人工智能如何为化工行业开辟全新可能

ChemicalsPractice

HowAIenables

newpossibilitiesinchemicals

Frommoleculeandmaterialsdiscoverytonewapplicationsandcustomeracquisition,AIcancreateopportunitiesacrossarangeofpromisingnewusecasesinchemicals.

ThisarticleisacollaborativeeffortbyLapoMori,MatejMacak,RSMallyaPerdur,andYashaswiGautam,withIanWells,SaumyaMisra,andZachGreen,representingviewsfromMcKinsey’sChemicalsPractice.

November2024

IthasbeennearlytwoyearssincethelaunchofChatGPTandothergenerativeAI(genAI)toolsthatrevolutionizedthewayAIisperceivedand

consumedbyindustries,businesses,andotherusers.Inthattime,McKinseyexpertsestimatethecapabilitiesunlockedbygenAIhavehelpedacceleratelevelsofhumanperformancebya

decade,onaverage.1

Noindustryremainsuntouchedbytheimpactof

genAI,butadoptionlevelsvarysignificantly.The

chemicalindustry,inparticular,remainsacautiousadopter.ArecentMcKinseyGlobalInstitute(MGI)surveyestimatesthatenergyandmaterials,whichincludeschemicals,hasthelowestexposureto

genAItoolsat14percent,comparedwiththe

cross-industryaverageof23percent.2Meanwhile,chemicalcompanieshavesignificantuntapped

potentialtoleapfrogcompetitorsusingtherecentadvancementsbroughtaboutbygenerative

technologies.

Thispotentialstemsfromtheindustry’sreliance

onscientificdataforinnovation,theavailability

of(oftenfragmented)customerdata,andthe

industry’snuancedandcomplicatedmanufacturingprocesses.3Simplystated,genAIaddsintelligence

andcompletenesstothesedata,whichcanthenbeusedtoinformdecisionmaking,speedupprocesses,andimproveoverallefficiency.Altogether,our

estimatesshowtheapplicationofgenAIacross

commercial,R&D,operations,andsupportfunctionsinenergyandmaterialscancreateanywherefrom

$80billionto$140billioninvalue.

Harnessingthisnewtechnologywon’tbeeasy.

Manyusecasescannotberealizedunlesssome

degreeofdigitalization,technicalcapability,and

scientificexpertiseisalreadyinplace.Thisarticle

providesanoverviewoftoday’smostrelevant

opportunityareasandoffersapathwayforchemicalindustryplayerstostarttakingconcreteactions.

ChemicalsandAI:Anoverview

Thechemicalindustryplaysacriticalroleinthe

globaleconomy,providingessentialmaterialsfor

mostotherindustries.Today,chemicalcompaniesfacemarketforcesthatrequirenewwaysof

thinking,includingthesearchfornewermaterialstosupportthefutureinnovationneedsoftheenergy

transition.4Otherforcesincludereinvigoration

ofgrowthwithbothnewandexistingcustomers,

efficienciesinmanufacturingandsupplychainto

fundgrowthandinnovation,andsignificanttalentandcapabilityattritionastheworkforceturnsover.

“GenAI”referstoapplicationsthatcanprocess

variedsetsofunstructureddata(suchaslabnotes,technicalspecificationsheets,scientificliterature,andsalespresentations)aswellasstructureddata(suchascustomerrelationshipmanagementand

transactionaldata)toaidsynthesis,suggestions,andnewcontentgeneration.Inthisway,genAI

generatesnewideasbyidentifyingpatternsin

datasets,particularlywhenitcomestocomplextaskssuchasfindingnewapplications,customeracquisition,andmoleculeormaterialsdiscovery.

Bycontrast,moretraditional,analyticalAItypicallysolvesspecifictasksbyanalyzingdataandmakingpredictionsbasedonstructureddatasetsand

predefinedrules.

AIandgenAIarenotcompetingtechnologies;

rather,theycancomplementeachother.The

combinationofthetwotechnologies,referredtohereas“genAI,”hasthepotentialtotransformnearlyallaspectsofthechemicalindustry,

revampingthewayscompaniesoperateand

potentiallyunlockingbillionsofdollarsinvalue.

Althoughcorepricingandforecastingmodels

willlikelycontinuetobebasedontraditionalAI,generativetechnologyallowsorganizationsto

preparedatafaster,tapintointernalandexternalsources,andsupportconversationalabilitiesthatcanleadtopricingorforecastinginsights.

1TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier,McKinsey,June14,2023.

2“ThestateofAIin2023:GenerativeAI’sbreakoutyear,”McKinsey,August1,2023.

3“Beyondthehype:NewopportunitiesforgenAIinenergyandmaterials,”McKinsey,February5,2024.

4Formoreonmaterialsandtheenergytransition,seeThenet-zeromaterialstransition:Implicationsforglobalsupplychains,McKinsey,July5,2023.

HowAIenablesnewpossibilitiesinchemicals2

HowAIenablesnewpossibilitiesinchemicals3

Chemicalcompanieshavesignificantuntappedpotentialtoleapfrog

competitorsusingtherecentadvancementsbroughtaboutbygenerativetechnologies.

Companieswilllikelyneedtohaveacombination

ofdigitalization,technicalcapabilities,and

scientificexpertisebeforetheycanharnessthe

potentialofAI,andnoteveryusecasewillapply

acrosstheboard.ThepotentialrisksofAImust

alsobeaccountedfor,includingtheaccuracyof

responses,securityfromcyberattacks,protectionofcompetitivelysensitivedata,biasedoutputs,andtheriskofintellectualpropertyinfringement.5

TheAIopportunityinchemicals

Innovationinthechemicalindustryhasbeenslow.Asapointofcomparison,Amazonspent$73

billionontechnologyandinfrastructure(amajor

componentofwhichwasR&D)in2022,6while

theentireUSchemicalindustryspent$13billion.7

Someofthisslownessisstructural,arisingfromtheindustry’sasset-heavynature,longerinnovation

cyclescomparedwithsoftwareorconsumergoods,enhancedregulatoryconsiderations,andless-

distributedcustomerbase(primarilyduetotheB2Bnatureofofferings).However,theindustryhasalsobeenalateadopterofnewtechnologiesandhas

subsequentlybeenslowtodeploythemtoderivebusinessvalue.

Inthechemicalindustry,genAIrepresentsa

substantialleapforward,makingthegenerationofinsightsandcreativeprocesses,suchasnew

molecularandmarketingdesigns,moreaccessibleandcustomizable.Thisdemocratizationof

technologycanhelpcompanies,especiallythosewithbelow-averageperformance,significantly

enhancetheiroperations.

Moreover,genAIisreshapingcompetitive

landscapesbyenablingnewwaystogenerate

hypothesesusingdiversedatasources,augment

individualcreativitywithsystematicsupport,

andembedtacitknowledgeintoinstitutional

advantages.Thisevolutionreducesentrybarriers

fornewplayers,whichcannowusecustomerdata

moreeffectivelyandoffertheirproductswithout

traditionalconstraints.Inaddition,customers

couldgaintheabilitytoeasilycompareandselect

suppliers,thankstoincreasedtransparency.TheseshiftssuggestthatfuturecompetitiveadvantagesinchemicalswillrelyheavilyonthestrategicuseofAI.

AlthoughgenAIwasinitiallyseenasaproductivitylever,overthepastyearsomeorganizationshave

uncovereditsgrowthpotentialaswell,particularly

innew-customeridentificationandaccount

management.Thefollowing11usecasescoverthreechemicaldomainsthatMcKinseyexpertsregardashavingthegreatestpotentialfornear-termimpact

(Exhibit1).

5Formoreontheserisks,see“Beyondthehype,”February5,2024.

6Annualreport,Amazon,2023.

7“ChemicalindustryresearchanddevelopmentspendingintheUnitedStatesfrom2014to2022,”Statista,May22,2024.

HowAIenablesnewpossibilitiesinchemicals4

Exhibit1

GenerativeAIcanhelpcreatevalueacrossallbusinessfunctionsinchemicals.

ImpactofgenerativeAIusecasesonfunctionsacrossbusinesses

Impactonrevenue1

Supportfunctions

Documentgeneration

Financialplanning

Riskidentification

EnhancingM&Aworklow

LowHigh

Commercial

Researchanddevelopment

Operations

Newapplications

Customeracquisition

Accountmanagement

Salesexecution(including

churnandpricing)

Salessupport

Newmoleculediscovery

Rapidandpreciseformulation

Augmentingknowledge

extraction

Operationalproductivity

Supplychainoptimization

Predictiveande代cient

maintenance

Focusofthisarticle

1Impactisbasedonapercentageofrevenueforeachstepinthevaluechain,usinghistoricaldatafromthechemicalindustry.

McKinsey&Company

Commercial

Thecommercialcycleinchemicalsrangesfromcustomeridentificationtothedevelopmentof

repeatpurchases(Exhibit2).GenAIhasvarioususecasesacrossthechemicalcommercial

cycle,includingnewapplications,new-customeracquisition,strategicaccountmanagement,

salesexecution(includingpricingandchurnmanagement),andsalessupport.

Newapplications

Potentialimpact:Reducetimerequiredtofindnewapplicationsfrommonthstodays

GenAIcanbeapowerfultoolfordiscoveringnewapplicationsforexistingchemicals.Byanalyzingvastamountsofexternalandunstructured

datafromvarioussources,includingpatents,

publications,andindustrynews,genAIcan

identifypotentialnewmarketsandapplications

notpreviouslyconsidered.Itcanalsosignificantlyreducethetimespentinapplicationidentificationanddevelopingbusinesscases.Moreover,genAI

canprioritizetheseopportunitiesbasedonpotentialmarketsizeandgrowth,helpingbusinessesfocus

theirR&Dandmarketingeffortsontheareaswiththemostpromise.Ideally,thisprocesswillnotonlyspeeduptheinnovationcyclebutalsoopennew

revenuestreamsbyextendingtheutilityofexistingproducts.

Customeracquisition

Potentialimpact:Increasetherateofnewcustomergrowthandsustainrevenueupliftof10to20percent

Itcanbechallengingtofindnewcustomersthatarelikelytobeusersofaparticularproduct.

Manycompaniestakeastructuredapproachto

identifyingnewcustomersonlyforthelargest

customers,takingareactiveapproachtotherest.

ButgenAIisreshapinghoworganizationsdiscoverandengagewithnewcustomersegments.By

deeplyanalyzingvastdatasourcessuchasweb

content,scientificliterature,companyreports,

andmarketupdates,genAIcanrevealnew

opportunitiesforgrowth.Doingsocanstreamline

Web2024

Exhibit2

MCK248195ChemIndustryGenAI

Exhibit2of4

HowAIenablesnewpossibilitiesinchemicals5

GenerativeAIcanenhanceperformanceacrossthecustomerlifecycleinchemicals.

Customerlifetimevalue

CustomervaluebeforeAI-poweredgrowth

Pricingmicro-segmentation

Contract

Cross-sell

optimization

Dynamicpricing

Lead

Upsell

Predictive

retention

conversion

Lead

Win-back

prioritization

Lead

identification

Acquisition

Shareofwallet

Pricing

Retention

Impactdrivers

Churnreductionandbetterservice

Top-linegrowthfromnewcustomer

Enhancedreturnonsalesofexistingcustomer

McKinsey&Company

theleadgenerationprocess,singlingoutthe

mostpromisingprospectsforengagementand

conversion.GenAIcanalsointegrateandlearn

fromhistoricaltransactionaldata,refiningcustomerprofilesforprecisiontargeting.Thiscanresultina

morestrategicdeploymentofmarketingandsalesresources,optimizingcustomeracquisitionand

fosteringsustainablebusinessgrowth.PrecisiontargetingviagenAIalsoofferstheabilityto

empowerinsidesalesandengagemoderate-tolow-volumecustomersappropriatelythroughtheconversionfunneland,ifneeded,handingthem

backtothesalesrepresentativewhentheyareonthecuspofmakingapurchasedecision.

Strategicaccountmanagement

Potentialimpact:20to30percentmoreincrementalopportunitiesinthedevelopmentpipeline

Manyorganizationsstruggletoturnkeyaccount

managementpracticesintorealgrowthdriversfor

thebusiness.Forinstance,theyoftendokeyaccount

managementinachecklist-likemannerbylooking

intohistorictrendstoidentifyfutureopportunitiesorbyreactivelyaddressingcustomerneeds.

GenAItoolscanhelpusersunderstandthebroaderbusinesscontextandstrategyofacustomer

bytappingintopublicreports,whichcanthen

betranslatedintothelikelyimplicationsforend

markets.Theseinsightscanthenbeconverted

intoopportunitiesforspecificproducts.Insimpler

applications,genAIcanlookintocustomersegmentinformationinternallyaswellastheopportunities

beingpursuedbycommercialteamsgloballytosuggestnewopportunitiesaparticularaccountmanagermaynotbeconsidering.

Forexample,alargechemicalcompanyrecentlylookedintotheopportunitiesthatitsproduct’s

uniquepropertiescouldhaveinaparticularend-marketapplication.UsinggenAI,thiscompanywasabletounderstandnotonlythestructure

oftheapplicationmarketbutalsotheconcrete

HowAIenablesnewpossibilitiesinchemicals6

opportunitiesitcouldpursuewithitscustomers.

Asaresult,anadditional,previouslyunexploredopportunitywascreatedinthepipelinefor

commercialteams.

Salesexecution(includingpricingandchurnmanagement)

Potentialimpact:Optimizepricinginrealtimeto

secure2to5percentreturnonsalesandreducerevenuechurnby10to20percent,comparedwithhistoricratesofattrition

Advancedpricingrequiresaccesstolargeamountsofdata—forexample,paymentterms,customer

visitreports,customerexpansionplans,and

buyingpatterns.However,thesedataareoften

fragmentedandofvaryingquality,makingthem

difficulttoanalyzeanduseeffectively.Togainan

edge,companiescanusegenAItoanalyzemore

thanadozenpricinginputs(includingmarketdata,competitorprices,buyingpatternsoverthepast

tenyears,andconsumerbehavior)andsetdynamicpricingstrategies.Advancedpricingisenabled

throughthefollowingthreelevers:

—Comprehensivecustomeranalytics.Analyzingvastamountsofdataandidentifyingpatternsandtrendsinconsumerbehaviorallow

companiestogaininsightsintocustomerpreferences,buyingbehavior,andpricesensitivity.

—Real-timepricingadjustments.Dynamicpricingallowscompaniestorespondquicklytomarketfluctuationsandoptimizetheirpricingstrategiesaccordingly.Forexample,ifthereisasudden

surgeforacertainpolyethylenegrade,AIcanautomaticallyraisepricestomaximizeprofits.

—Unlockinterdependencies.Analyzingtheimpactofslightpriceadjustmentsacrossanentire

categorycanprovideamorecomprehensive

view—forexample,showinghowachangeinthepricingofoneproductcanaffectsalesofotherproductsintheportfolio.

GenAIcanalsobeusedtomonitorriskforchurn

andcustomerretentionbyanalyzingavarietyof

datasources,suchasfinancialreports,market

analytics,andtransactionaldata.Itcanspotmarket

andcustomertrends,turningthemintoearly

indicatorsofcustomerdisengagementandenablingbusinessestoproactivelyaddresschurnrisk.As

anexample,traditionalAI-basedpredictivechurnmodelscanbeenrichedwithexternaldata,suchassocialmediacommentaryorearningsestimates.

Byevaluatingtheseinsights,companiescanmakebetterdecisionstopreventcustomerloss.Finally,

genAIcanspeeduptheresponsetimetothese

risks,allowingforquickandtargetedcustomer

retentionstrategies,includingpersonalizedoffers

andpromotionsaddressingidentifiedchurndrivers.

Salessupport

Potentialimpact:Upto20percentofsellercapacityredeployedforgrowth

GenAIcanpropelsalesteamsforwardbyconsolidatingcomplexcustomerdataintoasingle,actionableview,

enablingrichercustomerunderstandingandbetter

executionfocus.Itcanalsohelpstandardizesalesforcecapabilitiesacrossregions—forinstance,the

salesforceclosetoheadquarterscanmoreeasilybringtheremotesalesforceuptospeedoncurrentandnewproducts.Thisunifiedsystemoffersacomprehensiveunderstandingofcustomerneeds,drivingpersonalizedengagementandtailoredsalesapproaches.In

addition,real-timeassistancefromgenAIchatbotscanprovideimmediateaccesstocrucialinformation—fromcompetitoranalysistomultilingualsupport—helping

sellersadaptandrespondinthemoment.Theresultisastreamlinedsalesprocessthatnotonlyfreesupmoretimetosellbutalsoenhancestheoverallsellingexperience.

Asanexample,aNorthAmericanchemicalcompanyleveragesgenAItoaddresschallengesinexecutingcommercialexcellencelevers.Companyleaders

identifiedalackofunderstandingofexpected

commercialactionsamongsalesteammembers,

whichcausedsignificantvalueleakage.UsinggenAI,thecompanyisintegratingaconversationinterface

intoitscustomerrelationshipmanagementsystem,whichofferstargetedcommercial-actiontriggersforsalesrepsandenablesrepstoqueryandunderstandtherationaleforcommercialactions.Althoughmanyhighperformersinsalesorganizationsareskilledatpursuingsomesalesaugmentationpracticesintheirownintuitiveways,genAIallowsorganizationsto

significantlyliftlowperformers.

HowAIenablesnewpossibilitiesinchemicals7

Researchanddevelopment

Today,R&Dprocessesinchemicalstakealong

time.Thediscoveryofnewmoleculesormaterialscantakeseveralyearsandrequiretensofmillionsofdollarsinresearchfundingfromconceptionto

at-scaledeployment.However,genAItoolspromisetoaugmenttheinnovationcyclethroughfaster

discovery,netnewmoleculeormaterialidentification,andrapidandpreciseformulations,aswellasefficientleveragingofinternalandexternalR&Dknowledgetoaugmentthemetabolicrateofinnovation.

FoundationmodelsthatsitatthecoreofR&D

accelerationaremoredataefficientandcanbe

powerfullydeployedinclosed-loopresearchsystems(CLRS),optimizingresearchinnovation.Asaresult,

thenumberofR&DiterationsandtherequisitedatacanbedrasticallyreducedcomparedwithtraditionalAI(insomecasesby90to99percent).

Withallofthisinmind,emerginggenerativeAI

approacheshavedemonstratedvalueforthree

areasoffocus:newmoleculeormaterialdiscovery,rapidandpreciseformulation,andaugmented

knowledgeextraction.

Newmoleculeormaterialdiscovery

Potentialimpact:Two-tothreefoldaccelerationinmaterialsormoleculediscoverywithnetnew

patentablechemistriesdiscoveredandoptimizedforend-stateproductproperties

Somediscoverychallengesacrosschemicalsrequiresignificantamountsoftime.Forinstance,new

formulationscanhelpoptimizecostsorperformance,newmoleculescanbemoresustainable(suchas

thosefreeofper-andpolyfluoroalkylsubstances,orPFAs),andtherelevantpropertiesofmaterialscanbeimproved(suchasthermalandhydrothermalstabilityforcatalystsinpetrochemicals).

Forexample,achemicalcompanyinNorthAmerica

recentlylookedintothepossibilityofformulatinga

newcoatingtoaddresschallengesitfacedinoneof

itsend-marketapplication.UsinggenAI,thecompanyminedtheuniverseofmaterialsusingexternaldata

aswellasitsownproprietaryR&Ddataandultimatelyidentifiedthemoleculesthatcouldofferthedesired

functionality.Thisallowedthecompanytomovefromaslowandexpensiveuser-customizationcycletoarapidcustomizationcycleatafractionofthecost.

GenAImodelscanalsodiveintohugeamountsof

publicdata(suchaspatents,publications,andgrants)andsuggestchemistriesorformulationstobetestedinthelaborend-stateprocesses,requiringsignificantlylessdatathantraditionalAIfortrainingorfine-tuning.

Theresultsfromlabexperimentsorscale-uptestingarethenfedbackintothemodelstofurtherimprovetheproperties,resultinginadiscoveryflywheel.

GoogleDeepMind,forexample,hasalreadypredictedstructuresfor2.2millionnewmaterials,ofwhichmorethan700havebeencreatedinthelabandarenow

beingtested.8

8EkinDogusCubukandAmilMerchant,“Millionsofnewmaterialsdiscoveredwithdeeplearning,”GoogleDeepMind,November29,2023.

GenAItoolspromisetoaugment

theinnovationcyclethroughfasterdiscovery,netnewmoleculeor

materialidentification,andrapidandpreciseformulations....

HowAIenablesnewpossibilitiesinchemicals8

Rapidandpreciseformulation

Potentialimpact:Morethan30percent

accelerationinachievingthedesiredformulationandapproximately5percentsavingsoncost

Achievingthespecificationsrequiredbycustomersortargetfunctionalityprofilesrequiresmultipleiterationsaswellastheexpertiseoftheindividualformulator.

Insomecases,formulationsareoverspecified,whichcanaddtothecost.Withthisinmind,thereare

typicallytwoleversofvaluecreation:1)optimizing

formulationspecificationstobe“goodenough”fora

givenapplication(minimizingoverspecification),and2)recommendingthelowest-costformulation(basedontheidentityandratioofvariousrawmaterials)tomeettherequiredspecifications.

Inadditiontooptimizingraw-materialcosts,thereareopportunitiestooptimizeproductionprocesses(suchastemperatureandprocessingtime)andtherefore

reduceenergycosts.Achievingthis,however,entailsfoundationmodelsandlargelanguagemodels

(LLMs)todiscoverrelevantchemistriesfromexistingdatabases,patents,literature,andothersources.

Doingsocanhelpaidthediscoveryofchemicals

usedtooptimizepropertiesorassubstitutesintheformulationdesigntoimprovecost.

Augmentedknowledgeextraction

Potentialimpact:Morethan30percentincreaseininitialmanualassessmentsofliterature

Inchemicals,scientistsoftenspendconsiderable

timeextractingandsummarizinginformationfrom

diversesourcessuchaspatentsandscientific

literaturetobetterunderstandmolecularormaterial

propertiesandreactionmechanisms.Thisprocessis

labor-intensiveandpronetoincompleteorinaccurateinformationduetothevastamountanddiversityof

datainvolved.Bycontrast,genAIcangreatlyenhancetheextractionofscientificknowledgethroughefficientdatamanagement(Exhibit3).Inaddition,integrated

proprietarydatacanbesearchedandqueriedinasimpletext-anddialogue-basedinterface.

Exhibit3

GenerativeAIcanrapidlysynthesizeandscoreevidenceacrossabroadscopeofscientificdatasources.

GenerativeAI–basedretrieval

ValidationResults

Preparation

Problemdefinition

Understandthecore

analysisoutputsrequired

Extraction

Specificdataweregathered

Literature

(eg,PubMed,ArXivs)

Synthesis

Relevantdata,sources,andquotescombinedtosynthesizeevidence

Sourcedefinition

Selectmostvaluablesources

Patents

Databases

(eg,PubChem)

Classification

Outputsqualitativelyandquantitatively

classified

Prompting

Delineationofqueryanddesiredoutput

Other

(eg,websitesandnews)

Qualitycontrol

Scoringvalidatedwithdataanalysiscrossedwith

knowledge1andexpert

interviews

R&Dapplications:R&Dportfolio

strategy

1Basedonavailability,validity,andstrengthofscientificknowledgeidentified.

McKinsey&Company

Businessdevelopmentscanandassessment

IPstrategy

HowAIenablesnewpossibilitiesinchemicals9

Asanexample,aEuropeanchemicalplayerdeployedgenAItoautomaticallycollectdatafromconnectedlabinstrumentsandallowscientiststorecord

notesfromanywhereinthelab.Inturn,researchersgainedaccesstothescientificprotocols,andany

aberrationswereimmediatelyhighlighted.

GenAIinR&Dcanalsofacilitatecommunicationandcollaborationamongscientists,helpingthemsharebestpracticesandlessonslearned.Thisessentiallyprovidesreal-timefeedbackandsuggestions,

helpingscientistsadapttonewtechnologymore

quicklyandeffectively.Italsoallowsscientiststo

bettertrackprogressandmeasuretheimpactof

thenewtechnology,providingvaluableinsightsforcontinuousimprovement.

Operations

Chemicalmanufacturingprocessesproducevastamountsofstructuredandunstructureddatathat

haveuntilnowbeentoounwieldyfortraditional

analytics.WithAI-poweredmodels,however,thesedatacanbeeasilyprocessedtoimprovepredictiveandefficientmaintenance,operationalproductivityandthroughput,andsupplychainoptimization.

Predictiveandefficientmaintenance

Potentialimpact:30to40percentincreaseinmaintenancelaborproductivity

Onecrucialaspectofmaintenanceinchemicals

isunderstandingequipmentfailuremodesand

developingmitigationstrategiesthatbalancecostandrisk.Somecompaniesareleveraginggen

AItoinstantaneouslycreateorenhancefailure

modesandeffectsanalysis(FMEA)bycombiningtheirinternalmaintenancehistorieswithpubliclyavailabledata(Exhibit4).

Inaddition,recentadvancesinAIimagerecognitionandprocessingcanhelpoperatorsidentify

Exhibit4

GenerativeAIcanrapidlyprocessreliabilitydatatoidentifyrootcauses,lagbadactors,andevolvepreventive-maintenanceprograms.

1.GenerateFMEA

Createafailuremodesand

efectsanalysis(FMEA)fromanassetdescription

2.Analyzeworkorders

Evaluateworkorders(withequipmentdescriptionandmaintenance

activities)andoptionallyFMEAtoderivefailuremodelabels

Reliability

copilotprocess

<

3.Acceleratereliabilityanalysis

CombineFMEA,work

orderfailuremodes,and

maintenancehistorytoidentifybadactorsandassess

preventive-maintenance(PM)efectiveness

4.OptimizemaintenancestrategyReviewbadactorslistandPM

efectivenessandevolve

maintenancestrategywithcopilot

suggestions

Source:QuantumBlack,AIbyMcKinsey

McKinsey&Company

HowAIenablesnewpossibilitiesinchemicals10

anomaliesandpredictfailuresinthefield.In

turn,thisenablespreventivemaintenanceand

improvesequipmentuptime.AIcanalsogenerate

maintenanceplansbyingestingOEMmanuals,

preexistingjobplans,maintenanceassessments,

andpartscatalogs.Asanexample,onechemical

companycreateda“maintenancevirtualexpert”forprocessingequipmentmaintenance,whichanswersthequestionsoftechnicians,diagnosesissues,

andcreatesproceduresandworkinstructionsthatlinktotheworkordersystem,massivelyimprovingtechnicianproductivityandmanufacturing

equipmentuptime.

Operationalproductivityandthroughput

Potentialimpact:Morethan10percentincreaseinyieldandthroughput

Real-timeprocessoptimizershavebeendeployedtoprocesssensordatainnear-realtimeandhelp

controlroomoperatorsmakedecisionstomaximizeyield,energy,andthroughput.Bylayeringgen

AI,theseoptimizerscanquicklyandaccurately

answertechnicianqueriesandexplainmodel

recommendations,increasingoperatorconfidenceandadoption.AnotherusefulgenAIapplication

inoperationsiscont