theseadjustmentshappencontinuouslyandsystematicallyinadvancedoperations,notepisodicallybasedon

operatorobservation.

Equipmentdiagnosticsandmaintenance

Maintenancedecisionshavetraditionallybeenschedule-basedorreactive.Automationisexpandinginto

continuousdiagnosticsandcondition-basedmaintenanceworkflows.AI/MLsupportsearlieridentificationofequipmentissuesandhelpsreduceunplanneddowntimebyidentifyingdevelopingproblemsbeforetheycausefailures.

Applicationsincludeweldingrobots,stampingpresses,paintlineconveyors,andassemblyrobots,

equipmentcategorieswhereunplannedfailurehashighproductioncost.Manufacturersusingreal-time

monitoringandpredictiveanalyticshavereportedreductionsinunplanneddowntimeofupto50%inselectapplications,alongwithimprovementsinoverallequipmenteffectiveness(OEE)ofapproximately5%and

measurablyfasterbottleneckidentification.

Productioncoordinationandresponse

Scheduling,sequencing,anddisruptionresponsehavehistoricallyfallentoexperiencedproduction

supervisors.AIandMLsolutionsarenowtakingonmoreofthatcoordinationwork—automatingrouting,logisticsresponse,andproductionrecoveryinrealtime.

Inoneexample,aTier1supplierreducedlinestoppagesbyconnectingreal-timeproductiondatato

automatedresequencinglogic,eliminatingresponsedelaysthatpreviouslyrequiredsupervisorintervention.

Engineeringandenterprisefunctions

AI/MLapplicationsarealsoexpandingbeyondtheplantfloor.Engineeringteamsareusingsimulation

anddigitaltwintoolstoacceleratevehicledevelopment,reducephysicalprototypecycles,andevaluate

manufacturingprocesstradeoffsbeforetoolingiscut.Qualityteamsareusingfield-to-plantdatalinkagestoidentifywarrantyrootcausesearlierintheproductioncycle.Planningfunctionsareusingscenario

analysis,enabledbydigitaltwins,toimproveproductionsequencingandlogisticscoordinationundervariabledemandconditions.

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Whatischanginginpractice

TheoperationaleffectsofexpandedautomationandAI/MLareincreasinglyvisibleand,increasingly,

measurable.Thepatternacrossearlyapplicationsisconsistent:moreproductionstepsaregovernedbydefined,repeatablesystems;problemsaredetectedclosertothepointofoccurrence;maintenanceisshiftingfromreactivetocondition-based;anddecisionsarebecomingmoreconsistentacrossshifts

andplants.

Earlierissuedetection

Intraditionaloperations,qualityissuesoftensurfaceduringend-of-lineinspection,atthecustomer,orin

warrantydata.Eachofthosedetectionpointsisexpensive.Movingdetectionupstream,intotheproductionprocessitself,reducesthecostofcontainmentandthevolumeofaffectedvehiclesorcomponents.

AI-enabledvisionsystemsidentifysurfaceanomaliesduringpaintorbodyoperationsthatpreviously

requiredmanualinspection.Electronicsvalidationsystemscatchcalibrationandsoftwareissuesduringassemblyratherthanatend-of-line.Thepracticaleffectissmallercontainmentpopulations,fasterroot-causeidentification,andfewervehiclesaffectedbeforeaproblemisisolated.

Predictivemaintenancereplacingreactiveapproaches

Traditionalmaintenanceschedulesdon’taccountforhowequipmentisrunning.Condition-based

maintenance,supportedbycontinuousmonitoringandAI/MLpatternrecognition,replacesfixedscheduleswithreal-timeequipmentintelligence.

Earlydeploymentshaveshown40–60%reductionsinunplanneddowntime.Forhigh-utilization

equipmentlikestampingpressesandweldingrobots,thatreductiontranslatesdirectlytoproductioncostandthroughput.

Reducedvariationacrossshiftsandplants

Oneofthepersistentchallengesinmanufacturingismaintainingconsistentperformanceacrossshiftsandfacilities.Experiencedoperatorsandengineersdevelopjudgmentovertime,andthatjudgmentdoesnot

transferautomaticallytonewhiresordifferentplants.System-guidedprocessesreducethatdependencebyencodingbestpracticesintodefined,repeatableworkflows.

Manufacturersdeployingstandardizedmonitoringandautomatedprocessadjustmentreportmore

consistentqualitymetricsacrossshifts.Inonebodyshopapplication,real-timeanalyticsidentifieda

framing-linebottleneckthathadnotbeenisolated,producinga5…7%improvementincycletimeandafour-jobs-per-hourthroughputgain.Launchteamswithbettervisibilityintoprocessperformancecanidentify

andaddressvariationfasterduringramp-up,whenthecostofvariationishighest.

Operationalproductivity:TheAutolivexample

Autoliv,aleadingglobalproducerofsafetysystems,providesoneofthemoreconcretesupplier-level

examplesofwhatsustainedautomationinvestmentcandeliver,inCAR’sview.Thecompanyreporteddirectlaborproductivityimprovementsacceleratingfromapproximately4%in2023tomorethan8%in2024andover9%in2025.Managementhassinceraiseditsannualproductivityguidanceto8%,citingadditional

automationopportunitiesinlogisticsandoperations.

Autolivwasalreadyastrongproductivityperformerasanearly-adopterofautomation.BLSdataputthatperformanceinperspective:DurableGoodsManufacturingasawholeaveragedmodestornegativedirectlaborproductivitygrowthover2020–2024,rangingfrom0.8%in2021to-1.1%in2023,beforerecoveringto2.7%in2025.MotorVehiclePartsmanufacturers(NAICS3363)showedmorevolatility,swingingfrom

RockwellAutomation•SmartManufacturinginAutomotive:Deploymentandimpact|09

-2.6%in2020toapeakof5.9%in2022beforemoderatingto2.4%in2024(2025datanotyetavailable).

Againstthesebenchmarks,Autoliv’s8.1%in2024and9.2%in2025reflectthereturnsfromanearlyand

sustainedcommitmenttoautomation,nowextendedintoAI/MLanddigitalization.Thecompanyhasreachedaproductivitygrowthrateroughlythreetofourtimesthatofthebroaderdurablegoodssector,anadvantagethatcompoundsyearoveryearincoststructureandmanufacturingcompetitiveness.

Automotivesupplierproductivitycomparison

Directlaborproductivityimprovement(%)

—

—

—

—

—

—

Autoliv(CompanyEstimates)

DurableGoodsManufacturing(BLS)

MotorVehiclePartsMfg-NAICS3363(BLS)9.2%

8.1%

12%—

10%—

8%—

6%—

4%—

2%—

0%—

-2%—

-4%—

1.4%0.8%

0.1%0.2%

202020212022202320242025

5.9%

4.1%

-0.7%

-1.1%

2.7%2.4%

2.6%

2.2%

-0.5%

-2.6%

5.1%

Figure3.Automotivesupplierproductivitycomparison,2020–2025

Source:BLS/BEA;Autolivcompanyestimates;CARAnalysis)|*2025datanotyetavailableforNAICS3363

Whatthismeans:Strategicimplicationsforthe

AutomotiveIndustry

Aperformancedivideisemerging

Early-adoptersarereportingmeasurablegainsinquality,uptime,productivity,andlaunchexecution.

Companiesthathavenotyetmadecomparableinvestmentsarecompetingagainstthoseresultswithlegacysystemsandmanualprocesses.Thatgapwidenseachyear,andCARinterviewsindicateitisbeginningto

influencesourcingandcontractawarddecisions.

Unevenadoptionacrossthesupplybase

LargeTier1supplierswithglobaloperationsanddedicatedmanufacturingengineeringresourcesare

generallyfurtheralongindeployment.Mid-sizedandsmallersuppliersfaceamoredifficultpath,withfewerresourcesandlessinstitutionalbandwidth.ThegapbetweenwhatleadingOEMsexpectandwhatmany

supplierscancurrentlydeliverisobservableandgrowing.OEMsareincreasinglyweighingautomationcapabilityalongsidecost,qualityhistory,andcapacityinfutureprogramassessments.

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Risingexpectationsfromautomakers

Asautomakersextendsmartmanufacturingacrossproduction,engineering,andqualityoperations,theyareraisingexpectationsfortheirsupplybase.Greaterresponsiveness,moreconsistentquality,andbetterproductionvisibilityarebecomingbaselineexpectationsratherthandifferentiators.

Someautomakersareexplicitlycommunicatingautomationrequirementsinselectcommoditycategories,withdirectconsequencesinsourcingdecisions.

Onshoringwillbemoreautomatedthanbefore

Increaseddomesticproductioncommitments,drivenbytariffsandtradepolicy,supplychainresilience,andcustomerrequirements,arematerializingagainstpersistentlaborconstraintsacrosspartsofthesupply

base.Productionthatreturnswillnotlookliketheproductionthatleft:itwillbemoreautomated,moredata-intensive,andrequireadifferentworkforceskillset.

Futureoperationswillrelymoreheavilyonsystemoversightandprocessengineeringthanondirectlaborintraditionalassemblyroles.Manufacturersplanningthosemovesmustaccountforworkforcedevelopment,facilitydesign,andcapitalinvestmentaccordingly.

Whatcomesnext

Formanufacturersalreadywellalonginautomationdeployment,thenextphaseisaboutleveragingAI/ML

alongsideautomationratherthansimplydeployingnewsystems.Theemphasisisshiftingtowardcombiningthesetoolstogeneratemeasurablegainsinthroughput,productivity,andprocessconsistency.AI/MLis

increasinglyhowmanufacturersareunlockingthefullvalueoftheirautomationinvestments.

Throughputandproductivity

Manymanufacturershavedeployedmonitoring,analytics,andautomationsystemsthatarenotyetfully

integratedorfullyused.Closingthosegapsisthenextphaseofimprovement.Betterintegrationacross

quality,maintenance,andproductioncoordinationtoolscandeliverthroughputgainswithoutadditional

capitalinvestment.Inmanyoperations,thebottleneckisnothardwarecapacitybutinformationlatency:thetimebetweenwhenaproblemdevelopsandwhentherightpersoncanactonit.

Fasterdetectionandresponse

Shorterresponsecyclesaretheoperationalobjectivethattiestogetherquality,maintenance,and

productioncoordination.Asystemthatidentifiesadevelopingequipmentissuetenhoursbeforeitcausesafailureismorevaluablethanonethatidentifiesittenminutesbefore.Aqualitysystemthatcatchesa

processexcursionbeforeitaffectstenvehiclesismorevaluablethanonethatcatchesitatend-of-line.ThecompetitiveadvantageofAI/MLinmanufacturingisfundamentallyaboutspeed:reducingthetimebetweenproblemonsetandresolution.

Closeralignmentbetweendesign,manufacturing,andoperations

Thelonger-termdirectionismoreintegratedsystemsthatconnectengineering,manufacturing,andfieldoperationsinwaysthatcurrentarchitecturesdonotsupport.Fieldqualitydatainformingengineering

designinnearrealtime.Manufacturingprocessparameterslinkedtovehicleperformanceinthefield.Productionplanningsystemsthatincorporateactualmanufacturingcapabilityconstraintsratherthantheoreticalcapacity.

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Thisintegrationisnottrivialtoachieve–itrequiresdatainfrastructure,organizationalalignment,and

processdisciplinethatmostmanufacturersarestillbuilding.Butthecompaniesthatgettherefirstwill

haveastructuraladvantageinproductdevelopmentspeed,qualityperformance,andcostthatisdifficulttoreplicatequickly.

Thesuppliertransition

ForsuppliersthathavenotyetmadesignificantinvestmentsinautomationandAI/ML,thewindowfor

catchingupwithoutconsequencesisnarrowing.OEMexpectationsarerising,sourcingcriteriaareevolving,andtheoperationalgapbetweenleadingandlaggingsuppliersiswidening.Formostmid-sizedsuppliers,

therightstartingpointisnotacomprehensivedigitaltransformationprogrambutidentifyingtwoorthreehigh-impactapplicationswhereinvestmentcandelivermeasurableresultsquickly,andbuildingfromthere,accordingtoCARinterviews.

Keytakeaways

•Automotivestartsfromanalreadyindustry-leadingautomationlevels.ThecurrentshiftisaboutwhereandhowautomationandAI/MLareapplied,notnecessarilybuildingafoundationfromscratch.

•Theexpansionisintoareasthathavehistoricallybeenmanual,variable,oroperator-dependent:

electronicsassemblyandvalidation,in-linequalityprocesses,equipmentdiagnostics,andproductioncoordination.

•AI/MLisenablingandsupportingthisexpansion.Itsprimaryvalueisimprovinghowautomatedand

manualsystemsidentifyissues,supportmaintenancedecisions,improvequalityprocesses,andmanagecomplexityfrommixedpowertrainsandhigherelectronicscontent.

•Earlyresultsaremeasurable.Manufacturersreportedreductionsinunplanneddowntimeofupto50%inselectapplications,Overallequipmenteffectiveness(OEE)improvementsofapproximately5%,andthroughputgainsof5…7%fromreal-timeproductionanalytics.Autoliv’sproductivitya