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SourcingAutomation

IntelligentSourcingAutomationisanewcategoryofsoftwarethatleveragesintelligentsystemstoautomatecomplexhumanreasoningthatexceedsexpertstandards.ItisconstructedusingamultiplicityofAItechniquestoencodeintelligentreasoningatvariousstagesinasourcingevent,fromthedesignofasourcingeventthroughtotheconclusionofanawardingstrategy.ThiswhitepaperelaboratesonkeyrecenttechnologicaladvancesthatpermitthisnewintelligentautomationandthepathtowiderdeploymentofSourcingAutomation.

Originalpublishdate2018;updatedJanuary2022

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Introduction

SourcingAutomationisanewcategoryofsoftwarethatleveragesintelligentsystemstoautomatecomplexhumanreasoningthatexceedsexpertstandards.ItisconstructedusingamultiplicityofAItechniquestoencodeintelligentreasoningatvariousstagesinasourcingevent,fromthedesignofasourcingeventthroughtotheconclusionofanawardingstrategy.ThisacademicwhitepaperelaboratesonthekeyrecenttechnologicaladvancesthatpermitthisnewintelligentautomationandthepathtowiderdeploymentofSourcing

Automation.

IntelligentSystemsandArtificialIntelligence(AI)

AIisdeliveringmajoradvancesinmanyfields,frommedicaldiagnosticstoalgorithmictradingandpokerbots.IntelligentSystemsincludeAIandothertechniquessuchasstatisticalinferenceandprobabilisticreasoning.WhileAIplaysavitalroleindeliveringintelligentsystems,analystsoftenunderestimatethecollectivepowerofcomplementarytechniquestodeliversolutionstoautomateprocesses.

Keelvartakesapragmaticviewwhenselectingthemostappropriatetechnologicalsolutionforaspecifictaskinthesourcingprocessthatrequiresautomation.Forexample,outlierdetectionrequiresstatisticalinference,whereascategoryclassificationrequiresNaturalLanguageProcessingandMachineLearning.Whencombined,thesetechniquesformanintelligentsystemthatdrivesautomationacrossnumerousstepsthatthemselvesareindependentlyautomatedindifferentways.

StrategicSourcingprocessesaretooslow,fewpeopleunderstandbestpractice,andevenfewerhavesufficienttimeoraccesstotherighttoolstotrulyattainhighestquality.Suppliersareevenmorefrustratedbypoorsystemsandthequalityofthevariousprocessestheyencounter.SourcingAutomationaddressesthesefailingsbyleveragingAItoautomatebestpracticeandprovidingreliableexcellenceinanefficientmanner.

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SourcingAutomation

Automationof‘downstream’activitiesinprocurementhasbeenacceleratinginrecentyears.RoboticProcessAutomation(RPA)builtonsimplerepetitiveroutinescandrivesavings,speed,andreliability.However,automationofcomplexsourcingactivitiesdeliversgreaterstrategicbenefits.

Advantagesintermsofimprovedmechanismsformanagingcompetitivebidprocesses,supplierdiscovery,innovationsupport,improvedspeedofexecution,aswellasagilitytorespondtomarketeventsoffercompellingadvantagesovermanualsourcingprocesses.IntelligentautomationisalsocriticalforsourcingbecausenuancedandcomplexdecisionsrequireAIsystemstomakedecisionsthatoutperformhumanexpertsintermsofspeedandquality.

DrivingAutomation:AnAnalogy

forSourcingAutomation

TheautomationofdrivingisanaptanalogyforSourcingAutomation.Forpersonaltransport,travelerswishtosummonavehicle,instructittonavigatetoadestination,andtravelsafely,makingdueprogresswithoptimalrouting.Similarly,sourcingteamsknowinadvancewhattheobjectiveisbutneedtonavigateatime-consumingpathofcomplexinteractionsalongthatpathtoreachadesiredend-goal.

Often,theyknowlittleaboutthesupplylandscapebecausetheymayhaveworkedinothercategories,arenewtoprocurement,orweretrainedinadifferentdiscipline.

Fromatechnologyperspectivethesimilaritiesarealsoclear.Carsrequirelightdetectionandranging(LiDAR)toseeandheartheirsurroundingsandpredictwhatwillhappen.AutonomouscarsusesensoryawarenessandAItoconductinferencefromsurroundings,whichiscriticalforsafedriving.LiDARsystemsgiveacompletepictureoftheimmediateenvironment,andAI-basedMachineVisionwatchesforpotentialdangerbyinferringwhatobjects,people,oranimalsarenearby,andtheirvelocityandacceleration.

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Figure1:AutonomousVehicles

InSourcing,thekeydatathatneedtobeunderstoodiscontainedinstructureddatasheetsdescribingthegoodsandservicesbeingprocured,transposedfromExcelorinternalsystemstotablesinaKeelvardatabaseviaMachineLearning-basedclassification.

Therowsofdata,thecolumnheaders,invitedsuppliers,summarytext,eventcurrencies,andnumerousotherparametersallofferguidancetoAIastohowitshouldconductasourcingprocess.

Ofcourse,theanalogyendswhenweconsiderthephysicalworldandthedangersfacedonroads.Thephysicaldimensionsofdrivingleadtomorehurdlesandrisks.Fortunatelyforsourcing,mistakesdon’tcausedeathorinjury.SourcingBotscanbeadoptedatafasterpaceduetothelowerriskprofileandabsenceofregulatoryconstraints.

ThehardwareproductionconstraintsinDrivingAutomationarealsoanobviousfrictionaleffectonprogress.Tesla’sdifficultiesintheirownsupplychainareevidenceofthisfactor.TheaccelerationofSourcingAutomationwillbemuchfasterasaconsequenceofthelowercostsofinvestmentandlowerrisksofexecution.

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Speed,Excellenceand

CompetitiveAdvantage

Theprimarychallengesareknowingwhattoprioritizeforsourcing,doingsoswiftly,andapplyingaprocessthatisconsistentlyexcellent.Excellencereliesonmechanismsthatincludenumerousfeatures;somearequitemundanebutservecriticallyimportantroleswhenspeedandautomationarethegoal.Thesefeaturesinclude:

?Datacleansingtechniquesusingoutlierdetection.

?Datatypesafety.

?Inter-lotconsistencyrulesandothermethodstoensurewearereasoningabouttrustworthydata.

?Bidderfeedbackbaseduponappropriateandfairrulestomarshalcompetitivetension.

?Flexiblebiddingtocaptureeconomiesanddiseconomiesofscaleandscope.

?Richscenarioanalysistoexplorecostandnon-costtrade-offssothatParetoefficientoutcomesarefullycharacterizedandcompared.

Thereareotherkeycharacteristicsofsourcingexcellence,buttheyaretoomanytolisthereandarenotthemajorfocusofthispaper.Historically,amajorchallengetoreliablyachievingbestpracticeisthatit’stime-consumingandrequiressmartoptimization-backedsourcingtools.Mostorganizationsdon’thavesufficientresourcesand/ortoolstohelpachievethisstandardandiftheydo,thenonlyasmallnumberofuserscanaccesstherighttoolsandfullyunderstandthestandardsrequired.

ExpectedBenefits

ThenewfieldofSourcingAutomationoffersnumeroussalientbenefits.Themostimportantisthestrategicadvantagesofestablishingreliablebestpracticethat’sfasterthancompetitorsstrugglingwithmanualprocesseswhowillfailtoachievethesamelevelofqualityinfinaloutcomes.

1.SpeedandEfficiency:Automationcanexecutetasksfaster,trackstatusoftasks,andallowforscaleinthevolumeshandled.Newsourcingeventscanbelaunchedinsecondstominutes,andnumbersofsourcingtasksacrossmultipleeventscanberunningsimultaneouslyandatdifferentprogressstages,andthemachinecanmanageallofthatworkload.

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2.AgilityandResponsiveness:Automationcanprovidecontinuousmonitoringtoseewhentosourcegoodsandservices.Forexample,bymonitoringfuturesandspotmarkets,itispossibletojudgeleadingindicatorsthatsignifytheneedforaction.

3.EnrichedBidding:Thefasterexecutionofpublishedeventscaninstillincreasedcompetitivetensionwhileretainingfinecontroloverquality/costtrade-offs.Also,automatedandintelligentuseof3rd-partydatacaninformperformanceandfeedbackforbiddersinmulti-roundevents.

4.ProcessCompliance:Consistencyisensuredfortasksthatareautomated,therebyenhancingtheoverallsourcingprocess.

5.AuditabilityandTransparency:Automationhelpstocentralizeandgivevisibilitytomanysourcingfunctionsthatmayhaveonceoccurredoffline,includingnewrequests,bidsubmissions,negotiations,andevenawarddecisions.

6.HumanResourceAllocation:Offloadingtedious,repetitiveanddata-intensiveworktointelligentsoftwareagentscangivehumanteammembersa“promotion”ofsorts,freeinguptimetofocusonmorestrategicandcreativeopportunities.Thiscanhelpsourcingleadersnewlyassesstheirlaborcostsandneeds.

Therangeandsignificanceofbenefitspointtowardstheoverallstrategicadvantageofthistechnologyasadistinctcompetitiveadvantage.

“Today’sleadingprocurementorganizationsrecognizethattechnologyandautomationwillcontinuetoimproveallaspectsoftheprocurementoperatingmodel,drivingefficiencyandeffectiveness.”

*Source:KPMGFutureofProcurementReport,2020

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Wave1-Level4High

Automation

InFigure2below,thekeystepstowardsfullyautonomoussourcingsystemsaresummarized.ItisimportanttonotethatSourcingAutomationforallcategoriesdoesn’trequireallstepstobeautomated.Forexample,SourcingAutomationofRawMaterialsmayonlyrequireadeterministicbiddingmechanismbeapplied,soAItooptimallyconfigurethebiddingrulesandfeedbackisn’tnecessary.SimplercategoriessuchasthisdonotneedtowaitforfullLevel5Automation,insteadLevel4isallthatisneededinsuchacase.

Figure2:ThePathtoLevel5FullAutomation

SourcingAutomationimplementationsdonotrequirealltheadvancedcapabilitiesinLevel5AutonomousSystems.ThemainchallengesforthefirstdeploymentsofSourcingAutomationaretointegratenecessarydatafeedsforautomatedend-to-endexecutionofspendcategoriesthatarepredictableintermsofthesupplierstoinvite,thetiming,andthebiddingmechanismtobeapplied.Thecommercialtermsofsuchimplementationsarenotwithinthescopeofthispaper,butthereturnoninvestmentfromdeploymentsishigh,thetimetocashflowpositivestatusisfast,andtargetedheadcountreductionsfacilitateasimpleandcompellingbusinesscase.

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MEASURE

GOAL

SourcingAutomationRollout

ThefirstwaveofSourcingAutomationisfocusedonrepetitivebideventsthataremostlabor-intensiveandpredictableintermsofsupplierstoinviteandthebiddingprocesstoapply.Theseareanaturalstartingpointandcanbeincategoriessuchasmaterials,transport,maintenance,consumables,orroutineservices.

TECHNOLOGY

Speed

Minimizetimetoexecuteaneventfrombeginningtoendviafullyautomatedsetup,datacleansingandprocessing,controlofroundsandscenariogeneration.

Autopilot,automatedcleansing,AIbasedconfigurationandAutomatedMechanismDesignensureendtoendspeed.

Awareness

Understandthecategorybeingsourced,thedatabeingcollectedandadviseonpotentialmistakes.

MachineLearning(NLP&ANNs)toclassifyeachbideventandgeneraterecommendations.

Bidder

Biddersneedtobetoldwheremistakesaremade

Cell-leveleditabilitycontrolstopointbiddersto

Direction

andpresentedwithgatingcontrolstoincentivizeaction.

specificcorrectiveactionsthataremandatorybeforeproceeding.

Continuous

Intelligentsystemsshouldalwaysbelearningand

MachineLearningtocontinuouslylearnin

Improvement

improvingsothatperformanceexceedshumanexpertstandards.

supervisedandunsupervisedcontexts.

Intelligent

Givethecontextfromthecategory,automated

Supervisedlearningtotrainsystemstogenerate

Reporting

reportsthatdescribetrade-offsinnon-cost

dimensionsmostrelevanttothatcategory.

scenariosbasedupontaggeddata.

Override

Manualcontroltointervene,stoporadjustaprocess

Fromtheexecutivedashboard,actionable

Controls

manually.

interventionsshouldbeinitiatedwithdetailedcontrolswithinKeelvar’sSaaSapplication.

Oversight

Aviewofprogressacrossmultiplesourcingevents.

Executivedashboardsummarizingprogressofsourcingeventsandstagesofexecution.

Table1:Goals,measuresandsolutionsforLevel4Automation

Itisn’tstrictlynecessarytomonitorunstructurednewsfeeds;secondaryinformationsuchasfuturesmarketscanofferthespeedofupdateyouneedtoactionresponses.So,intelligentsystemscanleveragefastrespondingmarketdatafeedstoinferwhatactionsareneeded.Thenatureofsoftdataisthatitisambiguous;quantitativedatasourcesarepreciseandresponsivetounderlyingevents.Asmarterapproachistoinferwhichmarketsignalsaremosthighlycorrelatedwithsupplychaindisruptionsforagivenorganization.

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Theautomationdeploymentarchitectureshouldprovideamodularandextensibleapproachtohelpfacilitateachievingallthegoalslistedintheprevioustable.Thescopeofworkisusuallykeptnarrowinthefirstimplementation.Theeasiestapproachistosidesteptheneedforintelligentsensingofwhentosource,bychoosingacategorythatissourcedregularlyat,forexample,weeklyintervals.Implementationisalsoeasierwhenthespendcategoryalwaysrequiresthesamebiddingmechanismanddoesn’trequireintelligentdecisionmakingonthenuancesofadjustingbiddingrulestooptimizeresults.Thispermitsadeterministicflowandacceleratesinitialdeployment.Fromthere,usagecanbebroadenedrapidlyacrosslikeevents.

AutopilotApplications

Automationcanalsobeappliedtohandlethemechanicsofmanagingmultipleroundsofbidding,includingbiddercommunications,bidopening,feedbackgeneration,datacleansing,bidroundclosingandtermination-criteriamonitoring,andactivation.Thisisavitalpieceofinfrastructureforenablingend-to-endSourcingAutomationbecausethemechanicsofmanagingpotentiallymanybidderscanbethemosttime-consumingaspectofsourcing.

Considermulti-roundRFQswherethefollowingsequenceofactivitiesisrequired:

?Startinground1bidding

?Reviewingandrevertingtobidderstosuggestdatacleansing(outliers)

?Calculatingandsendingfeedbacktobidders

?Openingofroundswithmessagestomanageexpectationsforbidders

?Updatedbiddingrulesincludingalterationstominimumbiddecrementsandchangestobiddingrules

?Openingasecondroundofbidding,conductedwithdetailedfeedback

?Messaginglaggardbidderstoconcludetheiractivitiesandsupportingdatacleansing

Thisprocessistedioustoexecutemanuallyandthemorebiddersthereare,themoreoneroustheabovetasksbecomeandalsothemorelikelythatshortcutsaretakenandmistakesaremade.

Furthermore,theslowpaceandfrictioninmanualeventsleadstocurtailmentoftheroundsofbidding,limitingthenumberofinvitedbidders,andthereforetoinevitablelostsavingsopportunities.

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Autopilotmanagesalloftheseactivitiessmoothlyandataspeedandscalethatistransformativeforbuyingorganizations.

Figure3:Pre-plannedscheduleforautomatedexecution

SourcingRobotics(orBots)referstotheintelligentsystemsthatoperatesourcingeventworkloadsthatareconfiguredtofulfillaspecificgroupoftasks.Abotknowshowtomanageasourcingeventfrombeginningtoendandreliesonsupervisedlearningmechanismswhenhumanuserswishtooverridethebot’spreferredchoiceofaction.

Humanactionsandoverrideswiththebotalsoserveastrainingmechanismsforthebottocontinuouslyimprove.Aswithhumanintelligence,someoftheintelligenceisgeneral(e.g.knowinghowtonotifybiddersofrelevanteventssuchasroundopenings),whereassomeismorecontext-specific(e.g.knowingwhattypeofscenarioisrelevanttogenerateforoceanfreight),andyetmorecanbeinstance-specific(e.g.knowingthatforaspecificbusinessunit,whenvolumesexceedathreshold,splittheitemintotwoparts).

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MEASURE

GOAL

TheFuture:Level5Fully

AutonomousSystems

Thechallengeofknowingwhentosourceandhowtosourceitoptimally

--giventhespecificdimensionsofthegoodsorserviceinquestion--isanadvancedchallenge.Somesimplercategoriessuchasroutinesourcingeventsfortransportationspotbidding,materials,maintenance,orotherservicesmaybepredictablesothetimingofthesourcingeventisaneasydecision.Othercategoriessuchasglobaltransportorpartsmaydependuponobservationsofbenchmarkprices,spotmarkets,volumechanges,andcontractexpirationdates.ThelogicfortimingreliesuponStochasticModellingtechniquestogeneratepredictiveanalyticsandrequirescouplingwithotherdatastreamsregardingcontractdurationsanddemandpatternsforeffectivemodelling.

Autonomoussystemsshouldrespondrapidlyastriggeringeventsunfold.Forexample,ifthereisanaturaldisasterinsomepartoftheworld,thiswillimpactcommodityprices,andreactivemeasuresshouldbeinitiatedimmediatelytosourcealternativesupplylinesshouldsupplyuncertaintybecomeanissue.Thisactionableintelligence,however,requireshigherlevelsofsophistication,andtheexecutionrequiresmoresophisticatedAlgorithmicMechanismDesigntotrulyoptimizethego-to-marketstrategy.

Thenot-too-distantfutureofintelligentsourcingautomationwillseewideradoptionofLevel4,andthenultimatelyincludethetechnologythatcandeliverLevel5autonomyattheoptionoftheuser.

TECHNOLOGY

Agility

Monitoringofalarmstodetectwhenmarketconditionsshifttotriggerasourcingevent,giveninventorylevelsandriskattitude.

IntegrationservicehooksintodatafeedsandERPsystemsnecessarytotriggeraction.

Demand

Machinescanstatisticallyinferwithgreater

StochasticModellingforpredictiveanalyticsto

Prediction

precisionandspeedwhatthefutureprobabilisticevolutionofdemandwillbe.Thisiscrucialdataforsuppliersthatneedtocommunicateeconomiesofscaleforpricing.

estimatefuturedemand(andseasonalityifappropriate).Thismayinvolveeitherregressiontechniquesand/orfittingprobabilisticmodelstoinferfuturedemand.

Intelligent

Decisionsonwhotoinvitecanbecriticalto

RecommenderSystemsthatapplysupervised

Invitations

maximisingcompetitionwhilstalsopreventinglowerqualitysuppliersfromcreatingnoiseinabidevent.

learningapproaches.

Smart

Biddingrules,bidderfeedback,flexiblebiddingand

AlgorithmicMechanismDesign

Mechanisms

automatedbundlegenerationwithsmartAskpricingdrivesbetterandfasterresults.

Table2:Goals,measuresandsolutionsforLevel5Automation

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IntelligentAutomationEnhancingHumanWork

AIistrumpingexperthumanperformanceinmanydomains.Forexample,pokerisacomplexgameandfewpeopleevermasterit,andthosethatdoinvariablyrequiremanyyearsofpractice.AI-poweredbots,however,havenowmasteredpokerandeventheworldchampioncannotbeatabotthathaslearnedhowtomasterthegame-theoreticanalysisandcalculationofmixedstrategyequilibriatooptimizepayoffs.

Furthermore,AIisanti-fragileandthuswillcontinuouslyimprove,whilehumansneedcontinualpracticetomaintainstandards.AI’scontinuousimprovementiseffortlessasthetrainingdataisever-increasinganditslearningisautomatic.

ThesoberingfactisthatAIisdefeatingthebesthumanexpertsintaskswheretheboundariesandconstraintsondecisionmakingarewell-definedclosedsystems.ItwouldbeamistaketoassumethatAIwon’tbecompetitiveinthetasksassociatedwithtacticalsourcingandthenultimatelyovertakehumansinthisrole.Oncetheboundariesofdecision-makingarecommunicated,thenthegame-theoreticreasoningforoptimizingthemechanismforsourcinggoodsandservicesbecomesjustanothercomplexbuttractablecalculationforArtificialIntelligence.

However,thisneednotbeanegativeforthehumanbuyerinsourcing.Instead,theAIinthesesourcingbotswillfreeupthehuman’stimetomoveawayfromtryingtojuggletedious,repetitiveandpredictabletaskworktofocusonareaswheremachinescannotexceedus,suchasstrategicplanning,empathyandrelationshipdevelopment,andcreativethinkingasafewexamples.Thismakesastrongcaseforcombininghumanswithmachinestoadvancesourcingtodelivernet-newvaluegains.

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Figure5:BotscanAutopilotsourcingeventsinamannerandscalethatensureconsistenthighquality.

Summary

AsStrategicSourcingcontinuestonavigatemorecomplexity,disruption,andneedformodernization,thereisrarelysufficienttime,resources,andsystemsinplacetoexecuteprocessestoveryhighquality,letaloneexcellence.AIcannowdeliverautomatedsourcingtobest-practicestandardswithconstantavailabilityandunmatchedagility.

AboutTheAuthors

DavidDevlin,ChiefTechnologyOfficer,Keelvar.DavidisacomputerscientistandcametoKeelvarfromtheCorkConstraintComputationCentre.HisresearchfocusedonOptimizationandMachineLearning.

AlanHolland,PhD.CEO,Keelvar.AlanhasaPhDinComputerSciencespecialisinginArtificialIntelligence.Hispost-doctoralresearchfocusedonAlgorithmicMechanismDesign,GameTheoryandOptimizationwithpublicationsinIJCAI,AAAIandECAI.HewasbasedintheInsightCentreinUniversityCollegeCorkandthecourseleaderforataughtMScinIntelligentSystems.

BarryHurley,PhD.PrincipalSoftwareEngineer.BarryhasaPhDinComputerScienceandhisdissertationfocusedonMachineLearningtechniquesforoptimizingperformanceincombinatorialoptimizationproblems.AtKeelvarheleadstheIntelligentSystemsteam.

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AboutKeelvar

Foundedin2012,Keelvarismovingprocurementforwardwithourbest-in-breedSaaSsoftwareforintelligentsourcingoptimizationandautomation,designedforeasyadoption,scale,andproductivity.Ourcustomersareglobal,blue-chipcorporationsandmid-sizedcompaniesusingoursolutionsacrosstransportation,directmaterials,indirectgoodsandservices,andpackagingcategories.

Contactusforpricingandademo:

KKeeeellvvaarr..ccoomm

WHITEPAPER

Sourcing

Automation

IntelligentSourcingAutomationisanewcategoryof

softwarethatleveragesintelligentsystemstoautomate

complexhumanreasoningthatexceedsexpertstandards.It

isconstructedusingamultiplicityofAItechniquestoencode

intelligentreasoningatvariousstagesinasourcingevent,

fromthedesignofasourcingeventthroughtotheconclusion

ofanawardingstrategy.Thiswhitepaperelaborateson

keyrecenttechnologicaladvancesthatpermitthisnew

intelligentautomationandthepathtowiderdeploymentof

SourcingAutomation.

Originalpublishdate2018;updatedJanuary2022

K

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Introduction

SourcingAutomationisanewcategoryofsoftwarethatleveragesintelligentsystemstoautomatecomplexhumanreasoningthatexceedsexpertstandards.ItisconstructedusingamultiplicityofAItechniquestoencodeintelligentreasoningatvariousstagesinasourcingevent,fromthedesignofasourcingeventthroughtotheconclusionofanawardingstrategy.ThisacademicwhitepaperelaboratesonthekeyrecenttechnologicaladvancesthatpermitthisnewintelligentautomationandthepathtowiderdeploymentofSourcing

Automation.

IntelligentSystemsandArtificialIntelligence(AI)

AIisdeliveringmajoradvancesinmanyfields,frommedicaldiagnosticstoalgorithmictradingandpokerbots.IntelligentSystemsincludeAIandothertechniquessuchasstatisticalinferenceandprobabilisticreasoning.WhileAIplaysavitalroleindeliveringintelligentsystems,analystsoftenunderestimatethecollectivepowerofcomplementarytechniquestodeliversolutionstoautomateprocesses.

Keelvartakesapragmaticviewwhenselectingthemostappropriatetechnologicalsolutionforaspecifictaskinthesourcingprocessthatrequiresautomation.Forexample,outlierdetectionrequiresstatisticalinference,whereascategoryclassificationrequiresNaturalLanguageProcessingandMachineLearning.Whencombined,thesetechniquesformanintelligentsystemthatdrivesautomationacrossnumerousstepsthatthemselvesareindependentlyautomatedindifferentways.

StrategicSourcingprocessesaretooslow,fewpeopleunderstandbestpractice,andevenfewerhavesufficienttimeoraccesstotherighttoolstotrulyattainhighestquality.Suppliersareevenmorefrustratedbypoorsystemsandthequalityofthevariousprocessestheyencounter.SourcingAutomationaddressesthesefailingsbyleveragingAItoautomatebestpracticeandprovidingreliableexcellenceinanefficientmanner.

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SourcingAutomation

Automationof‘downstream’activitiesinprocurementhasbeenacceleratinginrecentyears.RoboticProcessAutomation(RPA)builtonsimplerepetitiveroutinescandrivesavings,speed,andreliability.However,automationofcomplexsourcingactivitiesdeliversgreaterstrategicbenefits.

Advantagesintermsofimprovedmechanismsformanagingcompetitivebidprocesses,supplierdiscovery,innovationsupport,improvedspeedof