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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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Allrightsreserved.
Thisdocumentmaychangewithoutnotice.
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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)
25
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)
27
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)
29
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.