人工智能和区块链

accen＞ture

ARTIFICIALINTELLIGENCEANDBLOCKCHAIN

InsightsandActionsfortheChemicalIndustry

CONTENTS

FOREWORD 3

INTRODUCTION 4

DISRUPTIONOFCHEMICALBUSINESSMODELSTHROUGH 7

ARTIFICIALINTELLIGENCEANDBLOCKCHAINTECHNOLOGY

ARTIFICIALINTELLIGENCE 11

ArtificialIntelligenceasDisruptor 12

VisionfortheIndustry 13

RealityCheck:ArtificialIntelligenceintheIndustryToday 14

ValuePotential 17

ChallengesandImplications 18

BLOCKCHAIN 21

BlockchainasDisruptor 22

VisionfortheIndustry 24

RealityCheck:BlockchainintheIndustryToday 26

ValuePotential 27

ChallengesandImplications 29

CONCLUSION 30

APPENDIX 32

ArtificialIntelligence 32

Blockchain 39

ABOUTTHERESEARCH 42

ABOUTTHEAUTHORS 43

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FOREWORD

DearReader,

Newtechnologiesareconstantlyonthehorizon,includingemergingtechnologiessuchasartificialintelligence(AI)andblockchaintechnology.Digitalizationwillbeasourceanddriveroftransformationalchangeacrossallindustries.Thechemical

industrystrivestobeafrontrunnerinmanagingdigitaltransformation.WethereforewelcomeAccenture’sstudyonAIandblockchaintechnologiesandthepotentialimpact

onthechemicalindustry.

Thepurposeofthestudyistosparkideasonhowthechemicalindustrycanreapbenefitsfromapplyingbothtechnologiesintheirbusinessmodels.AIcanboosttheindustrytowardahigherlevelofefficiency.Newdataanalyticsincludingartificialintelligence,machinelearning,anddeeplearningareexpectedtoimpactallbusinessprocessesastheywillefficientlyextractthevaluefromdata.

WhereAIwillreinventthenatureofourwork,blockchainwillimpacthowweinteractacrossthevaluechain.Throughblockchain,chemicalcompaniescanincreasethetransparency

intohowproductsareprocessedineachstepdownthevaluechain—allowingcompaniestotailorproductiontocustomersandprovingtheauthenticityandcircularityoftheirproducts.

Combined,bothtechnologiespresentmajoropportunitiesforourchemicalindustrytocontributetolong-termEuropeanpolicygoals,suchassubstantiallyincreasingresource

orenergyefficiencyandthusreducingcarbonandotherenvironmentalfootprints.

Valuechainsasweknowthemwillchangecompletely:productsandrelatedprocessesbecomemorepersonalized,creatinganddeliveringhighervalueforcustomersthroughtheempowermentoflocal,morespecializedvaluechains.Thechemicalindustrywilldevelopnewdata-basedbusinessmodels.

Simultaneously,productswillincreasinglyincorporatedigitalservicecomponents,enabledbytheabundanceandgrowingflowofdata,tostrengthenthecompetitivenessoftheentireoffer.

Thisstudyprovokesnewreactionstonewtechnologiesbygivingvaluable

recommendationstoourchemicalindustry.Let’sgetreadytocreatea(block)chainreaction.

MarcoMensinkDirectorGeneralCefic

INTRODUCTION

Onthesurface,today’schemicalindustrylookssolid,withpeakcashandstableprofits.Althoughsomesegmentshaveperformedbetterthanothers,ingeneral,timeshavebeengood.

Theindustry’sofferingscontinuetoenablebusinessesaroundtheworldtooptimizetheirperformance,createnewcustomerofferingsanddrivegrowthandprofitability.

Thatsaid,theindustryfaceschallengesonmanyfronts—fromshiftsinfeedstockcoststochangesincustomerneedsandcompetitivethreatsfromnewentrants.Thesechallengesareespeciallysignificantinareassuchasthecustomerinterface,

safeandsustainableoperations,integrating

newtechnologies,buildingtheworkforcetoaddressdemographicchangeandcontendingwithever-increasingregulatoryrequirements.

Chemicalcompanieshavelongreliedonadvancingtechnologytoaddresschallenges,fromtherobotsandmicroprocessorsofthe1960sthroughthesensors,bigdatatoolsandcloudplatformsofthelastdecade.(Figure1)

Today,theindustry’sattentionisturningtotwoemergingtechnologies:artificial

intelligence(AI)andblockchain.Bothhavethepotentialtohelpchemicalcompaniesachievestep-changeimprovementsinperformanceandvalue.(Figure2)Thesetechnologiesarelikelytohaveanimpactonthevaluechainsandprofitpoolswithinthosechains.Buttheycouldalsohavefar-reachingimplicationsforthestructureoftheentireindustryandhow

itoperates.

Figure1:Technologyevolutionfrommechanicaltodigitalinprocessindustries

ProcessIndustry

Electronic

Industrialrobots

Microprocessors

Processcontrolsystems

Enterpriseresourceplanning(ERP)

Controlloopoptimization

Manufacturingexecutionsystems

ERPsystemintegration

MachinelearningandproductionAI

Machine-to-machinecommunication

Autonomousrobots

Manualcontrol

transmissionto50MIA

Singleprocesscontrols

Sensor-basedminicomputing

Advancedprocesscontrols

ERPinterfaces

Smartsensors,RFID,mobile&tracking

Predictivemodelling

Advanced/bigdataanalytics

1930 1940 1950 1960 1970 1980 1990 2000 2010

Manualcontrol

Magnetictapesfordatastorage

Typewriters

Microcomputer/personalcomputers

Floppydiskdatastorage

HTML

Internet

Wifi

Cloud/dataplatforms

Drones

Speechrecognition

Military/government/organizationscomputersystems

OverallIndustry

Materialrequirementsplanning

Mobileinternet/apps

Smartphones

Tablets

Socialnetworks

Smartwatches

3Dprinting

Advancedrobotics

Blockchain

DrawingonanAccenturesurveyof200chemicalindustryexecutivesfromaroundtheglobe,thisreportexplorestherapidlyevolvingapplicationofthesetechnologiesintheindustry.1Itexaminestheeconomicbenefitsthattheyoffer,andtheirbroaderimpactonthewaycompaniesdobusiness.

Thepicturethatemergesisoneofdisruptionandshiftingcompetitiveadvantage—andfundamentalchangethatiscomingsoon.

Industryexecutivesneedtotakestepstounderstandthesetechnologiesand

theirpotentialimpact,anddevelopplansforincorporatingthemintotheirbusinessandtechnologystrategies.Thisreportisdesignedtoprovideinsightsthatcanguidethoseactionsandhelpchemicalcompanies

moveaheadtotakeadvantageofAIandblockchain—andavoidbeingleftbehindinthisnextstageofthedigitalrevolution.

Figure2:KeychallengesinthechemicalindustryandpotentialapplicationsofAIandblockchain

KeyChallenges ArtificialIntelligence Blockchain

Workersafety

Fewerinjuriesinproductionthrough –

earlywarningsystemsandgenerallyfewerworkersonsiteoverall

Demographicchange

Dynamicknowledgedatabases –

toensureknowledgeretentionfromanagingworkforce

Newmarketentrants/competitors

Advancedinsightoncompetitiveactivitiesforeffectiveshieldingstrategies

Networkstructurescreatinganincentivetodobusinesswithinanestablishednetwork

Sustainability/

resourceefficiency

Rawmaterialorenergyanalyticsforefficiencyincreaseinproductionprocesses

Lifecyclemanagementandtraceabilityfromendusebacktorawmaterialorigin

Changingcustomerpreferences

Sensingofmarketdevelopmentsandcustomerdemandpattern

Demandinsightthroughvaluechaintransparency,enablingmoreeffectiveproductandservicedevelopment

Regulatorycompliance

Intelligentdocumentstructuring,build-upofknowledgedatabasesandsystematicdocumentanalysis

Fraudresistant,tamper-evidenttransactionsthroughnetworkvalidations(e.g.,instantdataqualityforREACH,productcertificates,regulatoryreporting)

Newofferingstoenhancegrowth

Patternrecognitioninvastamounts

ofdata(e.g.,touncovernewformulationsorapplicationsforexistingproducts)

Platformforproductco-creationfromdifferentplayersacrossthevaluechain

Tracking

valueflows

– Transparencyonmaterialtransfers,financeflowandinventorylevelsacrosssupplychain(e.g.,improvedplanningandexecution,nearreal-timetrackingofdeliverystatus)

Errorsinroutinetasks

Augmentationofhumandecisionmaking(e.g.,toincreasefirst-timerightinproduction)

Eliminationofmiddlemen,

systembreaksandhumaninterventioninrepetitive/transactionaltasks

Manualeffort

Automationinproduction,R&Dandadministration(e.g.,chatbotsincustomercarecenters)

Automationandeliminationoftransactionsthroughnearreal-timevalidations(e.g.,verificationsofinvoices,shipments,taxes)

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DISRUPTIONOFCHEMICALBUSINESSMODELSTHROUGHARTIFICIALINTELLIGENCEANDBLOCKCHAINTECHNOLOGY

Chemicalcompanies’businessmodelsareconstructedaroundacommonsetofapproximately40buildingblocks.

Thesesupportseveralorganizationaldimensions,whichtypicallyincludeproductandserviceofferings,customerinteractions,themanagementofassetsandthesupplychainandorganizationalenablers.(Figure3)

Figure3:AIandblockchainhavethepotentialtodisruptbusinessmodelssubstantially

Dimension

Buildingblocks

Configurationoptions

OFFERING

Customerneedaddressed

Competitiveprice

Securedsupply

Highquality

Choiceofconfigurations

Takeoverofvaluecreation

Product/processinnovation

Productoffering

Catalogproduct

Productwithconfigurationoptions

Productformulation

Serviceoffering

None

Logisticsservices

Technicalservices

Co-development

Registration/ESHQservice

CUSTOMERINTERACTION

Pricing

Market-driven/indexpricing

Cost-plus

Value-based

Sales

Fullyautomatedinsidesales

E-commerce

Bypasseddistributors

Nosalesrepsinfield

Highlyautomatedkeyaccountmgmt

Technicalservice

None

Remotelyassistedandaugmentedfieldsupport

Solutionco-development

Labservice

R&D,ASSETSANDSUPPLYCHAINMANAGEMENT

Researchanddevelopment

None

Simulationofmoleculemodification/synthesis

Highlyautomatedlabs

Chemicalprocessoptimization/engineering

Production

Continuous

Campaign

Batch

Discrete

Orderfulfillment

Make-to-stock

Make-to-forecast

Make-to-order

Make-to-project

Logistics&inventory

Directtocustomer

Singleechelon

(Single-tierwarehouse)

Multi-echelon

(Multi-tierwarehouse)

…

Maintenance

Predictivemaintenance

Condition-orientedmaintenance

Period-basedmaintenance

Run-to-failuremaintenance

ENABLERS

Procurement

Spotpurchase

Contractagreements

Finance

Nodifferentiatingcharacteristics

HR

Nodifferentiatingcharacteristics

AccenturebelievesthateachofthebuildingblockswillexperiencedisruptionfromwidespreadadoptionofAIand/orblockchaintechnology.Adoptionofthesetechnologieswillfundamentallyalterhowchemicalcompaniesperformworkandinteractwithcustomers.(Figure4)Inthenearfuture,businessmodelswillfocusontechnology-basedofferings,

AI-enabledcustomerinteractions,data-drivenmanagementofassetsandthesupplychain,andback-officeprocessesthatareautomatedasmuchaspossible.

Forexample,today’scost-plusortarget-pricecostcalculationswillbereplacedbycontextualpricingbased

onforesightandmarketsimulationsdrivenbyAI.Primarycustomerinteractionswillbebasedonvirtualsupport,reducingtheneedforcustomerservicecentersandenablingcompaniestooffercustomershighlyrelevantproductsandservices—andtodosomore

frequently.Chemicalcompanieswillbebetterabletopredictprocessresultsbasedonmoredetailedspecificationsofinputmaterials,

ortobringtheirresearchanddevelopment(R&D)tonewlevelsthroughAI-basedresultexplorations.Logisticswillberevolutionizedthroughtamper-evident,nearreal-timetrackingandfreightsettlementfunctionalities.AndpredictivemaintenancewillreachnewheightsthroughtheprocessingoflargevolumesofdataandAI-basedsimulations.

Withthesetechnologies,commodityandspecialtychemicalcompaniesalikewillexperiencedisruptivechangeintheirbusinessmodels.Theywillhaveunprecedentedvisibilityintotheendusesoftheirproductsandwillbeabletooperateplantswithdrasticallyreducedlevelsoflost-timeincidents.Theywillalsobeabletooffer24/7serviceatzeroincrementalcostandtoextractinsightandvaluefromthemassiveamountsofdatabeinggeneratedcontinuously.Atthesametime,humanemployeeswillbeabletofocusonmorecreativetasksthroughouttheorganization—allowingformoreinnovationanddrivingadditionalgrowth.

Figure4:KeydisruptionsforchemicalbusinessmodelelementsthroughAIandblockchain

Reductioninchurnratethroughmorescalablecustomerinteraction,morefrequentinteraction,greaterresponsiveness

Learning-basedofferingdesign:moretailored,differentiatedoffers,automatedofferingtestingandcampaigning

Greaterinsightintocustomerneedsthroughblockchain-drivenapplicationtransparency

Greaterscalabilityofservices

Morehighlycustomizedserviceofferingsforeachcustomer

Vendor-managedinventories,logistics,recyclinganddisposal

Performance-basedoptimizationofcircularservices/businessmodels—e.g.,chemicalleasing

OFFERING

Customer

needaddressed

Productoffering

Serviceoffering

AI-poweredanalysisofwillingnesstopay,priceelasticity,switchingthresholds,etc.

Reducedpriceleakage

Improvedpricesynchronizationvs.rawmaterialcostandvs.playersoutsidenetwork

AI-basedcustomer-specificdemandsensing

Automatedcustomerinquiries(virtualassistants)

Automatedtransactionaltasksandsimpleinteractions

Deeperinsightthroughminingofoverallecosystem

Wearabledevicesforfieldsupportwithimagerecognition

Improvedunderstandingofendcustomerneeds

CUSTOMER

INTERACTION

Pricing

Sales

Technicalservice

Dimension CharacteristicsExpectedimpact

R&D,ASSETSANDSUPPLYCHAINMANAGEMENT

R&D

Production

Order

fulfillment

Logistics&inventory

Maintenance

Dimension CharacteristicsExpectedimpact

Data-drivenR&D,e.g.,cross-BUexperimentation,leverageofhistoricdata,newapplicationsforexistingproducts,directcustomerinsights

Insilicoexperimentation

Advancedlabautomation

ImprovedyieldandthroughputthroughAI-basedoptimizationofproduction

Reducedoff-spec,changeovers,slow-downsanddowntimewithAI-baseddemandplanningandproductionscheduling

Reducedmanualmaterialhandlingthroughintelligentautomation

Reducedenergyconsumptionthroughmachinelearning-basedoptimization,improvedspotvs.contractingmixforenergysourcing

Reducedeffortfordecisionmaking—e.g.,AI-assistedcontrolroomoperations

Automationofadministrativetasks

Machinelearning-poweredpredictivemodelsmakingmake-to-stockobsolete

Improveddemandvisibilityandsupplycapability

Track&trace,automatedqualitycontrol,assuredprovenance

Reducedtransactioncost(logistics,customs,quality,certificatehandling,moreautomation,lesspaper)

Improvednetworkandinventoryoptimization

Pre-consolidationsoftransports

Reducedwarehousingdemand

Optimizedloading/unloadingslotmanagement

Near-realtimerouteoptimization

Connectedplantsand3-Dmodeling(digitaltwin)

Real-time,remotemonitoringofequipmentperformanceandcondition

Causalanalyticsandadvanceddatavisualization

Higherassetavailabilityduetoreducedunplanneddowntime

Maximizedhands-on-tooltimethroughAI-poweredworkplanningandscheduling

Reducedspare-partinventoryandimprovedsparepartavailability

Reducedunplannedeffort,improvedpredictionofhighprioritynotifications

LowerrawmaterialpricesthroughhigherforecastaccuracyandAI-poweredmarketintelligenceandimprovedhedgingofrawmaterialprices

Optimizedthird-partyspendthroughbetterschedulingandoptimizedprocurement

Reducedinvoiceverificationthroughautomatedsettlement

Reducedforeignexchangeriskifchemicaltokenimplemented

Reducedemployeedatamaintenance

Individualized,adaptivelearningtools

ENABLER

Procurement

Finance

HR

Asaresultofallthetechnologicaldisruption,thetypicalsequenceofactivitiesfromsensingcustomerdemandtofulfillinganorderwillsoonlookverydifferentfromtoday.(Figure5)

Thedisruptivepotentialassociatedwiththesetechnologiesishuge,andchemicalcompaniesneedtorethinknowhowtheirorganizationswilloperateinthenearfuture.

Figure5:Disruptivechangeincustomerdemand-to-fulfillmentcycle

AI-basednextbestaction

forhumancustomerinteraction

Blockchain-baseddemanddetection

Automated,blockchain-based

settlement

Campaign/offeringdesigned

bymarketingteam

FULFILMENT CUSTOMER

LogisticalplansassistedbyAI,

materialhandlingbyrobots

Virtualassistant-executed

positioningtocustomers

AI-basedsynthesisand

productioninautonomousplant

DIGITALPLANT

VIRTUALASSISTANT

Contextualquotedeterminedby

AI-poweredpricingengine

Virtualassistant-basedcustomerservice

Virtualassistant-basedordertaking,

blockchain-basedcontracting

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ARTIFICIALINTELLIGENCE

Today,AIisalreadyapartofdailylife.Forexample,itisnotatallunusualfor

someonetotakeUbertotheairport,usingthetimeinthecartocatchuponpresortede-mailanddosomeonlineshopping—arrivingafteraproductiveandseamlessexperience.

Inthenot-too-distantpast,thatexperiencewouldnothavebeenpossible.Today,itis,becauseitislargelybasedonAItechnologies.AIhelpsthepersonandtheUberdrivertoconnect,navigatearoundtrafficjamsandfindthebestroute.Itprioritizese-mailandscreensoutfraudulentmessages,providesshoppingrecommendations,andevenoffersaselectionofmusictailoredtotherider’stastes.And

AI-enabledautonomousvehiclesareonthevergeofbeingapprovedforwidespreaduseonpublicroads—whichmeansthat

thecarsoonmayhavenodriver.

AIisagroupoftechnologiesthatincludesmachinelearning,computervisionandspeechrecognition,anditisatthecoreoftoolssuchaspredictiveanalytics,advancedprocessautomation,roboticsandmechanicalautomation,amongmanyothers.AIhasquicklybecomerelevantinprivatelifeandbusiness-to-consumer(B2C)contexts,anditwillsoonhaveatremendousimpactonthechemicalindustry.

AIhasthepotentialtodisruptbusinessoperationsbyhelpingcompaniesto:

Makemoreinformeddecisions.

Makebetteruseofexistingdata.

Developnewinsightsfromlargeamountsofdatathatwouldoverwhelmhumans.

Acceleratelearning.

Automaterepetitive,transactionalandjudgment-relatedtasks.

Reducehumaninvolvementinphysicaltasks.

Createnewservicesandofferings.

Tworecentexamplesshowjusthowfar

onetypeofAI—naturallanguagerecognitionandgeneration—haveevolved.Inrecentyears,auniversityusedIBMtechnologytocreate

“JillWatson,”anAI-basedteachingassistantthatrepliedtostudents’e-mails,2andtheGoogleDuplexAIsystem,whichsuccessfullyreservedarestauranttablebyphone.3Inbothcases,thepeopleonthereceivingendwereunabletodetectthattheywereconversingwitharobot.

Inabusinesscontext,AIisusedtohandleroutine,rules-basedandincreasinglycomplextasks,suchascapitalmarketinvestmentmanagement,eveniftherearemultipleandvaryingfactorsandsentimentstoconsider.

AIdoesthissowellthat56percentofhedgefundsbasetheirinvestmentdecisionsonAIandmachinelearning,andmorethanaquarteruseAItoexecutetransactionsautonomously.4

Ingeneral,today’sAIseeminglymimicsthehumanbraintocarryoutsimpletasksandmaketraditionaldigitizationandautomationmoresophisticated,whichinturnenablesthehumanworkforcetobemoreeffectiveandefficient—aswellasmorecreative.

Forexample,bycapturingandusingexistingknowledgeinautomatedalgorithmsandcreatingnewinsightsthroughmachinelearning,AIaugmentsthehumanworkforce.Asaconsequence,AIwillplayanimportantroleinhelpingtosustainandaccelerateproductivityandgrowthinareaswherethereisadecliningworkingpopulation,suchastheEuropeanUnion.5

ARTIFICIALINTELLIGENCEASDISRUPTOR

AIhasthepotentialtodisrupteveryaspectoftoday’schemicalindustrybusinessmodel(customerinteraction,operations,administration,etc.)andtoopenthedoortonewproductsandserviceofferings.Forexample,itcouldenablechemicalcompaniesto:

Conducthighlytargeted,proactivesaleswithAI-basedcustomer-specificforecastinganddemandsensing—whichcouldmakeitpossibletoship

goodsbeforethecustomerplacesanorder.

Reducepriceleakagethroughsuperiorproductqualityandfasterinnovationcycles,usingdata-drivenR&D.

Developamuchdeeperunderstandingofcustomersandbetteridentifynewapplications/marketsforexistingandnewproducts.

Increaseproductionthroughputbyensuringfirst-time-rightproduction,andtargetingreactionyield,optimized

change-oversandadvancedmaintenancethatreducesunplanneddowntime.

Reducecustomerchurnratesthrough24/7customerservice,withhigherlevelsofdata-drivenservicequality.

Useon-sitenaturallanguage-enabledvirtualtechnicalsalesagentsthatarealwaysavailabletoconsultwithcustomersincaseofproblems/inquiries,andtoinformthemaboutnewofferings.

Byenablingnewlevelsofefficiency

andeffectivenessinallbusinessfunctions,AIwillhaveasignificantimpactonindividualjobprofilesandtheworkforceoverall.

ManytransactionalandsimpletaskswillbeperformedbyAI,therebyenablingpeople

tofocusonmorevalue-addedworkinvolvingcomplexjudgmentandcreativity.Accenturehasassessedtasksin30typicaljobfamiliesinthechemicalindustryandfoundthatnotonlyoperationaltasks(suchasproduction

orsales)willbedisrupted,butalsoadministrative,managerialandscientifictasks.Allinall,AIisexpectedtoreducehumaneffortacrossthesejobfamiliesbyupto45percent.Intheshort-andmedium-term,however,itwillcreatealargedemandforhumanworkerswhowilldevelop,monitorandmanageAI-basedoperations.6

VISIONFORTHEINDUSTRY

LookingatAI’spowerfulandevolvingcapabilities,it’spossibletoimagineafutureinwhichthechemicalcompanyneversleeps—whereautonomousplantsrun24/7withmaximizedefficiency,yieldingproductsinspecwithoutwasteandwithminimaldowntime.It’safuturewhere:

Tedious,routinemanualworkisobsolete,andsafetyandqualityareatunprecedentedhighlevels.

Centralizedteamsofdatascientists,chemicalprocessengineersandproductionmanagerssuperviseautonomousplantsandusereal-timedigitaltwinsimulation

tooptimizeoperationsconstantly.

Dedicatedinterventionteamsofoperators,pooledforlargerplantclusters,are

onstandbytohandleproblemsandunexpectedevents.

AIaugmentssupervisionactivitiessuchasinspectionrounds,helpingtoincreaseoverallplantlifetime.

ProductiondataisfullyavailableforR&Dandcustomerservice.

Inthisworld,R&DwilldrawonatremendousamountofknowledgeandAI-poweredrobotswillconductexperimentswithsuchprecisionthatagivenexperimentwillneverhave

tobeperformedtwice.WithanAIsystemcontinuouslycrawlingthedataecosystem,newapplicationsandpotentialfieldsofinterestwillbeidentifiedquickly,andtheirchancesofsuccesswillbepre-assessedonthespot.Backedbyfullyautomatedchemicallabs,furtherexperimentationwillbetriggeredwithouthumaninteraction,resultswillbeautomaticallyassessedbasedonhistoricinternalandexternaldata,andworkerswillbegivencompletedossiersonpotentialactionstodecidewhethertoengagefurther.Virtuallynotimewillbespentonguessingvaluepotentials,diggingintopreviouslyconductedexperimentstoassesstechnicalfeasibility,

orsearchingtheinternetforsupporting

information—whichaltogetherwillenableleapsinefficiencyandeffectivenessthatwillspeedupproductinnovationcycles.

Meanwhile,customerservicewillusevirtualagentstoincreaseefficiencyandservethecustomer24/7,onlineorbyphone.Accessingallinternalapplication,productionandshippingdata,aswellascustomerinformation,frontlineAIsystemswillworkwithacompletesetofdatathatmakesitpossibleforthetechnologytohandlemostinquiriesonitsown.Onlythemore-complexquestionswillbepassedtospecializedAIsystemsorhumanexperts.

Forexample,withapplication-relatedquestions,theAIlabsystemwillconductsimulationsbasedonhistoricand/orextrapolatedR&D,applicationandcustomerdata,andthenwillconductpreliminarytrialsovernightinafullyautomatedlab.Thenextmorning,thehumanapplicationexpertwillhaveanexperimentallyverifiedrecommendationtoreviewandforwardtothecustomer.

InthisAI-enabledfuture,chemicalcompanieswillproactivelygivecustomerstailoredoffersbasedonmarketinformation,R&Doutput,currentproductportfolioandtheirownprocessdata.Andwiththegreaterefficiencyinvolvedsuchcustomizedofferscanbeextendedtoaverybroadrangeofcustomers,includinglower-priorityaccounts.Inaddition,AI-baseddemandsensingwillletchemicalcompaniesknowexactlywhenanewshipmentisrequired,meaningthatcustomerswillneverrunoutofproduct.AIwillsimplifythecustomerorderingprocessandoptimizewarehousing,increasingcustomerretentionandsimplifyingproductionplanning.

Altogether,suchproactive,individualizedinteractionswillmakeitpossibletodevelopdeeperwin-winrelationshipswithcustomers.

Overall,thisvisionpaintsapictureinwhichtomorrow’sleadingchemicalcompanieswilldrawonAItocreateanefficientandinnovativeenterprisewherehumanintelligenceissupportedbyanAI-enableddigitalworkforce.

REALITYCHECK:ARTIFICIALINTELLIGENCEINTHEINDUSTRYTODAY

Thechemicalindustryisalreadyhighlydata-driven,withitscorebusinessgeneratinglargeamountsofdata.(Figure6)

Forexample,atypicalplanthas5,000to10,000piecesofstaticandrotatingequipment,eachsendingconditiondatasuchaspressure,

temperature,flowrateandspeedofrotationeveryfewseconds—generatingasmuchas50–500terabytesofinformationperyear.Inadditiontoassetdata,chemicalcompaniesneedtomanageproductdata:Here,eachbusinesshashundreds(ifnotthousands)ofstock-keepingunits,includingproductvariants,gradesandpackaging.

InR&D,researcherstypicallyaddtodatavolumesthroughmultipletestseriesandmeasurementsthatyieldinsightsintothecomplexstructure-propertyrelationshipsofchemicalsubstancesortheperformanceofchemicalformulationsinhighlyspecificapplications.Inaddition,companiesalsogeneratealargevolumeofstructuredand

unstructuredtransactionaldataincustomerandsupplierinteractions—informationsuchasordersizes,prices,shippingaddresses,rawmaterialclassification,visitreports,

e-mails,applicationd