nist -制造业企业集成的元数据建模 Metadata Modeling for Manufacturing Enterprise Integration

NISTAdvancedManufacturingSeriesNISTAMS100-65

MetadataModelingforManufacturingEnterpriseIntegration

YanLu

BoonsermKulvatunyouDimitrijeMilenkovićJamesWilson

MichaelFiguraDavidNollerJoshKi

Thispublicationisavailablefreeofchargefrom:

/10.6028/NIST

.AMS.100-65

NISTAdvancedManufacturingSeriesNISTAMS100-65

MetadataModelingforManufacturingEnterpriseIntegration

YanLu

SystemIntegrationDivisionEngineeringLaboratory

NationalInstituteofStandardsandTechnology

JamesWilson

OpenApplicationGroup,Inc

MichaelFigura

OpenApplicationGroup,Inc

DavidNoller

VirginiaTech.University

BoonsermKulvatunyou

SystemIntegrationDivisionEngineeringLaboratory

NationalInstituteofStandardsandTechnology

DimitrijeMilenković*

SystemIntegrationDivisionEngineeringLaboratory

NationalInstituteofStandardsandTechnology

JoshKi

LockheedMartin

*FormerNISTGuestResearcher;allworkforthispublicationwasdonewhileatNIST.

Thispublicationisavailablefreeofchargefrom:

/10.6028/NIST

.AMS.100-65

January2025

U.S.DepartmentofCommerce

GinaM.Raimondo,Secretary

NationalInstituteofStandardsandTechnology

CharlesH.Romine,performingthenon-exclusivefunctionsanddutiesoftheUnderSecretaryofCommerceforStandardsandTechnologyandDirector,NationalInstituteofStandardsandTechnology

NISTAMS100-65January2025

Certainequipment,instruments,software,ormaterials,commercialornon-commercial,areidentifiedinthis

paperinordertospecifytheexperimentalprocedureadequately.Suchidentificationdoesnotimply

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LuY,KulvatunyouB,MilenkovićD,WilsonJ,FiguraM,NollerD,KiJ(2024)MetadataModelingforManufacturing

EnterpriseIntegration,(NationalInstituteofStandardsandTechnology,Gaithersburg,MD)AdvancedManufacturingSeries(AMS)NISTAMS100-65.

/10.6028/NIST.AMS.100-65

AuthorORCIDiDs

YanLu:0000-0003-2927-5860

JamesWilson:0009-0001-9809-7212

BoonsermKulvatunyou:0000-0000-0000-0000

ContactInformation

yan.lu@

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Abstract

Thecontinuousdigitalizationinmanufacturingdirectlyleadstoasignificantincreasein"datavolume",whiledigitaltransformationisexpandingtheexchangescenariosofsuchdata.Managingmetadata,thedescriptionofdata,isafoundationalaspectofsuccessfulenterprisedigitaltransformation.Byprovidingclarityandstructureofdata,metadatamodelsandstandardsensurethatdatacanbediscovered,accessed,sharedandusedeffectivelyacrosstheorganization,drivingbetterdecision-making,improvingoperationalefficiency,andsupportingcompliance.Thisreportexaminescommonmanufacturingenterprisemetadataexchangescenarios,analyzesandcomparesexistingmetadatastandardsusingtheFindable,Accessible,InteroperableandReusable(FAIR)principles.BasedonthemetadatastandardsreviewedandaFAIRdatametadatastackmodel,weproposeauniform,general,andextendablemetadatamodelwhichnotonlycapturesessentialmetadataelementsforFindability,Accessibility,andInteroperabilitybutalsousesanextendablemetadatamodelfor(Re)usability.AnextensionoftheproposedUniformMetadataModelforManufacturingEnterpriseIntegrationisdemonstratedforindustrialmeasurementdatasetmetadatarepresentation.Additionalgeneralmetadataelementsareidentifiedformeasured,sampleddata,andderivedstreamdescriptions.Thereportconcludeswithadirectionformanufacturingmetadatastandardsdevelopment.

Keywords

Manufacturingenterprise,metadata,metadatamodeling,FAIRdata,dataexchange,digitaltransformation

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TableofContents

1.Introduction 1

2.MetadataUseCasesandMetadataTypes 3

2.7.Generaldata-drivenmanufacturingenterpriseapplications8

3.ExistingMetadataStandards,Technology,andActivities 10

3.10.1.connectSpecMessageModel 21

3.10.2.ConnectSpecDatasetMetadatamodel 23

3.16.1.GoogleStorageMetadataModel 31

3.16.2.AmazonAWSmetadatadefinitions 32

4.MetadataModelMappingandAnalysis36

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4.1.MetadaastandardsMapping.……………38

4.2.AuniformMetadataModel.….40

4.2.1.GeneralUMM4Meimetadataelements 41

4.3.MeladataModelforIndustrialDataset…….49

5.SummaryandConclusions 53

6.Reference 54

7.Appendix 57

71.A:Termsanddefnitions…….s7

72.AppendixB;Exemplarmetadatamapping……….59

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1.Introduction

Metadataisdatathatdefinesanddescribes“data”basedonISO/IEC197631,whiledataisdefinedasaformalizedrepresentationofinformationthatcanbeinterpreted,communicated,orprocessed.Thescopeofthe“data”tobeaddressedinthispaperincludespersistentdigitalobjectssuchasdesignmodels,images,measurementdatasets,machinelearningmodels,andmanufacturingandsupplychaindocuments,whicharecommoninmanufacturingenterprises.

Metadatamanagementisakeycomponentofdigitaltransformation

[1]

inthatitprovidesinformationaboutwhatadigitalobjectis,whereitcanbefound,whoitscreatorsare,howitwascreated,howitwasupdated,whatpartiesareauthorizedtoaccessit,etc.Businessuserswhoworkcloselywithdata(e.g.,analysts,datascientists,andITteams)relyonmetadatatogivethemcrucialcontextforderivinginsightsaboutvariousdataobjects.However,manyorganizationsdonotrecognizetheimportanceofmetadatamanagementforeffectivestrategicandtacticaldecision-making.Metadata-relatedcapabilitiesandpracticesthatareoftenlackingintheenterpriseinclude:

1)understandingofhowdatacanberepurposedandreused,

2)workflowsthatcollectmetadata,logmetadatacollection,andsupporttheexecutionofbusinessprocesses,

3)digitalrightsmanagement,includingauthorizationdelegation,

4)trustinthequalityofdataandmetadatafrombusinessparties,

5)interoperablemetadatadefinitionthatsupportsitssharing,and

6)technicalmechanismandtoolsformetadatacapture.

Standardmetadatamodelsandmodelingapproachesarekeytoimprovingmetadatamanagement,dataobjectusage,andaddressingthedeficiencieslistedabove.Well-developedmetadatamodelsspecifyrichmetadataelementsforvariousdataobjecttypes.Industriesfacechallengesinmetadatamodelingduetothevastvarietyofthedataobjecttypeswithinandacrossenterprises,andduetothenumberofmetadataelementsneededtosatisfyeverydatauseandexchangescenario.Therefore,aneffectivemetadatamodelingarchitectureisrequired.

Severalorganizations,includingInternationalOrganizationforStandardization(ISO)2,ObjectManagementGroup3,ResearchDataAlliance(RDA)4,andInternationalElectronicsCommission(IEC)5etc.,areworkingondigitalobjects,researchandscientificdata,andenterprisemetadatadefinitions.Morespecifically,ISO/IECJTC1/SC32/WG2wasestablishedin1998todevelopandmaintaindomain-agnosticstandardsthatfacilitatespecificationandmanagementofmetadata.Theirscopeincludesaframeworkforspecifyingandmanagingmetadata,anddetailedspecificationsonhowtodescribedataelements,valuedomains,andotherreusablesemanticandrepresentationalinformationobjectsaboutthemeaningandtechnicaldetailsofadataitem.

1

/standard/84749.html

2

/

3

4

/

5

https://www.iec.ch

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GOFAIRisabottom-up,stakeholder-drivenandself-governedinitiativestartedbytheOpenSciencecommunitythataimstoimplementtheFAIRdataprinciples,makingdataFindable,Accessible,InteroperableandReusabl

e[1].

ToimplementFAIR,the

ResearchMetadataSchemas

WorkingGroup

underRDA6aredevelopingguidelinesforthedata-sharingcommunitieswhoseneedsarenotaddressedbyexistingmetadataschemasuchas,andprovidesguidelinesonproposingextensions.Inaddition,therearemultipleinteroperability-basedstandardsdevelopmentorganizationsworkingonpromotingbusinessprocessinteroperabilityforbothinter-enterpriseandintra-enterprisebusinessprocessesbasedonopenstandardsandtool

s[17][18][19].

Forexample,connectSpec(formerly,OAGIS,

[2])

fromOAGi,anon-profitindustryconsortium,definesacommoncontentmodelandcommonmessagesforoperationalorengineeringinformationexchange.Thesestandardsandguidelinesgreatlyimproveenterprisebusiness-processintegration.

Meanwhile,enterprisemetadatamanagementsoftwareoptionsareincreasinginnumberandcapabilities.Theyprovidethetechnologynecessarytoensurethatthemetadataacrosstheenterpriseaddsvaluetothatenterprise’sdata.Themarketformetadatamanagementsolutionscomprisesvendorsthatincludeoneormanymetadatamanagementcapabilitiessuchasmetadatarepositories,businessglossary,datalineage,semanticmodeling,andmetadataingestionandtranslation.Metadatamanagementisalsotypicallyabuilt-infunctionindatacatalogs,datalakeproducts,anddatawarehouseproducts.However,thelackofstandardsleadstothedifficultyinintegratingmetadata-managementsoftwaretools.

Continuousdigitalizationanddigitaltransformationareexpandingenterpriseinformationexchangescenariosthatspanproductdata,processdatasets,andsupplychaindocuments.Thistechnicalreportaimstoprovideafoundationforenterprisemetadatastandardsdevelopment.Wefirstintroduceafewenterprise-dataobjectexchangeusecasesandtheirneedsofmetadatadata(Section2).Section3reviewsexistingstandardsformetadatamodelingandstate-of-artmetadatatechnology.InSection4wepresentaUniformMetadataModelingapproachforManufacturingEnterpriseIntegration,namedUMM4Mei.Itisanovel,extensible,hierarchicalmetadatamodelingapproachwhichcanbeusedforCADmodel,processdatasetandsupplychaindocumentmetadatamodeling.Section5concludesthepaperwithstandardsdevelopmentplan.

6

/groups/research-metadata-schemas-wg/members/all-members/

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2.MetadataUseCasesandMetadataTypes

Therearevarioususecasesofmetadatabymanufacturingenterprises,forproduct

development,productionsystemcontrol,operationandmaintenance,andsupplychain

management.Inthissection,somerepresentativedigitalobjecttypes,theirmetadataandassociatedusecasesaredescribed.

2.1.CADmodelmetadata

Three-dimensional(3D)CADmodelsarethedatasetthatcontains3Dgeometricelementsrepresentingtheobject/parttobemanufactured.CADModelscanbegeneratedbycommercialCADsolidmodelingsoftwaresuchasSolidWorks,NX,Creo,andAutoCAD.3DDesignistypicallyaniterativeprocessthatinvolvesmanyiterationsofrefinementsintheshapeanddimensionoftheinitialmodel.Itoutputsanoptimizedmodelaccordingtogivenobjectivesandboundaryconditionstomeetdesignrequirements.Designersfrequentlyusesimulationsduringthisprocesstovalidatedesignimprovements.

CADmodelscanbeexportedfromtheCADsoftwareinmanyformats.Someofthefileformatsarenativetothespecificprogrambutincludemoreinformationandfeatures.Somefileformatsareneutral,allowinginteroperabilityamongdifferentCADsoftware.UsingtheCADsoftware,userscanaddadditionalmetadatasuchasaTitle,Subject,Author,Keywordsandcommentsasfileproperties.Theycanalsoaddcustompropertiessuchasthedrawingnumberandrevision.Forexample,userscanaddmetadatatoanAUTOCADDWGfilewiththe‘DWGPROPS’function[4].Designintents,designrules,andvariousversionsoftheCADmodelsandthepedigreeinformationcanbecollectedandmanagedaswell.AdditionalmetadataforCADmodelsmakethemodelmoreusefulandcanbereusedbyvariousstakeholdersduringaproductlifecycle.

2.2.AgricultureMetadatausecases

Datageneratedbyglobalagriculturesystemsaretransformingfoodproductiontowardsasafe,sustainable,resilient,and

high-qualityfuture[3].

Industrybenefitsfromawarenessofandlinkagebetweenagriculturaldatasetsattheproducer/farmer,retailer,processorandup-anddownstreamchannelpartnerlevels.Supplychaintransparency,soundfarmbusinessmanagement,andconsumerconfidencethroughimprovedtraceabilityallrelyontheabilitytoclearlyandconsistentlylinkdata.AspecificusecaseistoprovidethestructuretolinksummarymetadataaboutCriticalTrackingEvents(CTE),suchthatrawdatacanbefoundforanalyse

s[4].

Metadatawilldescribetherawdataspecifictoitsscope,provideaURIlinktotherawdata,andpotentiallyknownrelatedpredecessormetadatathatmayexist.Agriculturedatasetmetadata:

●Allowsawarenessandlinkageofrelatedagriculturaldata,

●Addressesrealitythatindividualsworkingtosolveaspecificoftheagriculturalvaluechainoftenworkwithmorethanonedatarepository,

●Assistsinproductrecalls,traceability,andconsumerconfidence,and

●Enablesfarmerstosharetheirdatawithabest-of-breedtoolforgaininginsights.

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2.3.ProcessDataMetadataExchange

Industrialsystemsgeneratelargeamountsofprocessdata,whichusuallyareintimeseries,collected,storedandcanberetrievedfromthesoftwareknownashistorian.Processdatausuallydescribemeasurementsovertime,whichcapturethequantificationofattributesofamanufacturingobject,suchasequipment,partorprocess,oraneventonthatobject.

Processdataarethemostvaluableassetsformanufacturingandotherenterpriseperformanceimprovements.Theyofferinsightintohowasystemhasperformedandvariedovertimeandenableoperatorstomakedata-drivendecisions.Thedataareusuallyarchivedinalong-termhistorianthathelpstrack,overtime,manufacturingsystemproblemsandproductdefectscausedbytheproblems

[5].

Toidentifycausesofproductqualityproblemsorissueaproductrecall,thetimeseriesdatainhistoriansshouldbepackagedandsharedwithstakeholdersinthesupplychain

[6].

Metadataoffersinterpretabilityforinsightsfromprocessdataintoresolvingmanufacturingoperationproblemsoraidingenterprisedecisionmaking.However,metadataforprocessdataisachallengeinmanufacturingcontext,sincedataarenotstandardizedacrossduetoawiderangeofproducts,machinesandproductionlinesused.Amoreprecise,standardizeddescriptionofdataisessentialinordertoextractmeaningfulmetadatafromthedata.

Business

Planningand

Logistics

Manufacturing

Operationand

Control

ShortTerm

Historian

Trendeddata

S95Level0,1,2PLCDCSSCADA

fast

specific

Historian

Data

Abstraction

andConditioning

Models

and

Knowledge

ContinuousControl

S95Level3MES

S95Level4ERP

slow

abstract

DiscreteControl

BatchControl

Figure1.ProcessdataandISA95Hierarchy(Adaptedfrom[9])

TheISA95describestheflowofmanufacturingprocessdataasillustratedintheFigure1.Movingfromlowlevelstohigherlevels,dataarecompressed,aggregatedandabstractedfordecisionmaking.Thecontextofdatageneration,aswellasthepedigreeinformationaboutthedatatransformation,mustbecapturedfortheoperationalorbusinessintelligenceatLevel3and4,whicharespecifiedbytheISA95activitymodelandobjectmodel.ISA88capturessome

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metadataforprocessdataincludingtheirformats,associatedproductdata,materialdata,equipmentdata,personnel,otherprocessdataandbusinessdata.Themostimportantmetadataaretimeandlocation,whichplaceprocessmeasurementsincontextoftimeandspace.Thecombinationoftheprocessmodel,theequipmentmodelandtimecanputtimeseriesdataincontextofothereventsintheenterprise.Forexample,videodatacanbecombinedwithtimeseriesdatabyrepresentingthevideocameraintheequipmentmodelandcombiningthetimestampsfromthevideostreamandthetimeseriesdata.

Traditionally,ISA95equipmentmodelandISA88batchdatamodel,aswellasPACKML,OPCUAareusedfortransientdatamodelingandenablethedataexchangebetweenmachines,SCADAandMESorERPsystems.Insmartmanufacturing,machinelearningismorefrequentlyappliedtopersistentprocessdatastoredinclouds.Thesemachinelearningmodelsareusedforproduct/processdevelopmentorvaluechainoptimization,beyondtypicaloperationaluseinMESandERP.Thereisaneedtore-examinethemethodtomodelthemetadataforprocesstimeseriesdata.

2.4.SupplyChainMetadata

AsgoodsmovethroughtheSupplyChain,aplethoraofinformationaboutthegoods,custodyofthegoodsandmovement/handling(includingdivisionintoboxes,bottlesorpackagingintocartonsorpallets)ofthegoodsiscollectedalongtheway.Untilrecently,thisinformationwascollectedin“siloed”systemsownedbyvariousparticipantsinthesupplychain(e.g.themanufacturer,theshippingentity,orthereceivingentity).Thetrendtodayistowards“chainofcustody”systemsthatcangatherupmuchofthisinformationandstoreitinoneuniversallyavailablesystemsoastoprovidea“bigpicture”viewoftheinformation,namedmetadata.

Thedata(andmetadata)requirementsfor

“Provenance”

,or

“ChainofCustody”

forgoodsmovingthroughtheSupplyChainareclearlydifferentfroman

“opensupplychain”

(e.g.tosupportCloudManufacturing).

Considertheexampleofmovingfoodfromfarmtotable,asdescribedby

[9].

Figure2:TrackingFoodfromFarmtoTable

[9]

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ThefigureshowsthattherearetypicallymultiplepartnersinvolvedinsuchaSupplyChain,including,Farm,Packer,Transporter,Customs,Processor,DistributionCenters,RetailerandCustomer.

Potentially,allthesepartnersmayhavesiloedsystemscollectingdataontheirportion/involvementintheSupplyChainprocesses.Informationcollectedduringtheprocessincludes,forexample,PackingLists,BillofLading,TransportationDocuments,Exportlicense,Customsdocuments,Invoice,FreightBill,Waybills,Inspectiondocuments.Thesedocumentsinthesupplychainareimportantforcustomersandcanaffectthingslikeprice(Organicscommandahigherpricetoday,sometimesmuchhigher)andrecalls(itisimportanttoknowthesourceoffoodandwhereitwentforsituationslikerecallsinthecaseofcontamination).

Likewise,contractServiceLevelAgreements(SLAs)mayrequirespecifiedtimesfordelivery(toensurefreshness)andtemperaturecontrolaswell,soIoTdataandbusinesseventsareusedtocollectdatathatcanbeusedtohelpensureSLAcompliance.

Inordertounderstandmetadatarequirements,itisfirstnecessarytounderstandthedatacollectionrequirementsfortheusecase.Insummary,thefollowingcategoriesofdataarecollected:

1.BusinessEvents-includeseventssuchaspacking,unpacking,transportation,location,changeofcustody.ThesearetypicallycapturedviadigitalbusinesseventsbasedonstandardssuchasEDI,OAGISorGS1/EPCISwhichareself-describingevents(nometadatareallyrequiredoutsideoftheevents).

2.Documents-manydocumentsaregeneratedandexchangedduringthelifeofthefoodinthesupplychain,someofwhicharedescribedintheUseCaseDescriptionsection.Thesedocumentscanbepaperordigital(e.g.PDF,JPG)butmustbestoredinasecurewaytoensureprovenance/chainofcustodyforthegoods.Metadatarequiredherewouldinclude,forexample,CreationTimestamp,Format,DocumentID,DocumentType,Location,Creator,Device,EditsandURI.

3.IoTData(e.g.Temperature,Humidity,Velocity,Shock,Location).RequiredmetadataincludesDevice,Timestamp,DataType,Quantitykindandunit,andtheaboutness/contextofthedata,e.g.,containernumberorlocationfromwhichthedatawerecollected.

2.5.AdditiveManufacturingBigDataSetMetadata

Additivemanufacturing(AM)isasmartmanufacturingtechniquethatfabricatescomponentsdirectlyfrom3-Dmodelsbyselectivelypoint-to-pointorlayer-upon-layerjoiningmaterials.Differentfromthetraditionalsubtractivemanufacturing,avastvarietyofbigdatasetsare

generatedthroughAMdevelopmentlifecycles.Theamount(~TB),type(images),andspeed

(~GB/sec)ofthecollecteddataareunprecedented.ThedatasetsarecreatedandcollectedtocaptureandcharacterizevariousfactorsthatcouldaffectAMpartquality,includingthosefrommaterialdesign,machineandprocessdevelopment,andprocessmonitoringandcontrol,as

wellaspartinspectionandtesting.Inthedetailsoftheseactivities,alargerangeofoverlappingdataneedsexistthatmaybenefitfromsharingandreusingdata.

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Figure3:AMdatasetandusescenarios.

AsshowninFigure3,designdata,machinedata,in-processmonitoringdata,aswellasmeasurementsfrommaterialcharacterization,inspectionandtestarecommonacrossmanyAMactivities.Threecategoriesofdata-drivenAMengineeringdecisionsexist:AMprocessmonitoringandcontrol,AMpartqualificationanddevelopment,andAMprocess,machineandmaterialqualificationanddevelopment.Thelatterdecisionscanrelyonabroadspectrumofdatabeyondindividualstakeholder’sdatagenerationcapability.Forexample,in-processmonitoringdatageneratedforpartqualitycontrolcanbeusedformaterialandmachinepropertyunderstandingandreducetheneedforexperimentsbythematerialandmachinevendorsformaterialandmachinedevelopment.

Toenhancetheusabilityandreusabilityofthedatasets,metadata,“descriptionofdata”,mustbecollectedandaccessiblebyAMecosystemusers.TheAMmetadatashouldbeorganizedtosupporton-demanddatadiscoveryandretrieval,aswellasdataexchange.Forbigdata,especiallythosefromin-processmonitoringorstructureinspection,metadataisrequiredtosupportpartialdataretrieval,e.g.,locatingdatabasedonmaterial,process,partcriticalityand(in-process/NDT)datatype,retrievethein-processmonitoringdataforthefirst5layersandthelastfivelayersforaspecificpart.FivetypesofmetadataareidentifiedforAMbigdataobjects,includinggeneraldescription,activityrelatedinformation,instrumentrelatedinformationandmeasurementrelatedinformation.

2.6.BioPharma

Pharmaceuticalproductlifecycleincludesdrugdevelopment,clinicaltrial,regulatoryfiling,processdevelopment(scaleupanddrugproductdesign),production,anddisposal.Allthese

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lifecyclestagesgeneratealotofdata.Supplychain,logistics,andtraceabilityarealsoimportantactivitiesandcanalsogeneratemoredata.

Takingtheproductionlifecyclestageasanexample,thewholepharmaindustryismovingfrombatchtowardcontinuousmanufacturingbecauseofpotentialbenefitssuchassmallercapitalinvestment,smallerfootprint,easiertoscaleup,lowerriskofwaste,moresuitablefordistributedmanufacturing.However,theprocesscontrolincontinuousmanufacturingismorecomplexbecauseitneedsaholisticcontrolmodelinsteadofindividualcontrolforeachunitoperation.Thatmeansthenumberofprocessparametersisatleastamultiplicationofallunitoperationscombinedandusingafeedbackcontrolorobtainingamechanisticcontrolmodelisratherdifficult.Scientistshavetorelyonadata-drivenorhybridcontrolmodel.Whiledata-drivencontrolcanbeusedtoimproveperformanceinbatchmanufacturing,scientistsandprocessengineershavetostitchrelevantdatasetstogethertodevelopacontrolmodelforcontinuousmanufacturing.

Metadataareneededtohelpscientistsretrievedatasetsaboutthesameorsimilardrugsubstanceanddrugproductsandtheirrelatedbatchrunstocreateadata-drivencontrolmodel.Metadataisalsorequiredforscientiststoperformprocessscaleupeffectivelyaswellastofigureouthowdatafromsmallerscalescanbeutilizedtoimprove(train/pre-train)thecontrolmodel.Finally,metadataplaysapivotalroleforregulatoryfilingasithelpsensuretraceabilityandintegrityofthedatasets.Examplemetadataarebatchidentifier,drugsubstance,drugproduct,deviceid,timeoflastcalibration,referencematerialusedandbatchcontrolrecipe.Whilethebatchcontrolrecipeisthekeytootherdataelementsrelevanttoanalyticalmodeling,selecteddataelementsmaybehelpfulforscientiststofindandreusedatabetter.Examplesofthesedataelementsmaybethecriticalqualityattributes,criticalprocesscontrolparametersandkeyperformanceindicators.

Theexemplarymetadataelementsgivenaboveareessentialforprocessdevelopmentandmanufacturingcontrol.Whenconsideringotherlifecycleactivities,additionalmetadataelementsmaybeidentified.

2.7.Generaldata-drivenmanufacturingenterpriseapplications

Tosummarize,metadataanddataobjectsplayanimportantroleinindustrialdecisionmaking.Whiletheusecasesdescribedabovereflectvariouslevelsofuseofdigitalobjectmetadataformanufacturingenterpriseapplications,thefigurebelow,theCRISPframework,summarizestheroleofmetadataindata-drivenindustrialdecision-makingprocessesforsmartmanufacturing.

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Figure4:CRISPframeworkfordata-drivenbusinessapplications

[7]

InFigure4,amongthesixphasestacklingdata-drivenproblemsinvar