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BackgroundIncollaborationwitheMoldino,OEMclientsimplementedtheeMoldinosolutiontogainabetterinsightintotheirmanufacturingassets(namely,tooling)and,viatheanalysesgeneratedbygranularproductiondata,clientssoughttobetterunderstandand,therefore,achievebettermanagementoftheoverallmanufacturingprocesses.Byleveragingnewlydiscoveredproduction-relatedinsights,clientsidentifiednewwaystooptimizetheir
production
conditions.*Alldatarepresentedinthepresentanalysisaremockdatadesignedtodemonstrateonlythecoreinsightsfrom
the use cases under anonymized conditions.Client Challenges●Lack of a system to
efficiently monitor, maintain, and govern all tooling assets managed by the
client●Need for optimization of management capacity to minimize production risk
and maximize efficiency●Need to evaluate the performance of the supply chain as
a whole, of which outsourced productioncomprises a large part of this holistic
capacity●Lackofaconsistentperformancemeasurementframeworktogaugetheeffectivenessofeachproduction
activity or discrete manufacturing
processesAsillustratedinFigure1,aprocess-orientedsystemholdstheentireprocessof‘injectionmolding’asasingleunit.Ontheotherhand,adata-orientedsystemdecomposestheinjectionmoldingprocessintomorediscreteprocesses,makingimportantdistinctionsbetweenthediverseinjectionmoldingsubprocesses.Clientcompanieshavebeenrelyingonaprocess-orientedmanufacturingsystem,wheretheeffectivenessoftheproductionactivities(whethercycletimesarebeingconsistentlymettooptimallevelsorwhetherthemoldtemperatureisconsistentlybeingheldtodesignatedpoints)wereevaluatedonlybasedontheresultsgenerated
by the larger
process.Inotherwords,ifthelargerinjectionmoldingprocessproducedpartsthatpassedthequalitystandards(evenifbarely)andweredeliveredwithinreasonabletimeframes,themoreintricateproductionparameters(cycletimes,temperature,pressure,etc)werehardlyscrutinizedandthewholeoperationalprocesswasdeemedsufficientlyoptimal.Ineffect,thecurrentlyoperativeproductionmanagementsystemreliedheavilyongeneralproductionprinciples(theholdingofmoldtemperaturetoxamountwillbeoptimal)andwasimplementedinproduction
without any real analysis of the discrete production
components.Thismayhavebeenthepragmaticandefficientapproachintermsofcostandwhatwastechnologicallyfeasibleatthetime.However,inlightofnewtoolssuchasbigdata,IoTtechnology,anddata-drivenanalyticalframeworksthatuseAI,thecontinuedrelianceonaprocess-orientedapproachwouldbewalkingblind.AsseeninFigure1foraprocess-orientedapproach,theanalysisstartsinthe‘evaluationphase’and,morespecifically,whenthepartis‘faulty’.Thissuggeststhatanykindofcloseinspectionoftheproductionconditionsisconductedonlyintheeventofamajormanufacturingmalfunction(ie.,whenthescraprateisunusuallyhigherthannormallyaccepted).Accordingly,untiltheOEMclienthadconfrontedthemalfunctionintheformofamajorproductionflaw,theclienthadnoeffectivevisibilityoftherisksofforthcomingassetfailure1

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