麦肯锡-科莱恩化工智慧生产研讨会 CPS workshop on Digital Manufacturing-20170720

CONFIDENTIALANDPROPRIETARYAnyuseofthismaterialwithoutspecificpermissionofMcKinsey&CompanyisstrictlyprohibitedMcKinseyDigitalIntroductiontoDigitalManufacturing/Industry4.0NilsMüller

|July20,2017AgendaforCPStrainingdayTimeModelfactoryinaBoxExperiencecurrentstateExercisesonprofitperhourtargetfunction,datacapturinganddecisiontreesExperienceimprovedfuturestateGroup1Group28:00-12:0012:00-12:30IntroductiontoDigitalManufacturingWhatisDigitalManufacturing?IntroductiontoitscoreelementsandtypicalimprovementleversChallengesinimplementingDigitalManufacturingIntroductiontoDigitalManufacturingWhatisDigitalManufacturing?IntroductiontoitscoreelementsandtypicalimprovementleversChallengesinimplementingDigitalManufacturingModelfactoryinaBoxExperiencecurrentstateExercisesonprofitperhourtargetfunction,datacapturinganddecisiontreesExperienceimprovedfuturestate12:30-16:30LunchAgendaforthissessionIntroductiontoDigitalManufacturing/Industry4.0–whatishappeningintheIndustryandwhydowethinkweshouldactCoreleversforChemicalscompanies–concreteusecasesBreakWhatdoesthismeanforCPS–howtomovefromLeantoDigitalLean:coreconceptofDigitalManufacturingDiagnosticOpenQ&A8:00-9:009:00-10:0010:00-10:1510:15-11:3011:30-12:00AgendaforthissessionIntroductiontoDigitalManufacturing/Industry4.0–whatishappeningintheIndustryandwhydowethinkweshouldactCoreleversforChemicalscompanies–concreteusecasesBreakWhatdoesthismeanforCPS–howtomovefromLeantoDigitalLean:coreconceptofDigitalManufacturingDiagnosticOpenQ&A12:30-13:3013:30-14:3014:30-14:4514:45-16:0016:00-16:30Whydowetalkabout“Digital”andIndustry4.0?

Pace&magnitudeoftechnologicalchangeisstaggeringTheaveragewashingmachinetodayhasmorecomputingpowerthanNASAusedinitsApollo11missionin1969Moreinformationiscreatedevery2days

thanfrom0AD-2003ADMoretextmessagesaresenteachdaythanthepopulationoftheplanet100hoursofvideoareuploadedeveryminute35%ofallphotostakenarepostedtoFacebook10yearsago,sequencingahumangenometook$50millionandseveralyears;todayittakes<$10,000andafewdaysSOURCE:McKinseyOurdefinitionofIndustry4.0SOURCE:McKinseyQuarterly:TheInternetofThings(2010);McKinseyIndustry4.0

TheapplicationoftheInternetofThings(IoT)intraditionalindustries:

sensorsineverything,networkseverywhere,analyzeeverythingTheIoT1SensorsandactuatorsembeddedinphysicalobjectsLinkedthroughwired/wirelessnetworksCollectionofhugevolumesofdatathroughnetworksforanalysisObjectscansensetheenvironmentandcommunicate,thusbecomingtoolsforunderstandingcomplexityandrespondingtoit1AsdefinedbyBosch(foundingmemberoftheIndustry4.0platform,aninitiativeacrossindustryassociations),"theIoTisthenextgenerationoftheInternet.ItisaglobalsystemofIP-connectedcomputernetworks,sensors,actuators,machines,anddevices.MergingthisphysicalworldwiththevirtualworldoftheInternetandsoftwareenablescompaniesandconsumerstocreateandenjoynewservicesthatarefoundedonWeb-basedbusinessmodels.Thiswillhaveabigimpactonthewaywedobusiness."ManufacturingalreadygeneratesmoredatathananyothersectorPetabytesConstructionConsumerandRecreationalServicesResourceIndustriesUtilitiesWholesaleTransportationInsuranceEducationHealthcareSecuritiesandInvestmentServicesManufacturingGovernmentBankingCommunicationsandMediaRetailProfessionalServicesSOURCE:IDC;McKinseyGlobalInstituteanalysis1Discretemanufacturingconstitutes1072petabytes;Processmanufacturing740petabytesAnnualnewdatastoredbysector,2010Industry4.0isoftencalledthe4thIndustrialRevolutiondramaticallyshapingIndustryandourwaystoproduceSOURCE:StatistischesBundesamt;DeutscheBundesbank;Prognos;ThomasNipperdey;McKinsey1strevolution

2ndrevolution

4threvolution

3rd

revolution

Industry4.0isoftencalledthe4thIndustrialRevolutiondramaticallyshapingIndustryandourwaystoproduceSOURCE:StatistischesBundesamt;DeutscheBundesbank;Prognos;ThomasNipperdey;McKinsey1strevolution

(Water/Steam)2ndrevolution(Electricity)4threvolution

(Cyberphysicalsystems)Percent

ofinstalledbase100~10-20~30-50~80-90ReplacementofequipmentReplacement

ofcompleteloomnecessaryLittlereplacement,

astoolingequipmentcouldbekept,onlyconveyorbeltneededExistingmachineswillbeconnected,onlypartialreplacementofequipmentHighlevelofreplace-mentastoolingequipmentwasreplacedbymachines3rd

revolution

(Automation)From……to…Industry4.0isstillsomewhathypedinthemedia,long-termimplicationsandimpactstillnotfullyappreciatedSOURCE:McKinseyIndustry4.0GlobalExpertSurvey2015,1GoogleTrendsgivesestimateaboutsearchtrends(numberofqueriesforkeyword)/(totalgooglesearchqueries)8030701005040206010900201120132015GoogleTrendsgraphfor“Industrie4.0”inGermany1

RelativepercentageInterestonIndustry4.0asatopicisincreasedoverlastyearsShareof"Industrie4.0"(I4.0)queriesrelatedtototalsearchqueriessteadilyincreasingLong-termpotentialandimplicationsofI4.0effortsonplantsstillunder-appreciated2012201420172016Industry4.0disruptstheindustrialvaluechainandrequirescompaniestorethinktheirwayofdoingbusinessSOURCE:McKinseyDisruptive

technologiesTransformintoadigitalcompanyReach

nexthorizonofoperationaleffectivenessAdaptbusinessmodelstocaptureshiftingvalue

poolsDisruptive

technologiesTransformintoadigitalcompanyReach

nexthorizonofoperationaleffectivenessAdaptbusinessmodelstocaptureshiftingvalue

poolsIndustry4.0disruptstheindustrialvaluechainandrequirescompaniestorethinktheirwayofdoingbusinessSOURCE:McKinseyIndustry4.0:Disruptivetechnologiesthatwillchangethemanufacturingsectorbetweentodayand2025Industry4.0Analyticsand

intelligenceConversiontophysicalworldData,computationalpowerandconnectivityHumanmachineinteractionBigdata/opendataInternetofThings/Machine-tomachineCloudtechnologyTouchinterfacesandnext-levelgraphicaluserinterfacesVirtualandaugmentedrealityDigitizationandautomationofknowledgeworkAdvancedanalyticsAdditivemanufacturing

(i.e.,3DPrinting)Advancedrobotics(e.g.,human-robotcollaboration)EnergystorageandharvestingDISRUPTIVETECHNOLOGIESSOURCE:McKinseyTechnologicallimitationshavefinallybeenovercome–nowisthetimeforIndustry4.0LPWA1technologiesprovidewirelessinfrastructuretoconnectthousandsofIoTnodesPricesforIoThardwareexpectedtobeaslowasUSD1perIoTnodeinthenearfutureConnectivityAffordabilityInteroperability1Lowpowerwidearea

2Machine-to-machineCommunicationprotocolsespeciallydesignedforseam-lessM2M2inter-

actionhavebeendevelopedSOURCE:McKinseyDISRUPTIVETECHNOLOGIES1mbps10kbps1gbps100kbps100mbps10mbps100km1kbps100bps1km10km100m10mDatarate,logscale

Range,logscale

Nowisthetime–newwirelesstechnologiesprovideLPWAinfrastructure(802.11n)1IPv6overLowpowerWirelessPersonalAreaNetworksCurrentwirelessconnectivitytechnologiesCurrentlyavailableconnectivitystandardsrepresentatrade-

offbetweenrangeandtrans-missionrateManystandardsforlow-rangeconnectivityavailable;someopenstandardslike6LoWPAN1

makingafurthersteptowardsconnectingdevicesacrossdifferentnetworktypes(e.g.,integratingWi-Ficlientswith802.15.4-baseddevices)Justrecently,newtechnolo-gieswithultra-widerangesandlowdatarateshavebeenintroduced–thesetechnologiesareveryenergy-efficient

(IoTnodescanlastforyearswiththesamebatterypack)Keyinsightsandlearnings231123802.15.4SOURCE:McKinseyDISRUPTIVETECHNOLOGIESIndustry4.0quiz

Yourturn–1voteperquestion1GBstoragecostsonaverageUSD0.03.

Whatusedtobethepricein1992?YourvoteUSD1,000USD5,000USD10,000USD20,0001234DISRUPTIVETECHNOLOGIESSOURCE:McKinseySignificantdecreaseofcostfordatastorage,

computationandtransmissionSOURCE:DeloitteUniversityPress,,/users/hpm/book97/ch3/processor.list.txt,/internet-of-things-hardware,/product/CC3100/description;McKinseyStorageComputationConnection$perGB$per1milliontransistors$permbps10,000.001.0010.001,000.00100.000.100.012015101992200010,000.01,000.0100.010.01.00.120151020001.0000.1000.0100.0011,000.000100.00010.00020151020001992$222

$0.01$10,000

$0.03$1,200

$0.63DISRUPTIVETECHNOLOGIESCostofInternetofThingsnodeshascomedowndramatically,andisexpectedtofallstillfurtherOther4

~1.01.0-2.0-50%Sensor3

2020E52015MCU1

Connectivity2~1.02.5-4.00.3-1.00.1-0.8Unitprice,USDNosignificantcostsassociatedwithInternetofThingsconnectivityanymorePricesexpectedtocontinuetofalloverthenextfewyearsAdditionalcostsavingpotentialfromfutureintegrateddesignsolutionsCalculationdoesnotincludefixedcostssuchascostsforinfrastructure1CurrentpricesrangefromUSD0.3(e.g.,Cypress32-bit)toUSD1.2(e.g.,TI16-bit)dependentonspeed,quality,andintegratedmemorysize(rangesforlargerordervolumes)2Combinationoffiltertransceiverandantenna–additionalcostsforswitchesandamplifiersnotincluded3Forexample,temperature,position,pressure,gyroscope...4AdditionalcomponentslikeADCconverters,power-managementconverters,capacitors,resistor,fuse,PCB(listnotexhaustive)52020pricesestimatedbyinflatingcurrentpriceswithaCAGRof-15%p.a.SOURCE:;expertopinionDISRUPTIVETECHNOLOGIESArtificialintelligencesystemsalreadyautomate

tasks

thatusedtorequirehighlytrainedexpertsIBMWatson,

oncologyadvisorAragoAutopilotfor

ITservicemanagementSeveralUShospitalsuseWatsontoderiverecommendationsforindividualizedtreatmentplansforcancerpatientsSystemhasbeen"trained"withmillionsofmedicalresearcharticles,clinicaltrialreports,patienthistories,andfeedbackonproposedsolutionsfromspecialistdoctorsGoalistousesystemtobringleading-edgecancertherapiestocommunitysettingswithlimitedaccesstohigh-qualitymedicalcareSystemautomatesITservicemanage-ment(e.g.,ITILincident,problem,changemanagement)Algorithmsdonotexecutestaticscriptsbutdynamicallycombine"knowledgemodules"tohandlenewsituationsandlearnfromtheoutcomesSoftwareenablesaverageautomationratesof~90%leadingtoaverage

costreductionsof~30%(externalassessmentbyGartner)aswellasperformanceimprovementsSOURCE:McKinseyDISRUPTIVETECHNOLOGIESNewformsofhumanmachineinteractioncanfurther

optimizeproductionprocessesSOURCE:Festo;Microsoft;UbimaxDescriptionPossibleIndustry4.0applicationExoskeletonsFestoExoHandExoskeletonemulatesphysiologyofhumanhandCansupportstrainingmanualmovements(wornasglove)andtransmithumanhandmovementstorobothandAccelerationofprocessesthatrequirestrainingmanualworkbyenablingworkerstodothemfasterandmoreoftenEnablingofremotehandlingofdangerousgoodsGesturerecognitionMicrosoftKinectInputdeviceforWindowsPCsenablesgesture,facial,andvoicerecognitionDocumentationofcomponentqualityflawsbypointingatanon-screen3-DrepresentationAugmentedrealityUbimaxappsonGoogleGlassApplicationsonGoogleGlassshowlocation-basedinstructionstoworkers(e.g.,directionswheretogo,howtocompleteatask)Moreefficientwarehouse/assembly/serviceprocessesVirtualtrainingofworkersRemoteassistancewithplantmaintenanceDISRUPTIVETECHNOLOGIES3Dprintingnowadaysnotonlypossibleforpolymers

andmetals,butalsoceramicsSOURCE:3,3DISRUPTIVETECHNOLOGIESCERAMICSEXAMPLESSignificanttechnologicaladvancesin3Dprintingalreadyachievedwithinthelast25yearsSOURCE:WohlersReport;McKinseyResearch;McKinsey1Overallcostsincludingenergyandfacilities,maintenance,labor,machine,andmaterials 2ExemplarycalculationforDMLStechnology 3BasedonSLSSinterstation2000for1990and3DSsPro230HSfor2014;however,highdependenceonexactpartthatisbeingprinted41988and2012datapointsforIndustrialAMprintersMaterialsTypeMaximumsize3m3Laserpower3WattSoldindustrialprinters4Numberp.a.Manufacturersof3Dprinters4Number3Dprinting1EURperpart2Maximumspeed3cm3perhr1990sToday(2014)Polymers

andmetalsAdditionally,glass,biocells,sugar,cement~0.03~0.23~50~200~30~9,800<5~40~12.0~5.0~1,600~4,900>+1,000%+300%>+1,000%>+1,000%-60%+200%DISRUPTIVETECHNOLOGIESFirstHRCapplicationshavebeensuccessfully

introducedatautomotiveOEMsSOURCE:McKinsey,companyhomepageAudi,Ingolstadt1

Pickingandhandoverofcoolantexpansionreservoirfromlarge-loadcarrierAssemblyandinstallationcoolantexpansionreservoirbyoperator(handlingofodd-shapedpartsstillrequired)ImprovedergonomicsandreducedlevelsofworkerfatigueApplicationhassuccessfullygonethroughcertificationfromtheOEMsliabilityinsuranceRobotsupportsoperatorindoorassemblylineRobothandlespositioningandpressingofdoorsealswhichrequiresprecision,highforceandconstantpressureUpto70%offloorspacesavingsiminassembly-nearareasbyavoidingperiphericsafetyshieldsandbarriersBMW,Spartanburg21Finalassembly,AudiIngolstadtplant2Doorassemblyline,BMW,SpartanburgplantDISRUPTIVETECHNOLOGIESPaceofchangewillbeslowercomparedtotheconsumerInternetduetolargedownsiderisksincaseoffailure…SOURCE:Pressclippings1http:///companystory/downtime-costs-auto-industry-22k-minute-survey-4810172

http://www.vdi.de/artikel/gute-perspektiven-fuer-standort-deutschland-durch-industrie-40;onlytheofficiallyreportedcases–realdamageisexpectedtobebigger3http:///2014/12/31/business/a-year-of-record-recalls-galvanizes-auto-industry-into-action.html?_r=2CybersecurityriskEUR50bnAnnualdamage

totheGermanmanufacturingindustrycaused

bycyberattacks2Numberofcarsthatwererecalledin2014throughouttheUS3

60mQualitylossriskCostsintheautomotiveindustryperday1–weighrisksofintroductionofnewtechnologyagainstprocessreliabilityProductiondowntimeriskEUR28mDISRUPTIVETECHNOLOGIES<1,89,56,0<12,015,0…andduetosignificantlylongerinvestmentcyclesAverageusageperioduntilreplacement,yearsSOURCE:ReconAnalytics;Siemens;USInternalRevenueServiceManufacturingequipmentSteelAutomotiveChemicalsElectronicsSmartphoneDISRUPTIVETECHNOLOGIESIndustry4.0disruptstheindustrialvaluechainandrequirescompaniestorethinktheirwayofdoingbusinessOPERATIONALEFFECTIVENESSSOURCE:McKinseyTransformintoadigitalcompanyAdaptbusinessmodelstocaptureshiftingvalue

poolsReachnext

horizonofoperationaleffectivenessDisruptive

technologiesFromabaseof30,000datatags,closetozerotagsareusedtoinformoperationaldecisionsInacaseexample,99%ofalldatafromanoilrigwaslostbeforereachingoperationaldecisionmakers~30,000tagsmeasuredCommentPeopleandprocessesSchedulepredominantlybasedonOEM-recommendedmaintenanceintervalsDatamanagementDatacannotbeaccessedinrealtime,enablingonlyadhocanalysisInfrastructureOnly~1%canbestreamedonshorefordaytodayuseData

capture~40%ofalldataisneverstored–remainderisstoredlocallyoffshoreDeploymentNointerfaceinplacetoenablerealtimeanalyticsto"reach"offshoreAnalyticsReportinglimitedtoafewKPIswhicharemonitoredinretrospect0%~1%60%100%<1%<1%SOURCE:McKinseyOPERATIONALEFFECTIVENESSILLUSTRATIVESOURCE:McKinseyBeforeIndustry4.0OPERATIONALEFFECTIVENESSManualcheckingofbearing;replaceevery30daysregardlessofconditionWiredcommunicationwithcontrolcenterExactyieldpercoilunknownIdentifieddefectcreatedbyPaperMachineincardboardhastobescrapedIndividualcontrolroomforspecificmachine;novisibilitytoup/downstreamprocessesExcessiveWIP;notrackingsystemmakesiteasierforcoilstogetlostSignificantexcesslabormanagingunoptimizedflowpathandhardtolocateinventoryUnoptimizedpreventivemaintenanceschedulewithallpartschangedonsettimesILLUSTRATIVEBeforeIndustry4.0SOURCE:McKinseyOPERATIONALEFFECTIVENESSAfterIndustry4.0transformationSOURCE:McKinseyILLUSTRATIVEOPERATIONALEFFECTIVENESSAGVsimproveslaborefficiencyandprocessingtimeformaterialhandlingReducemachinedowntimeControlsmanymachinesMonitorsqualityandcomponentperformanceinadditiontothroughputUsesadvancedanalyticstoupdateparametersinrealtimetoimprovequalityandyieldPiezoElectricSensormeasuringvibrationonbearing;onlyreplacedifconditionrequiresImproveyieldImprovequalityIncreaselaborefficiencyCommonoperatingpictureBatchmatchingtodemandPreventiveMaintenanceSpectrometerQualityissuesidentifiedinrealtimewithdatarelayedtocontrolroomforadvancedanalyticsandparameteradjustmentYieldinputandoutputdatarelayedtocontrolroomforadvancedanalyticsandparameteradjustmentRFIDtagsoncoilsallowpreciselocationandtriggerautomaticKanbanwhenstockisdepletedWirelesscommunicationwithcontrolcenterOptimizedpreventivemaintenanceschedulebasedonrealtimecomponentmonitoringDigitizeperformancemanagementthroughrealtimedataandalarmsCommonoperatingpictureIncreaselaborefficiencyAfterIndustry4.0transformationILLUSTRATIVESOURCE:McKinseyOPERATIONALEFFECTIVENESSSOURCE:I,Steuler-ab.de(picture)Real-timeprocessadaptation:Productivityincreasethroughlimekilnmid-zonetemperaturemonitoring/adjustmentbasedonsophisticateddataanalyticsSensing–sensorsinakiln’smid-zonemonitorlimemudtemperature,aleadingindicationofcalcinationDataaggregation&analysis–temperaturereadingsarecombinedtosimulatetheheatprofileofthekilnDecisionmaking&actuation-basedontheinferredheatprofile,theshapeandintensityoftheflamedrivingheatthroughthekilnisoptimizedPULP&PAPERINDUSTRYImpact: 6%fuelsavings

16%limethroughputincreaseOPERATIONALEFFECTIVENESSBoschusesSICK'sRFIDtechnologytoenableautonomoustransportsystemsSOURCE:SICKinsightmagazine,July2014;

McKinsey1Radio-frequencyidentification 2MethodtomanageproductionprocesscontrolStartingpointRFIDsystemintroducedImprovementsAlldataregardinggoodsflowscollectedmanuallybyfillingoutpapercardsandenteringinforma-tionintoITsystemApproachhighlyerror-proneandasynchronous–informationflowlaggingbehindphysicalgoodsflowGoodsandtransportcontainersallequippedwithRFID1

trans-pondersthatcanbetrackedinrealtimeviaRFIDkanban2

systemIndividualobjectscanbeunambiguouslyidentifiedWheneveraunitisremovedfromthewarehouse,theRFIDsystemautomaticallytransfersinformationtotheSAPsystemAssoonasminimuminventorylevelisreached,pullsignalistriggeredtorefillstockAvailabilityofdataenablesinteractionwithcustomersandsuppliersforend-to-endprocessoptimizationImpact"Theproductionprocessisimprovingallonitsown.Newdataleadstonewknowledge.Newknowledgeleadstoimprovementsinthesystem."(Boschprojectmanager)OPERATIONALEFFECTIVENESSCondition-basedmaintenance:Decisionsupportcentertomonitorandidentifyearlywarningsonrotatingequipmenton~200platforms100%0%EquipmentconditionEquip-

mentlife

EventsandminordamagePotentialFailure

damagethatneedsrepairOperationsrunningwithoutproblemsAlert,e.g.fromvibrationorbearingtemp.FunctionalFailureInputfromequipmentPredictiveanalyticsEngineeringanalysisReportandrecom-mendationsTool1Tool2Tool3PatternrecognitionTagsfromcurrentmachinerycanbeusedtotellanomaliesPatternrecognitionisdoneagnostictovendorAnalytictoolsareusedtoidentifyrootcausestoanomaliesRecommen-dationsIncreasedmaintenanceplanningandrepairtimeMachinerytags(speed,current,etc.)VibrationsensorsSOURCE:McKinseyOPERATIONALEFFECTIVENESSEnablesworkers

tolocateproductsfasterandmoreprecisely,scansproductsautomaticallyGivesworkersexactinstructionshowandwheretostackproductsforpalletbuildingoptimizationKnapp'sKiSoftVisionguideswarehouseworkerswithvisualpromptsHelpsworkersoptimizecubestackingandensuresecurelocationsforfragileitemsSOURCE:;McKinseyKnappAGhasdevelopedaugmentedrealityglasses

toincreasetheefficiencyofwarehouseworkersOPERATIONALEFFECTIVENESSThewarehouseof2020willbehighlyautomatedSwarmAGVrobotsprovidingefficientgoods-to-manFlexiblemanagementofnumberofshelfsRandomlocationstrategyAdvancedsortingsystemwithopticalrecognitionofproductsPickingrobotforsingleitempickingAssistedmanualpickingsystem,e.g.man-to-goodsviaSwarmAGVs,smartglasses,…AGVconnectingconveyorbeltwithpickingareaHighspeed,highcapacitymulti-shuttlesystemShuttleabletoleaverackandoperateasAGVWMSautonomouslymanageinventory,real-timeconnectiontoorderingsystemAnalyticstoolsincreasingperformanceOPERATIONALEFFECTIVENESSSOURCE:McKinseyAmazonDistributioncenterPostalhub(e.g.DHL)CustomerAddressinformationOrderDeliver‘untagged’tohubFilloutpartialstreetaddressesorzipcodestogetitemsclosertowherecustomersneedthem,andlatercompletethelabelintransitAmazonplanstoshipbasedonpreviousorders/otherfactors.ParcelswaitathubsorontrucksuntilanorderarrivesAmazonmightsuggestitemsalreadyintransittocustomersusingitswebsitetoensuretheyaredeliveredAmazon’salgorithmsmightcauseerrors,promptingcostlyreturns.Tominimizethosecosts,Amazonsaiditmightconsidergivingcustomersdiscounts,orconverttheunwanteddeliveryintoafreegiftPredictiveShippingPatentShopping-cartcontentsReturnsTimeofcursorhoveringPreviousordersProductsearchesWishlistsSourcesofPredictionsTendertocommoncarrieratfulfillmentcenterSOURCE:McKinseyCustomerExperiencePractice,A,BBC,WSJCan’tweleveragetheinformationtoshipBEFOREthe

customerhasordered?Amazon's“predictiveshipping”doesitOPERATIONALEFFECTIVENESSIndustry4.0disruptstheindustrialvaluechainandrequirescompaniestorethinktheirwayofdoingbusinessSOURCE:McKinseyNEWBUSINESSMODELSDisruptive

technologiesTransformintoadigitalcompanyReachnext

horizonofoperationaleffectivenessAdaptbusinessmodelstocaptureshiftingvalue

poolsThereare4maintrendsregardingnewbusinessmodelsthatexploitopportunitiesSOURCE:McKinsey1 Intellectualpropertyrights

PlatformsProvisioningofTechnologyplatforms:ecosystemsfordevelopersbasedonopensystemsBrokerplatforms:industrialspotmarketsthatconnectthirdparties(e.g.,forexcessproductioncapacity)Data-drivenbusinessmodelsUsageof(crowd-sourced)dataforDirectmonetizationofcollecteddatainsteadofprimaryproduct(e.g.,Google)Indirectmonetizationofinsightsfromcollecteddata

(e.g.,micro-segmentationforpricingorcustomization)Pay-by-usage/subscription-basedmodels

formachineryNewpaymentmodelstransformCapex

intoOpexformanufacturersPerpetuationofrevenuestreamsinsteadofone-offassetsaleforsuppliersAs-a-servicebusinessmodelsIPR1-basedbusinessmodelsIPR-basedservicesRecurringrevenuemodels(e.g.,licensingfeesfordatastandards)Add-onservicesforprimaryproducts(e.g.,consultingonbestusageofproducts)NEWBUSINESSMODELSExampleJohnDeere:frommanufacturingtractorstoofferingsophisticatedonlineservicesforfarmersJohnDeere:theymaketractors,right?NowusesensorsaddedtotheirlatestequipmenttohelpfarmersmanagetheirfleetandtodecreasedowntimeoftheirtractorsaswellassaveonfuelTheinformationiscombinedwithhistoricalandreal-timedataregardingweatherprediction,soilconditions,cropfeaturesandmanyotherdatasetsTheinformationispresentedintheMyJohnDplatformaswellasontheiPadandiPhoneappMobileFarmManagerinordertohelperfarmersfigureoutwhichcropstoplantwhereandwhen,whenandwheretoplough,wherethebestreturnwillbemadewiththecropsandevenwhichpathtofollowwhenploughingSOURCE:JohnDeerestatements;DataFloqNEWBUSINESSMODELS–IPR-BASEDBUSINESSMODELSSOURCE:Rolls-Royceannualreports;Rolls-R;McKinseyRolls-Royceoffersfullafter-salesservice

modelbasedonpredictivemaintenanceNEWBUSINESSMODELS–PAYBYUSAGERatherthansellingturbinestocustomers,Rolls-Roycenowrentsthemoutona"timeonwing"basisaspartoftheirTotalCareofferingBefore:servicerepairsduringenginedowntimemeantrevenuesforOEMNow:

OEMtoensureengineavailability,customerspayforuptimeonly–allrisksassociatedwithengineaftercarestaywithOEMHow:newbusinessmodelenabledbyadvancedbigdatacapabilitiessinceRolls-Roycecanaccuratelypredictenginefailuresseveraldaysbeforetheyoccur(predictivemaintenance)Result:

improvedsafety,improvedcustomerservice,andlowerservicecostsImpactRolls-RoyceexpectstheshareofLTSAs1,includingTotalCare,torisefrom73%oftheirinstalledfleetin2012toover90%overthenextdecade1 LongTermServiceAgreementDetails–SCIO,acrowd-sourcedapproach

tospectroscopySOURCE:McKinseyNEWBUSINESSMODELS–DATADRIVENTypicalspectrometerScientificapplication–mainlyusedinphysicsandchemistryapplicationsIsoftenthesizeofalaptopTypicallypricedatUSD10,000andaboveConsumerapplication–afterscanninganobject,theuserre