D122.1 Experiment description and evaluation · 2017. 5. 11. · optimization is run in order to...

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EXPERIMENTDESCRIPTIONANDEVALUATION

DeliverableD122.1

Circulation: PU:PublicLeadpartner: TTSContributingpartners: SUPSI,FICEPAuthors: Paolo Pedrazzoli, Diego Rovere,

GiovannidalMaso.QualityControllers: AndreStorkVersion: 1.0Date: 18.05.2016

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©Copyright2013-2016:TheCloudFlowConsortium

Consistingoforiginalpartners

Fraunhofer FraunhoferInstituteforComputerGraphicsResearch,Darmstadt,GermanySINTEF STIFTELSENSINTEF,DepartmentofAppliedMathematics,Oslo,NorwayJOTNE JOTNEEPMTECHNOLOGYASDFKI DEUTSCHESFORSCHUNGSZENTRUMFUERKUENSTLICHE

INTELLIGENZGMBHUNott THEUNIVERSITYOFNOTTINGHAMCARSA CONSULTORESDEAUTOMATIZACIONYROBOTICAS.A.NUMECA NUMERICALMECHANICSAPPLICATIONSINTERNATIONALSAITI ITIGESELLSCHAFTFURINGENIEURTECHNISCHE

INFORMATIONSVERARBEITUNGMBHMissler MisslerSoftwareARCTUR ARCTURRACUNALNISKIINZENIRINGDOOStellba STELLBAHYDROGMBH&COKG

ESS EUROPEANSENSORSYSTEMSSAHELIC HELICELLINIKAOLOKLIROMENAKYKLOMATAA.E.ATHENARC ATHENARESEARCHAND INNOVATIONCENTER IN INFORMATIONCOMMUNICA-

TION&KNOWLEDGETECHNOLOGIESINT INTROSYS-INTEGRATIONFORROBOTICSYSTEMS-INTEGRACAODESISTEMASRO-

BOTICOSSASIMPLAN SIMPLANAGUNIKASSEL UNIVERSITAETKASSELBOGE BOGEKOMPRESSORENOTTOBOGEGMBH&COKGCAPVIDIA CAPVIDIANVSES-TEC SES-TECOGAVL AVLLISTGMBHnablaDot NABLADOTSLBiocurve BIOCURVEUNIZAR UNIVERSIDADDEZARAGOZABTECH BARCELONATECHNICALCENTERSLCSUC CONSORCIDESERVEISUNIVERSITARISDECATALUNYATTS TECHNOLOGYTRANSFERSYSTEMS.R.L.FICEP FICEPS.P.A.SUPSI SCUOLAUNIVERSITARIAPROFESSIONALEDELLASVIZZERAITALIANA(SUPSI)

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DOCUMENTHISTORYVersion1 IssueDate Stage ContentandChanges

1.0 18/05/2016 100% FinalversiontobesubmittedtoProjectOfficer

1Integerscorrespondtosubmittedversions

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EXECUTIVESUMMARY

The current process of designing a new steel fabrication plant encompasses several steps and in-volvespersonnelwithdifferentcompetencies.Atthebeginning,thecommercialcrewinteractswiththecustomer,inordertogathertheplantrequirements.Subsequently,thetechnicalteampreparesafirstdraftplantlayout,basedbothonthecustomerrequirementsandonthepreviousexperiencesrelatedtoverywell-knownplantlayouttemplates.Basedonatypicalproductionmix,aproductionoptimizationisruninordertoverifytheexpectedplantperformance.Sincetheneededsimulationandoptimizationtoolsrequirepowerfulhardware, this taskhastobedonebythetechnicalofficeandcannotbeexecutedatthecustomerpremises.Therefore,avideoofthesimulationrunisrec-ordedbythetechnicalofficeandsenttothecustomer.Bywatchingthevideo,thecustomerdevel-opsabetterunderstandingoftheprocessand,typically,hewantstoapplyseveralchangestothelayoutor to themachinesused.Thosechangesaresentback to the technical team,whereanewsimulationandoptimizationtask isexecutedandanewvideo isgenerated.This loop isusuallyre-peateduntiltherequiredlevelofmaturityofthesolutionisachieved.

Thus,theexistingprocessistimeconsumingandinefficientbecauseofthemanyiterations,whereonlythetechnicalteamcanrunsimulationsandassesstheplantproductivity,usingdedicatedwork-stations.Eachproductionoptimizationofatypicalplantofmediumcomplexity(composedof4ma-chiningstations,2 loadingbays,2unloadingbaysandtheautomatichandlingsystem)requiresap-proximately8minutesonahigh-end,8coresdesktopPC,whileitrequires30minutesonanormallaptop.Clearly,a30minuteswindowforeachoptimization isprohibitive inanegotiationwiththecustomer.Eachmonth,atleast10requestsforearlydesignmodificationsandsimulationaresenttothetechnicaloffice,tostartandcarryonthenegotiationphaseandthementionediterations(withintheaverageof20newnegotiationsperyear).

Thegoalsoftheexperiment

Theexperiment ismeanttooptimizethisprocessandtoenablequickerandfastersimulationandoptimizationevenatthecustomers’site.Thisvisionrequirestheimplementationoftwocould-basedservicestosimulateandoptimizetheproductionofacomplexmanufacturingsystem,composedofseveralmachines and conveyors. The two services are coupledwith a client applicationmeant tostreamline the access and the steps required to successfully simulate and optimize a productionplant(i.e.uploadofthesimulationmodel,customizationofthelayout,selectionoftheproductionmixandvisualizationoftheresults).Thetechnicalobjectiveofthisexperimentisthustoprovidethefunctionalities of the simulation and optimization tools as cloud-based HPC services, in order toachievethemainbusinessgoal toempowerawiderrangeofuser (i.e. thecommercialcrew)withquickersimulationandoptimizationsolutionstobedeployedatthecustomer’spremises.

Technicalimpact

The implementationof theexperimentwas successful in reducing the timeneeded toperformanoptimization for a layout ofmedium complexity from 30minutes to approximately 3minutes onportabledevices,blowingaway thebarrier thatmade impractical theuseof such toolsduring thenegotiationphase,at thecustomer’spremises.Modificationscannowbeappliedshowingdirectlythe effects of the changes, streamlining the interaction towards the best configuration.With theachieved implementationof theexperiment, severaldirecteconomicbenefitsareexpectedoverashorttomid-termperiod.

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Economicimpact

FICEPbenefitsfromamoreefficientproposalphaseduetothestreamlinedinteractionbetweenthetechnical teamandthecommercialcrew,nowendowedbyquick-everywheresimulationandopti-mization capabilities (for the average plant aforementioned, the number of iterations is reducedfromaminimumof6–whereeachiterationtakes4man-days–to2,quantifiablein4800€savings,nottakingintoaccounttheimprovedqualityoftheserviceoffered).Takingintoaccountthenumberof negotiationsprocesses initiatedper year,whichwere estimatedbefore in 20negotiations, thisleadtoanestimationof96.000€/yearsavings.Furthermore,collaborationbetweendifferentFICEPteams locatedworldwide isboosted,as theresultsofdifferent layoutsimulationarestored in thecloud,furtherincreasingthecapabilitytoproperlyaddressthecustomer’sneeds.

The cloud-based configuration also allowedTTS todevelop anewbusiness (andpricing)model: amonthly100€feeinapay-per-usemodelallowstoreachawidernumberofSMEshavingalimitedexpenditure capacitybut a strongnecessityof simulation functionalities especially during thema-chinedesignphase. These companieswouldbenefit fromausageof theplatformpurchasedas aserviceondemand.SuchSMEsusuallyoperateinnichemarketsprovidingspecialityhigh-performingmachinesinsmall(alsoone-of)lots.ThiswillresultinanincreasednumberofactivecustomersforTTS,withmorethan20additionalmachinemanufacturersusingTTScloud-basedsolutions,resultingin80.000€ofadditionalsalesovera3yearstimehorizonstartingfromtheprojectconclusion,withthecreationoftwonewjobsoverthesametimeperiod.

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TABLEOFCONTENTS

Executivesummary................................................................................................................................41 Descriptionofthecurrentengineeringandmanufacturingprocess(PU).....................................72 DescriptionoftheengineeringandmanufacturingprocessbasedonCloudservices(PU)...........83 Lessonslearned(PU)....................................................................................................................134 Impact(PU)...................................................................................................................................145 BusinessModel(CO)....................................................................................................................156 ExecutionoftheExperiment(CO)................................................................................................187 RecommendationtotheCloudFlowinfrastructure(CO).............................................................198 Confidentialinformation(CO)......................................................................................................199 InvolvedOrganisations.................................................................................................................20Appendix1:Userrequirementsandhowtheyaremet.......................................................................22Appendix2:UsabilityEvaluation..........................................................................................................24

EvaluationDetails-Processoverview...............................................................................................24Issue122.1:Userguidanceismissing..........................................................................................28Issue122.2:Availablefunctionalityshouldmatchuserrole........................................................28Issue122.3:Confusionoverlocalvs.cloud-basedoperations.....................................................28Issue122.4:Noindicationthetimetorunthesimulationoroptimisation.................................29

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1 DESCRIPTIONOFTHECURRENTENGINEERINGANDMANU-FACTURINGPROCESS(PU)

FICEP, the end user within this experiment, is one of the most important steel-fabrication plantbuildersintheworld.Asteel-fabricationplantisahugefactorycomposedofseveralmachinetoolsto shot blast, cut, drill and paint pieces of steel bars, that are employed to build frameworks ofbridgesandbuildings,orsupportsforoilpipelines.All thesemachiningcentresare interconnectedbyautomatichandlingsystemsthatmove,buffer, loadandunload ironbarsofseveral tons,whilekeepingtheproductionconstantlyundercontrol.

Thesalesphaseofthis“complexproduct”(i.e.thewholefactory)isalongandmultifacetedprocessthatinvolvesmanyactorswithdifferenttechnicalskills,fromthesalesoperativestothedesignde-partments, and it is organized in threemain activities: 1) negotiation, 2) early design and 3) ad-vanceddesign.

Inthenegotiationphase,thesalesagentmanuallycollectsthecustomer’sneedsandrequirementsconcerningthenewsteelfabricationsystem(suchastheparttypestobeproduced,expectedannualproduction volumes, needed manufacturing operations and the desired throughput). This infor-mationisoftenneitherstructurednorconsistentlyformalizedand,mostofthetimestheonlydoc-umentavailableindigitalformatisthe2Ddrawingofthebuildingthatwillhosttheplant.Thesedataaresenttothetechnicalofficethatstartstheearlydesignactivity,analysingthecollectedrequire-mentsinordertocreatefewsolutionsin2DusingtheSolidworksCADsystem.Thesefirstdraftlay-outsareprocessedusingTTSsimulationandoptimizationtools,toassesstheirperformanceagainstcustomer’s targets. In case of discordance, the needed changes are applied and simulations aremodifiedaccordingly.

Whenthedesignteamleaderapprovesasetofsolutions,theyaresentbacktothesalesagentwhodiscussesthemwiththecustomer,showingsimulationreportsandvideosoftheanimationsprovid-edbythesimulationtool.Thisiterationusuallyoriginatesnewrequirementsthat,inturnnecessitateanew interventionof thedesign teamthat,basedon feedback,applies theappropriatemodifica-tionstothesolutions,restartingtheloop.Typically,theactivitiesofnegotiationandearlydesigncanlastfromafewweekstosomemonths.

Oncethecustomeragreesonaproposedsolution,thedesignteamincollaborationwiththetech-nicaldepartmentcreatesamoreaccuratelayout,keepingintoconsiderationallthetechnicaldetails.Inthisadvanceddesignactivity,designersusetheplantsimulationinordertodefineandoptimizetheoperationalrulesofthefactory,suchastheloadbalancingandroutingrules.

Theevaluation,usingsimulation,of theexpectedthroughputof thedesignedplant isofparticularimportance,becausethedeclaredproductivitybecomesabindingclauseforFICEPtowardsitscus-tomer.

It isself-evident that thecurrentprocess is timeandresourceconsumingbecauseof theneedsofmany iterations,wherethedesigndepartment istheonlyonethatcanrunsimulationsandassesswith themtheoptimizedplantproductivityusingdedicatedworkstations. In fact,eachproductionoptimizationofatypicalplantofmediumcomplexity,composedof4machiningstations,2loadingbays,2unloadingbaysandtheautomatichandlingsystem,requiresapproximately8minutesonahigh-end, 8 cores desktopPC,while it requires 30minutes on a normal low-end laptop. For eachsingleplantdesignseveraloptimizationsmustberun, inordertoevaluatethe impactofusingdif-ferentmachinetoolsandconveyorsystemsduringtheearlydesignphaseandtheimpactofchang-

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ingdespatchingandloadbalancingrulesintheadvanceddesignphase.Thisimpliesthateachmonthat leastapproximately10 requests forearlydesignand simulationare sent to the technicalofficejusttostartanegotiationphase.

Moreover, the initialmanual collection of requirements rises significant additional burden on thedesigndepartment,whichhastodealwithseveralerrorsintroducedbythesalesagents,whooftendoesn’thaveenoughtechnicalknowledge(theycouldbemulti-vendoragents)orexperiencetocor-rectlyguidecustomerchoices.

2 DESCRIPTIONOFTHEENGINEERINGANDMANUFACTUR-INGPROCESSBASEDONCLOUDSERVICES(PU)

In this experiment, TTS adapted its simulation and production optimization engine to run in thecloud,centralizingboththecomputationalburdenandthesignificantamountofplantdataontheCloudFlow infrastructure and exposing the SW functionalities both to the CloudFlow Portal andthroughauser-friendlyapplicationfortabletdevices.

RelyingontheCloudFlowinfrastructure,nowthetechnicalofficestoresasetoflayoutmodeltem-platesthatrepresentthemostcommonsolutionsrequiredbythecustomersandasetofproductionplans that are representative of typical steel parts production scenarios (e.g. industrial buildings,single-storeybuildings,multi-storeybuildings,etc.).

Using the simulationworkflow, the salesagent,directlyat thecustomerpremises,andduring thefirstpre-salesmeeting,choosesthe layouttemplatefromthepublishedplantmodelsandthepro-duction mix that best fit the customer requirements and customizes the template by selectingamong the available options. Then, he runs the simulation and obtains a report containing infor-mationaboutroughlyexpectedplantperformanceandresourcesutilizationandoptionally,avisual3Danimationofthepartflowsthatheusestoimmediatelystartdiscussionwiththecustomer.Ifthecustomerisnotsatisfied,parameterscanbemodifiedandanewsimulationisrun.

Once the customer is satisfiedwith theplant configuration, the sales agent runs theoptimizationworkflowand,withouttheneedofiteratingthroughtheFICEPtechnicaloffice,he-inafewminutes-receivesadetailedassessmentoftheoptimizedplantperformancethankstothefactthatoptimiza-tionisexecutedontheremoteHPCplatform.

Attheend,thefinalplantconfigurationisstoredontheCloudFlowstoragesystem,fromwherethetechnicalofficecandownloaditasbasisfortheadvanceddesignphase.

TTS implemented two cloud-based workflows that guide the user through the steps required toevaluate theperformanceof typicalproductionmixesondifferentplant layouts.Byexploiting theHPC resources in the cloud, the computations now are distributed onmany CPUs to significantlyreducetheprocessingtimetoapproximately3minutes(from30minutesonastandardlaptop)foratypicalplantofmediumcomplexity.

Additionally,thepossibilitytorunoptimizationandsimulationremotelyremovesthehardwarebar-riersthatpreventedtheadoptionofthesetoolsonlow-specsdevices,promotingitsuseatthecus-tomer premises, thus reducing significantly the amount of iterations through the technical office,shorteningthedurationofthenegotiationphaseandincreasingtheprobabilitytosuccessfullycon-cludeit.

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Moreover,theautomatizeddatamanagementsupportedbytheusageofsimpleandguidedwork-flows based on a cloud storage dramatically increases the quality of the requirements collection,reducingtheburdenonthedesignersthatwasintroducedbyhumanerrors.

Optimizationworkflow

Thedevelopedoptimizationworkflowconsistofthefollowingcloud-basedservicesandweb-basedapplications:

1. Parametereditorweb-basedapplication:editingoptimizationparameters.

1. cloud-based optimization service: service that optimizes a production plan on a para-metrizedplantmodel,executingtheTTSsimulationsoftwareontheHPCinfrastructure.

2. web-basedresultsviewerapplication:userinterfacethat,basedontheoptimizationserviceoutput,generatesthereportwiththemostrelevantKPIs fortheplant layoutandforeachresource.

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Simulationworkflow

Thedevelopedworkflowconsistsof thecloud-basedservicesandweb-basedapplications thataresimilartotheonesdescribedabove.

Additionally,thesimulationworkflowprovidesaweb-basedapplication,developedbySINTEF,thatallowsthevisualizationoftheanimationgeneratedbythesimulationservice.

Clientapplication

Inaddition to theweb-basedworkflows,a touch-friendly clientapplication, calledThinSimulationandOptimizationClient(ThinS&O),hasbeendevelopedinordertobeusedbysalesagentsontab-let devices at customer premises. Under the hood, the application uses the CloudFlowWorkflowManager SOAP interface to start one of the implementedworkflows (simulation or optimization)andtheCloudFlowGenericStorageService(GSS)touploadtheinputdataandtodownloadthere-sults.Theapplicationprovidesfourmainfunctionsthatcanbestartedfromtheinitialscreen:simu-late,optimize,compareandupload.

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The steps hereby reported describe how the client application supports and streamlines the newnegotiationprocess.

1. Thesalesagentselectsthe layouttemplatebasedonthecustomerrequirementsandcon-straintssuchasthetypeofbarstobeproduced,theneededfabricationtypes,theavailablespace,theannualvolume,thenumberofmachinetools,etc.

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2. Thesalesagent selects themachine typesand theirequipmentandaproductionmix thatbestrepresentsthecustomer’sproduction

3. Thesalesagentstartsproductionoptimizationandshowstheperformanceresults

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4. Iftheperformancematchesthecustomerneeds,thesalesagentcandecidetoshowthe3Dsimulationinordertomakethecustomerawareofthelayoutbehavior

5. Iftheperformancevaluesarelowerthanneeded,thesalesagentcanselectmoreperform-ingmachines (Step2)orhecanselectadifferent templateequippedwithmoremachinesthanthefirstsolution(Step1)andreiteratetheprocess.

6. Intheend,thesalesagentcollectsalltherequirements,theselectedsolution,thecustomerproductionmixandsendsallinformationtotheFICEPdesigndepartment.

7. Thedesigndepartmentcustomizes the layout, starting fromtheselectedtemplate, forex-ample: themachinespositions canbe changed to complywith thepillarsof the customerpremises,the lengthorthewidthofthecrosstransferdevicescanbechanged,andsoon.Thenthecustomerproductionmixisanalyzedandpost-processedtocreatetherightinputforthesimulationmodel.

8. The design department optimizes the solution and stores it into the cloud using the “Up-load”function.

9. The salesagent selects the solutionproposedby the technicalofficeusing the“Compare”functionandrunsthemodeltovisualizethefinalresult.

3 LESSONSLEARNED(PU)

FICEPdeveloped theawareness that cloud-basedapplicationsempower sales force,mainlyduringtheinitialphase,toshortenandstreamlinethesalesprocess,thankstoa“democratization”ofcom-putationalpower.AFICEPsalesagent,evenusingamobiledevice,cananalysedifferentlayoutsolu-tions, selecting themachine type closer to the customerneedsandhe/shecan simulatedifferentproductionmixes.Furthermore,themost importantkeyperformance indicatorsarevisualizedinagraphical,easytounderstandway, inordertofocusthediscussiononthemaintopicsandtocon-vergeon thebest solution for each FICEP customer. FICEP found also promising thepossibility toformalize and automatize in a simpleway the gathering of customer requirements, that now arecapturedwithwelldefinedparameters,andthusreducingthecostscausedbyhumanerrors.

TTS,asasoftwaredeveloperandvendor,considerstheavailabilityof theHPC infrastructureasanopportunity towidened the adoption of the simulation tools, because there are nomore specialhardware requirements for the user’s PC. Based on previous experience, simulationmodelswere

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oftentoobigforthespecificationsavailableand,inthecaseofoptimization,thecomputingpowerandnumberofcoreswerenotadequatetoobtainresultsinacceptabletime.Thankstotheexperi-ment,itwasalsopossibletogainexperienceincloud-basedservicesthatcouldbefurtherintegratedintotheproductportfolio,increasingthecompetitiveadvantageoftheoffer.Workingtogetherwiththeenduser,TTSbetterunderstoodwhatisexpectedfromaservice-basedproduct:themainfeed-backisthattheenduserisnotinterestedinthedetailsofusingacloudserviceandthusthetech-nicaldetailsshouldbehiddenasmuchaspossible.Thedrawbackisthat,whenthereis littleornoInternetconnectivity,theapplicationcannotworkproperlysosomekindofsafe-planmustbepro-vided.Currentlythis ismitigatedprovidinga localcacheofthecloudstorageandthepossibilitytorunthesimulationandoptimizationonthelocalcomputerwithlowerperformance.

4 IMPACT(PU)

Fromtheenduserpointofview,thetimeneededtoperformanoptimizationofamediumcomplexlayouthasbeenreducedfrom30minutesonportabledevicestoapproximately3minutes,usingtheHPC-based services (a factor of 10 which empowers also future scenario of on-site evaluation ofseveralalternative layout).Thismeansthatthebarrierthatmadetheuseofsuchtoolsduringthenegotiationwiththecustomerimpracticalhasbeenremoved,andthesimulationandoptimizationareavailabletothewholeFICEPsalesforceindefinitely.

Now,atthecustomer’spremises,asalesagentcanquicklycreateamock-upofanewlayout,start-ingfromsometemplates,selectingthemachinetypeanditsequipmentbasedonthecustomerre-quirements,simulatingandoptimizingit inaveryshorttime,usingaproductionmixsimilartothecustomer’sone.Inthiswaythecustomercaneasilyunderstandthefutureperformanceofthenewlayoutanditsbehaviouraswellastheoneofthemachines.

The clear and intuitive interface helps the sales agents in using this application evenon amobiledevice,asthedevelopedcloud-basedapplicationisveryunderstandableevenbyuserswhicharenotIT-skilled.

The cloud approach streamlines the advanced design process also because now the technical de-partment, starting fromwell-formalized requirements, can produce in a shorter time a fully engi-neeredplantmodelthatcanbeeasilyuploadedonthecloudandthesalesagentscanusethedevel-opedworkflowstoshowthefinalproposaltothecustomer.Inthefuture,theoptimizationservicewillbeextendedtoFICEP’scustomers inorder tosupport thedailyproductionplanningtoreduceserviceandmaintenancecosts.

FICEP’sresponseswithrespecttotheCloudFlowandthisexperiment’sapplicationsinthefollowingway:

A cloud-based infrastructure enables the development of more innovative and novel prod-ucts/services.

□Stronglyagree■Agree□Neutral□Disagree□StronglyDisagree“Cloud-basedsystemallowsamore integratedandstreamlinedflowbetweensalesandthetechnicaloffice”

Acloud-basedinfrastructureenablesmorereliableandrobustproducts/services.□Stronglyagree□Agree□Neutral■Disagree□StronglyDisagree“Theservicemaynotbealwaysavailablewhenthereisnoorlimitedinternetconnectivity,asitisusedonthefieldatcustomerpremisesallovertheworldandnotonlyatFICEP’soffice”

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The integrationofservicesonthecloudwithinyourdevelopmentchaincreates flexibilityandproductionondemand.

□Stronglyagree■Agree□Neutral□Disagree□StronglyDisagree“CloudFlowenables the latestmachineryandplant layouts tobeavailable foruseby salespeopleduringcustomerdiscussions.”

Servicesonthecloudstreamlineandunclenchtherelevantprocesses.□Stronglyagree■Agree□Neutral□Disagree□StronglyDisagree“Thecloudisanexcellentcommunicationtoolallowingdiscussionsbetweensales,theclientand the technical office,which can be challenging as they don’t always use or understandeachother’sterms. Also, itcanreducethetimespentsendingdiagramsbetweensalesandthetechnicaloffice.Finally,thetoolallowsthesalespersontoinvestigate“whatif”scenariosatthecustomerpremises,toshowtothempossibleproductivityimprovements”

FromTTSpointofview,thepossibilitytorunoptimizationsontheHPCinfrastructurehasgiventheabilitytoincreasethequality(intermsofoptimizationcycles),keepingthecomputationtimeatanacceptable level. Furthermore,most of the problems related to low-end hardware configurationsencounteredbyTTScustomers,whenrunningthesimulationontheirownPCs,aresolvedsincenowthesimulationenginerunsonawell-knowninfrastructurewithenoughmemoryandcomputationalpower.ThisimpliesthatitispossibletoguaranteeacertainlevelofperformancetonewcustomersthatareinterestedinusingTTSsimulationtools.

5 BUSINESSMODEL(CO)

Thefollowingsetofstatementsshapethebackgroundforthebusinessmodelanalysis:

• As far as the channels used for the service distribution is concerned, no relevant changes areforeseenwithrespecttothecurrentbusinessmodel. Inthisregards,directsaleswithlargeandloyal over the years’ customers, normallywithin personal networks, will prevail. The Internet-basedchannelwillstillbethemainmeanforcasualcustomersrequiringsimulation.

• Thesoftwareproviderhastheintentionofcontinuingtomaintainalong-termrelationshipstrat-egywiththecustomers,withastronginteractioninwhichthesoftwarefunctionalityprovidediscomplementedwithaknowledge-basedserviceduringandafter-sales.Eventhoughthenatureofsuchrelationshipwillbekeptthesame,itsintensityisexpectedtoincreaseinacloud-basedenvi-ronment,withan“alwaysavailable”service.

• Thecustomersdonot requestanytrainingassociatedto theservicebasicallybecausethesoft-wareprovideroffersahighnumberofin-situhumanresources.

• In the cloud-based business model, the target will still be large manufacturing companies ac-quiredthroughdirectcontactandbasedonanalreadyexistingnetworkapproach.Thecustomerbuysasoftwareservice,consultancyhours,andprovidesdomainspecificknowledgeonhowthemanufacturingplantworks.

• Themaincoststoberemarkedforthecloud-basedmodelwillbetheonesassociatedtotheHPCresourcesdemandedandanincreaseinpersonnelresourcescostsforsoftwaremaintenanceandevolutionrequirements.

• Thepricingmodelwillbebasedonkeepingthesameincomeina5years’timeframe,butonlyfor customersusing theoptimizationasa serviceduring theplantoperation.Forpre-salesand

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designphasethepricingmodelwillbekeptinvariableandbasedonthesoftwarelicenceplusavariablechargeattendingtothenumberhoursofpersonneldedicatedtotheservice.

Theenduser licence is€3.000upfrontplus10%maintenanceeachyearplus twomainupdates (€500each).Thetotalcostofownershipintheconsidered5years’timeframeis€5.200.Thepay-per-usemodelisbasedonamonthly€100fee,resultinginatotalof€6.000,butwiththebenefitforthecustomerofa lower initial investment (only€1.200thefirstyear).Thefollowinggraphshowsthat thepay-per-usemodel is cheaper for theend-user in the first3,5years. Theequivalencebe-tweenthetwopricingmodels isachievedafter3,5yearsandnot inthefirstyearas inthecurrentbusinessmodel.

Themonthlyfeeof€100hasbeencomputedbydividingthecurrent€5.200income,resultinginamonthlycostofabout€86,plus€14toincludeadditionalcostofHPCresourcesusage(oneoptimi-zationbeforeeachworkshift,thatis2,5minx12coresx0,1€/hx220days=€11and5GBx0.6€/GB=€3).

Thisnewpricingmodelallowsstartingtotrytheservicewithjust100€.Thismeansthat,whilein-cumbent customers keep followingwith almost the same cost, a higher number of one-shot cus-tomerscanbeattractedbasedonapay-per-usemodel.ThisresultsparticularlyalignedwiththegoaloftargetingSMEsthathave lowexpenditurecapacityandanoccasionalneedtoaccesssimulationfunctionalities.Therefore,TTScloud-enabledbusinessmodelcomplementsthetraditionalbigclientswith aplethoraof smaller clients. The following tablesprovide insightonTTS scenario in 1 and3years.

1year 3years

Numberofproprietaryapplications/workflowsinthecloud

2 4Quantify the number of applications orworkflowswith other solutions to be exploited in a cloud-basedmanner

Customersegment/niche Machinemanufacturersarethetraditionalcustomersofthenon-cloudversionofthis

€ -

€ 1.000

€ 2.000

€ 3.000

€ 4.000

€ 5.000

€ 6.000

€ 7.000

1year 2year 3year 4year 5year

license+support+upgrade pay-per-use

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Define the type of customers to be addressed intermsofsector/industry,customerprofile,custom-ersize(SME,etc.)

software. A cloud-based format/deliveryapproachallowstoextenttheusageofthesoftwaretoSMEshavingalimitedexpendi-turecapacitybutastrongnecessityofsim-ulationfunctionalitiesespeciallyduringthemachine design phase. These companieswouldbenefitfromausageoftheplatformpurchased as a service on demand. SuchSMEs usually operate in niche marketsproviding speciality high-performing ma-chinesinsmall(alsoone-of)lots.

MarketsizeData available from industrial associationsofmachinetoolmanufacturers (inparticu-lar:CECIMO) countmore than1.500 com-panies manufacturing machine tools. 80%are SMEs. Around 10% of them are ma-chinemanufacturers operating inmarketsreasonably suitable for our purposes. Thefinal number is thus 120 European SMEsarepotentialcustomers.

Quantify approximately the globalmarket size forthatsegment intermsofnumberofbuyerspoten-tiallydemandingtheproduct/service

Numberofclients20 40

Quantify approximately the number of final usersthatwillpayfortheproduct/service

Marketshare

10%* 20%*Quantifyapproximatelythepercentage(intermsofunitsorrevenue)ofthemarketsegmentaddressedthatwillbuytheproduct/service

Numberofnewjobscreated1 2

Quantifyapproximatelythenumberofjobscreatedasaconsequenceofthecloud-basedmodel

*SMEsusuallyadoptdifferentsolutionscomingfromseveralproviders.

1year 3years

Numberofsalestoexist-ingclients

Quantify approximately the number ofunitarysalesofthecloud-basedproduct/servicetoalreadyexistingclients

2 2

Numberofsalestonewclients

Quantify approximately the number ofunitarysalesofthecloud-basedproduct/servicetonewclients

18 38

AveragepriceDefine approximately the average priceor prices of the cloud-services to thepreviousclients

1.200(ex-istingcli-ents)600

(newSMEs)

1.200(ex-istingcli-ents)600

(newSMEs)

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Totalincome Quantify the income derived from totalsales

13.200(€forthefirstyear)

25.200(€/year)

Productionandcommer-cialrelatedcosts

Quantify approximately the total costsraised from any type of activity associ-atedtothecloud-basedbusinessmodel

40.000(€forthefirstyear)

15.000(€/year)

Payback

Estimate the period of time (normallyexpressed in years) required to recoupthe funds expended in the investment*ortoreachthebreak-evenpoint

4years

*Totalcostincurredtodevelop,promoteandsellthecloudproduct/service.ThebeginningofCloudFlowprojectmaybeusedasareference

6 EXECUTIONOFTHEEXPERIMENT(CO)

Theexecutionoftheexperimentencompassedthefollowingstepsandactivities:

• TTS is both the developer of the commercial application “DDD Simulator”, used in the currentprocess,andthesoftwareproviderinthisexperiment,thus,owingthedetailedknowledgeofthesoftware engine, it was possible to seamlessly integrate the application to the cloud environ-ment.

• Thedevelopmentwasorganizedinfourdifferentstages:1)integrationofthesimulationapplica-tiontotheHPCenvironment,2)developmentofsimulationandoptimizationwebservices,3)de-velopmentofsimulationandoptimizationwebfrontend,4)developmentof theclientapplica-tion.

• TheintegrationofthesimulationapplicationtotheHPCenvironmentrequired,asafirststep,torecompiletheapplicationplatformdependentcodetobecompatibletotheCentOS6.6operat-ingsystemused intheHPCenvironment.Thesecondstepwastosupport theexecutionof thesimulation fromdifferentnodes, inorder toexploit theadditionalcomputational resourcepro-videdbyHPC.

• ThesimulationandoptimizationwebservicesweredevelopedincompliancewiththeAPIprovid-ed by the CloudFlow Portal infrastructure. The design and implementation of the serviceschangedduringthetimeoftheexperimentinordertotakeintoaccountthenewfunctionalitiesintroducedbytheCloudFlowinfrastructure(e.g.HPCservice).TheservicesweredesignedfromtheverybeginningtobeusedbyboththewebapplicationthatcouldrunfromthePortal(i.e.in-sideaworkflow)andtheclientapplication,thatrunsnativelyontheenduser’sPC(i.e.usingtheCloudFlowWorkFlowManagerAPI).

• Thesimulationandoptimizationwebfrontendweredeployedinthesamewebapplicationthatexposes the simulation andoptimization services. By implementingCloudFlowapplication typeservice, it ispossibletoshowauser interfacetothePortalenduser.Thisactivityalsoaccountsforthecreationoftherequiredworkflows(asdescribedinD122.3).

• TheThinS&OClientisaclientapplicationdevelopedinJavaFX,optimizedfortouchdevices(suchasWindowstablet).Thedevelopmentoftheuser interfacewasstrongly influencedbycontinu-oustestingbyFICEP.

• Theintegrationwithservicesdevelopedbythirdparties(e.g.SINTEF’sWebGLviewer)hasbeenachieved.Asthemodels,theinputsandtheresultsarepublicallyavailable,otherservicescanbeimplementedtofurtherprocessthem(e.g.graphicalvisualizationandcomparisonofresults).

Asaresultoftheaboveactivities,thefollowingapplicationshavebeenimplemented:

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• Simulation and optimization cloud-based services, implemented as reusable workflowscomposedofapre-processingservice,HPCserviceandpost-processingservice.

• Web-basedparametereditorapplication• Web-basedresultsviewerapplication• ThinS&OClient,implementedasaJavaFXapplication• ModifiedproprietaryTTS“DDDSimulator”softwaretorunonCentOS6.6andHPCenviron-

ment

TheonlyminordeviationfromtheplannedactivitieswascausedbythechoicetousetheHPCser-vicelaterinthedevelopmentphase(asitwasnotavailableatthebeginningofexperiment).

7 RECOMMENDATIONTOTHECLOUDFLOWINFRASTRUC-

TURE(CO)

Basedontheexperienceindevelopingtheservicesandtheclientapplication,andonthefeedbackreceived by the end user, the following recommendations might further improve the use of theCloudFlowInfrastructure:

• Addfile-sizeinformationforfilesstoredintheGSS(GenericStorageService).Thisallowsthepos-sibility,fortheclientapplication,toshowthecurrentprogressofdownloadingandtogiveanes-timateofthedurationofthetask.

• ImplementaccesscontrolandprivatestorageontheGSS.Somefilesshouldbekeptconfidentialandtheaccessshouldberestrictedtoalimitednumberofpeoplebasedontheirrole(i.e.com-mercialcrew,technicalteam,customer).

• Implementqueueprioritization.Simulationand,dependingontheparameters,optimizationareexecutedinashorttime(about3minutes)andkeepingthistimeframeisakeysuccessfactorfortheservice.Withoutaprioritization,thesimulationjobcanbequeuedafterajobfromanotherHPCuserthatcanpossiblytakealotmoretime,thusresultingin5to10minutes(orevenmore)of waiting time. During the test phase, this problem was observed and generated a negativefeedbackfromtheenduser.

8 CONFIDENTIALINFORMATION(CO)

Thecomputationaltimeexpectedwithinthisexperiment,ifcomparedtootherexperiments,islim-ited(typically8minutesonhighenddesktopPC,toamaximum30minutesonlow-specshardware,beforetheintroductionofthecloud-basedsolution):usingaplatformsuchastheoneprovidedbyCloudFlowcouldappear,atafirstglance,notfullyjustified.Actually,relyingonanHPCenvironmentstronglyincreasesthebusinessopportunitiesbecausethereducedruntimeduration(approx.afac-torof10)isfullycompatiblewiththetypicaltimingofasalesprocess,andthespecificationsoftheuserhardware isno longera limiting factorwhenoffering in-situ simulationandoptimization ser-vices.

ApricemodelbasedonlyontheCPUusageisnotwellsuitedforthisexperimentasthesimulationandoptimization jobsarerelativelyquicktocomplete, ifcomparedtothetimesrequiredbyotherheaviersimulationslikeFEMorCFD.Furthermore,theexperimentend-user,FICEP,preferstoknowin advance the total costs related to the usage of plant simulation, in order to allocate the rightbudgetatthebeginningofthefiscalyear.Thisisfurthercomplicatedbythefactthepersonnelusingtheservicearenotthesamethatcanapprovebudgets.

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Considering this background, it is possible to analyse twodifferent scenarios for theusageof thesimulationandoptimizationservices.Thefirstscenario,FICEPusingtheservicestosupportthene-gotiationanddesignphaseof theplant,dealswith thecore focusof theexperiment.Thesecond,FICEP’s customersusing the services tooptimizedailyproduction, represent apossible andenvis-agedextension.

Scenario1:FICEPusingsimulationandoptimizationservicestosupportnegotiationanddesignphas-es.

Asinglesimulationisruninabout1m30son12cores,thusequivalenttoatotalof20CPUminutes.ItisestimatedthatacustomerlikeFICEP,thatusestheserviceinthepre-salesstage,willdoabout25 different layouts and 10 different runs (to tweak some parameters of themodel) resulting in25×10×20'()*+,- = 5000'()*+,- ≅ 83ℎ3*4-every year. Since HPC resource is billed 0,1€/hourtheresultisavariablecostof8.30€/yearfortheHPCenvironment.ThecostoftheVM(Vir-tualMachine)runningthefront-endwebservicesisafixedcostsharedamongallthesimulationandoptimizationservicesusers.Amonthlyfeeof50€wouldcoverthevariablecostsbutitwouldn’tbeprofitableifnomoreusersareattracted,becausefixedcostsofsoftwaredevelopmentandmainte-nancewouldnotbecovered.

Scenario2:FICEP’scustomerusingsimulationandoptimizationservicestooptimizedailyproduction.

Itisreasonablesupposingthattheproductionschedulingwillbeoptimizedeveryworkingdaybeforestarting a new work shift. Considering about 220 working days, we get 220567-×2089:'()*+,- = 4400'()*+,- ≅ 73ℎ3*4-,equivalenttoanHPCcostof7,30€/year.Thecur-rentpayingschemeis3.000€thefirstyearand10%asamaintenancefeeeachfollowingyear.Inatimeframeof5years,thereare2majorupdatesusuallypayed€500each.Thetotalincomeinthe5years’periodistherefore3000 + 4×300 + 2×500 = 5200.Tomaintainthesameincome,thecal-culatedmonthlyfeewouldbeapprox.87€.Tooffsetfortheflexibilityofthepay-per-usescheme,itwouldbereasonabletosetthemonthlyfeeto100€.Theuseradvantagewouldconsistindeferringpayments (1200 €/year) and reducing the initial investment (he doesn’t have to pay € 3000 upfront),makingtheservicesmoreappealingtobetriedbynewcustomers.

9 INVOLVEDORGANISATIONS

TTS-TechnologyTransferSystems.r.l.isaKnowledgeIntensiveSMEstronglycommittedtosupplyitscustomerswithhightechinnovativeITsolutions:research,developmentandexploitationofnewtechnologiesareaprimarymeanforachievingsuchagoal.TTSexpertiselieswiththedevelopmentofadigitalfactoryrepresentation,thatcanbethenlinkedandcontinuouslyupdatedwithdatacom-ingfromtherealfactory:thistechnologyisusedforsettingupnewplants,testingPLClogicsandasamonitoringsystemforproductionplantbehaviour.Furthermore,TTShasa longandstrongexperi-ence in the participation andmanagement of national and international project, targeted for thedevelopmentoftoolsfor3Dfactoryenvironments.

FICEPwasfoundedin1930andurgentlyitisworldwideleaderinthemanufacturingoflinesfortheprocessingofflatsandprofilesforthestructuralsteelindustry,andisoneofthefewmanufacturersin theworldproducingacompleterangeofmachinesandsystemsfor theprocessingof thethreemainelementsthatbuildasteelstructure,i.e.plate,angleandbeam.MachinesandsystemsfortheforgingindustryrepresentFICEP’ssecondimportantproductionrange-infact,FICEPsuppliesauto-maticforgingsystemsforsteel,brass,aluminium,etc.startingfromshearingand,throughautomatichandling,uptoforgingofthefinishedpiece.Thefullautomationoftheshearingproductioncycle,

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also integrated by control options on weight, temperature and the relevant manipulation of cutpieces,uptothe loadingofthefollowingoperation,characterizetherangeofsolutionsthatFICEPcanaffordbyofferingtocustomersafullrangeoftechnologicallyadvancedsolutionsforshearing.

SUPSI -offersmorethan30Bachelor'sDegreeandMaster'sDegreecourses,characterizedbycut-ting edgeeducationwhichmerges classical theoretical-scientific instructionwithpractical orienta-tion.WithinSUPSI,theInstituteofSystemsandTechnologiesforSustainableProductionistheonesupportingtheexperiment.ThemissionoftheInstituteistheinnovationofproducts,manufacturingprocessesandbusinessmodelsinordertoaccompanycompaniesinfacingthechallengesofglobali-zationundertheeconomic,environmentalandsocialaspects.

ArcturistheHPCprovider,anSMEfromSloveniawhichoffersHPCresourcesplussupportforparal-lelizationandcloudificationofsoftwarecomponents.

TheCloudFlowCompetenceCentreconsistsofseveralCloudFlowpartnersfromdifferentEuropeancountrieswhocontributetheirexpertiseinCloudComputing,simulationandvisualisation.

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APPENDIX1:USERREQUIREMENTSANDHOWTHEYAREMET

User Requirement Feasibility Successcriteria Method of Measuring suc-

cessSuccesscriteriaachieved?

EndUser:FICEP

Create and compareoptimization resultstogether with thecustomer

Medium Availability of graphicalcomparison of optimiza-tionresults

Successfuldemonstrationofgraphicalcomparisonduringevaluationphase

Yes – demonstrated duringfinalevaluation

Simulation is availa-ble on portable de-vices at customerpremises

Medium Simulation can run onportable device duringevaluation phase. Thisincludes availability of thegraphical visualization ofthesimulation

End user attempts to runsimulation on a portabledevice during evaluationphase

Yes – demonstrated duringfinalevaluation

Mock-up of a newplant at customerpremises

Medium Amock-up of a new plantcan be created startingfrom a template availableonthecloud

Auser fromthecommercialcrewcreatesanewmock-upofaplantstartingfromasetof typical customer re-quirements. Tested duringevaluationphase.

Yes – Demonstrated duringfinal evaluation, based onplant templates and parame-terinput.

SoftwareVendor:TechnologyTransferSystem

Reduce optimisationtime, but retain cur-rentquality

High Optimization time Ot < 2minwith a population of 30individuals and 10 genera-tions

Measure calculation time(basedonCPUclock)duringevaluationphase.Qualitytobejudgedbyenduser.

Yes – the optimization takesfrom 39 sec to 1 min and 24seconds (depending on themodel complexity), based ontestsrunatTTS. This includesuploadanddownloadtime.

Increase optimisa-tion quality withincurrent optimizationtimes

Medium No. of generations of 100individuals executed in 5min is greater or equal to20.

Duringtheevaluationphase,record the number of gen-erationsof100individualsinan evaluation period of 5min.

100 individualswith20 gener-ations takes 5 min 1 seconds.But testing has revealed thebest quality is achieved byincreasing thepopulation size;

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300 individualswith 3 genera-tionscanbeachievedin4min5seconds.

Plant simulation andoptimisation isavail-abletoothersimula-tions

High Engineisavailable,runningand applicable to othersimulations.

Successfuldemonstrationofasimulationfromadifferentdomain (e.g. logistics, auto-motive,…)duringevaluationphase.

Is available on the portal. An-other workflow could use thesimulation.Cansimulateothertypesofplants.

Research Institu-tion:SUPSI

Acquire knowledgeon Cloud BasedModels

Medium Accessible and Usable bystudents of Design andConfiguration of Automat-ed Production SystemsusingVirtual Environmentscourse

Obtain feedback from stu-dents during evaluationphase.

Results unavailable as of finalevaluation (29/02/16); stu-dents at an appropriate stageoftheMastersprogrammewillnot be available until Spring2017

D12x.1 CloudFlow(FP7-2013-NMP-ICT-FoF-609100)

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APPENDIX2:USABILITYEVALUATION

Thefollowingmethodswereusedfortheusabilityevaluation:

1) heuristicevaluationinwhichtwousabilityexpertsobservedtheenduserwhileperform-

ingasetoftasksoneachapplication;

2) talkaloudinwhichtheenduserdescribedtheirprocessastheywereusingtheapplica-

tion;

3) aninterviewoftheenduserfollowingthesoftwaredemonstrationtoexploretheissues.

Inthisreport,severitiesoftheusabilityissuesareidentifiedandrecommendationstoresolvethem

areproposed.Itisrecognisedthatitmaynotbepossibletoresolveeachissue,andthesuggestions

areforguidanceonly.Highseverityitemsshouldbeaddressedasapriority.

SummaryofusabilityevaluationPros:

• Enduserwasabletocreateandvisualiseaplantsimulationfromaportabledeviceinminutes.

• Attractiveuserinterfacewithintuitivecontrols.

Cons:

• Someconfusionoverwhichoperationswerecloud-basedandwhichwereperformedlocally.

• Userguidanceisnotyetavailable.

EvaluationDetails-Processoverview

1. ThesalespersondownloadstheThinS&Oclienttotheirportabledevice,priortovisitingthe

customer’spremises.

2. Thesalespersonselects“simulate”(Figure1).

FIGURE1.THINS&OCLIENTINTERFACE

3. Duringdiscussionswiththecustomer,thesalespersoncanselectoneofthepre-definedfac-

torylayouttemplates(Figure2).

D122.1 CloudFlow(FP7-2013-NMP-ICT-FoF-609100)

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FIGURE2.SELECTINGPLANTLAYOUTTEMPLATE

4. Thesalespersonchecksandadjustssomeparametersbeforeselecting“Run”.

FIGURE3.SETTINGSANDPARAMETERSWINDOW

5. ThesimulationrunsontheCloudFlowserver.

CloudFlow(FP7-2013-NMP-ICT-FoF-609100) DXXX.XorINTERNALXXX.X

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FIGURE4.SIMULATIONRUNNINGONTHECLOUDFLOWSERVER

6. The salesperson reviews the performance indicators to see productivity and efficiency of

plantandmachines.

FIGURE5.PERFORMANCEINDICATORS

7. ThesalespersonreturnstotheSettingsandParameterswindow(Figure3) tomodifyvaria-

blestoimproveproductivityandefficiency.

8. Themodifiedsimulationisrunandtheresultsarecomparedwiththeoriginalones.

D122.1 CloudFlow(FP7-2013-NMP-ICT-FoF-609100)

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FIGURE6.COMPARINGORIGINALANDMODIFIEDSIMULATIONRESULTS

9. Tovisualisethefactorysimulations,theuserdownloadsaviewerfromtheserver.

10. Theycanthenvisualise(withtheclient)thefactorytoinvestigateanyproblems.

FIGURE7.VISUALISINGSIMULATIONRESULTS

11. TheS&Oclientalsoprovidestheabilitytocompareresultstopreviouslyrunsimulations,and

uploadtheresultstothecloud.

CloudFlow(FP7-2013-NMP-ICT-FoF-609100) DXXX.XorINTERNALXXX.X

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Issue122.1:Userguidanceismissing

Severity:

Medium

Description:

Nouserguidance iscurrentlyavailable. FICEPexplainedthattheywouldrunatrainingsessionbe-

fore thesystem is implemented.However,usersmay forget their trainingorwant to refer touser

guidanceforconfirmationbeforeexecutingoperations.

Recommendations:

Ideally,ausermanualshouldbemadeavailable.Ifthisisnotpossible,inline(e.g.tooltip)helpmay

supporttheuser,andcontactdetailsforfurthersupportcouldalsobeincludedonthepage.

TTShave indicated thatapdfmanual couldbemadeavailablebefore theSeptember2016 review

meeting.

RESPONSEFROMEXPERIMENTLEADER:atutorialandreferencehelpwillbeprovidedwiththeapplication(beforeSeptember2016).

Issue122.2:Availablefunctionalityshouldmatchuserrole

Severity:

Low

Description:

OntheSettingsandParameterswindow(Figure3)notallofthesettingsandoptionswouldbeused

bysalespersons;somewouldonlybeusedbythedesignteam.

Recommendations:

Customisethiswindowbyuserrole;onlyshowthefunctionalitywhichisrelevanttotheuserroleas

determinedbytheirlog-inID.

RESPONSEFROMEXPERIMENTLEADER:theapplicationwillbeinitiallyusedbyalimitedtrustednumberofusers,inordertofullytestthefunctionalitiesandgatherfeedback.Theissuewillbeaddressedinafollowingrelease,afterthelimitedtestedphase.

Issue122.3:Confusionoverlocalvs.cloud-basedoperations

Severity:

Low

Description:

D122.1 CloudFlow(FP7-2013-NMP-ICT-FoF-609100)

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It is not clear that clicking on “Run” in the Settings andParameterswindow (Figure 3)will start a

cloud-basedsimulation.Thismayreduceusers’understandingofthesystemandthereforedecrease

usability.Similarly,itisnotclearthatclickingontheplayarrowintheperformanceindicatorswin-

dow(Figure5)willdownloadtheviewer.

Recommendations:

Providesomeindicationthat“Run”willstartasimulationonthecloudandthattheplayarrowwill

downloadaviewer.Thiscouldbeapermanentlabel,orapop-upwindoworatooltip.

RESPONSEFROMEXPERIMENTLEADER:itwillbeimplementedasatooltip.

Issue122.4:Noindicationthetimetorunthesimulationoroptimisation

Severity:

Medium

Description:

Noindicationwasgivenofthetimetheusermustwaitduringsimulation/optimisation(e.g.Figure4).

Thiswasdiscussedduring theevaluation, and itwasexplained that a timeestimatewouldnotbe

possible.Theriskisthatausermaynotknowifthesimulationisrunningorcrashed,andclosethe

program.

Recommendations:

Providean indicationofthetimeforthesimulationoroptimisation. If this isnotpossible,at least

providesomewarninge.g.“Thisoperationmaytakeseveralminutes.Pleasedonotclosethiswin-

dow”.

RESPONSEFROMEXPERIMENTLEADER:Noaccuratetimeestimationispossible,butwewilllookintogivinganestimationbasedonthesettings.