KROGERSTORESIMULATIONUSINGARENA

DHIVYARAJPRASADM10857825

BANAFall2016

CHAPTER-1

INTRODUCTIONSYNOPSISThisprojectisdoneasafinalprojectforthecourseBANA6035-SimulationModelingwhere the focus lies on understanding the basis of Simulation concepts using asoftwarecalled“Arena”.ThepurposeoftheprojectistoprepareaworkingsimulationoftheKroger’sstoreat4777KenardAvenue.ThemodelinArenaprovidesanoutputformanystatisticalvaluesliketotalnumberofcustomersservedinaparticularintervaloftime,timespentinthequeuesinthestore,maximumservicetimesinthecounters,resourceallocationandutilizationlevelsetc.Themodeluses the layoutof thestore,managementsystems,check-outcounters,optionsofresourceallocationetc.inArenatosimulatereallifescenarios.The input data for themodel consists of inter-arrival times of the customers, theservicetimesateachcounterandtheshoppingtimeswhichwascollectedatthepeakhoursofstudentvisitsatKroger’sstore.Themodelwasalsorunforabout10daystoanalyzetheresults,changesweremadetotheexistingmodelandbasedontheconclusions,suggestionsaremadetoimprovetheefficiencyofthestore.PROBLEMSTATEMENTTheKrogerCompanyisanAmericanretailerformedinCincinnati,Ohioin1883andhasestablisheditselftobecomethelargestsupermarketchainbyrevenueinUSandthethirdlargestretailerintheworld.ThepurposeofchoosingtheKroger’sstoreat4777KenardAvenueisthatitisthemostfrequentlyvisitedKrogerstorebythestudentsofUCasthereisaweekend(Friday,SaturdayandSunday)shuttlethatrunsfromtheUniversitymaincampustoKroger’sinhalfhourintervalstoenablestudentstocompletetheirweeklygroceryshopping.AccordingtotheinformationfromtheshuttledriversandtheKrogerauthorities,themaximum influx of students’/rush hour occurs between 3pm-7pm on Fridays,SaturdaysandSundays.Duringtheserushhours,thewaitingtimeforacustomerpeakstoalmost16minutesaftershoppingtocheck-out fromthestorethuseffectivelywastingthetimeofthestudentsandlosepotentialcustomersforthestoreasStudentsmightchoosetoshoplessfrequentlyorinnearbystorestoavoidthewastageoftime.

Thustheneedtomakethesystemmoreefficientisnecessary.Thesimulationprojectusestheparameterspresentedinthemodeltoprobeintothegroceryretailsystemandhowtomaketheexistingsystembettertodecreasethetimespentbyacustomerinthestore.ASSUMPTIONSTheexisting systemat Kroger’s has beenmodeled to thebest possible extent andthere are some variations due to unscheduled or unforeseen circumstances andnaturalvariations.Therearesomeassumptionswhicharemadeontheabovebasistoexcludesuchoccurrences.1. Theshopisopenforabout19hourseverydaywiththestoretimingsas6:00am

to1:00am.2. Therearenoworkshiftsbetweentheworkers.3. Therearenobreaksfortheworkersinthemodel.4. Everycounterexcepttheself-check-outhasoneserver.5. The time of the customers who don’t shop at all is not counted in the total

customertime.6. Theservicetimevariesfordifferentworkersindifferentcounters.7. ThedecisionmoduledatahasbeentakenfromobservationandKrogerauthorities

duetothescopeofthedatacollection.8. Customerleavesthestoremeansthetransactionwiththepackingdepartmentis

overanddoesnotcountthewaitingtimeforshuttleoradditionalbrowsingwithoutapurchase.

9. Theaboveassumptionsholdgoodthroughoutthemodellingperiod.

CHAPTER-2

DATACOLLECTION,MODELELEMENTSANDFITTINGOFDISTRIBUTIONS

DATACOLLECTIONThemodelwhichispreparedforthescopeofthisprojectconsiderstheresourcesinthe entire shopping process like Self-Check-out Counter,Manual Counter, ExpressCounter, Packing Counter, Self-Check-Out Loading etc. to keep a sequence of theprocess. Inorder to input theservice times for thesedifferent resources,datawascollectedattheKrogerstoreintheformofinter-arrivaltimesofcustomersandtheservicetimesfortheresourceallocatedatthevariouspointswiththepermissionofKrogerauthority.Thedatausedherewascollectedforbetween3pmand7pmforthreedays(ThedayswereeitherFriday,SaturdayorSunday).From the interaction with the authorities and assumptions, nearly none of thecustomersleavewithoutapurchase.About30%useself-check-outcounters,20%useexpresscountersandremaining50%usethemanualcounters.Outofthe30%whouseself-check-outcounters,nearly30%encounterproblems,whichareresolvedbyanemployeeatacounterallocatedforthesame.MODELELEMENTSTheentirestoresimulationprocessisshownbelowasaflowchartwhichwillbeusedasthebasisforArenaModelcreationanddecisionfordatapointstobecollectedasrawdata.

FITTINGOFDATATODISTRIBUTIONSTheArenamodeltakestheinputdatatorunandanalyzethroughdifferentmodulesinthemodel.Arenaacceptsthedataintheformofadistributionwhichwillbebestfittedfortherawdata.Therawdataofinter-arrivaltimesandservicetimesforalltheresources are fitted into a distribution which is fed into Arena. Rockwell’s InputAnalyzerwasusedinthisprojecttodeterminethedistributionsofthevariousinter-arrivaltimesandservicetimes.Thedatawasloadedintheformofatextfilewithrawdatawhichwasthenfittoasuitabledistribution.Pleasefindbelowthedistributionoftheinter-arrivaltimesandservicetimesatvariouspointsofthemodel.CustomerInter-ArrivalTimes:

ShoppingTime:

Self-Check-OutCounter:

ExpressCounter:

ManualCounter:

ResolvingCounter:

PackingCounter-Self:

PackingCounter-ExpressandManual:

PaymentCounter:

CHAPTER-3

MODELINGMODELINGTHEPROPOSEDMODELINARENATheKrogerStorewasbifurcatedintodifferentpieces/processestoprepareamodelin Arena. Various modules have been used Eg: Create, Assign, Process, Decision,Dispose. The procedure has been split into steps according to the flow diagrammentionedabove.Thedifferentstepsare:1. Customerentersthestore2. Customerproceedstoshop3. CustomerWaitsintheQueueforacounter4. CustomerBillsthepurchase-Self/Express/Manual5. Theitemsarepackedinthecounter6. Customerpaysforthepurchase7. CustomerleavesthestoreTherearealsosomedecisionmodulestocheckforthepathofthecustomer.AssignandRecordmodulesareusedtoassignandrecordtheaveragetimeofthecustomerinthestore,inthequeueandthenumberofcustomerswhopurchasedandcameoutofthestoreinaspecifictimeinterval.TheotherparametersareResourcesandtheQueuesallocation.

Arena Store ActionPerformedEntity Customer PartResource1 Self-CheckOutbyCustomer SeizeDelayReleaseResource2 Express-CheckOutPerson SeizeDelayReleaseResource3 Manual-CheckOutPerson SeizeDelayReleaseResource4 ResolvingPerson SeizeDelayReleaseQueue1 Self-CheckOutQueue FIFOQueue2 Express-CheckOutQueue FIFOQueue3 Manual-CheckOutQueue FIFOQueue4 ResolvingQueue FIFO

TheArenamodelthatwasdonebasedontheabovemodulesisasbelow:

1. CustomerentersthestoreACreatemoduleisusedtomodeltheCustomerenteringthestorewhosedialogboxandtheentryparametersareshownbelow,theexpressionsusedaregotfrominputanalyzer.Thereisanadditionalexpressionfortheentitiesperarrivalwhichvariesinauniformdistributionbetweenaminimumof3andamaximumof5.AnAssignmodule is thenused to record the timeof arrivalwhichwill be used tocalculate total times for different check-outs. TNOW is an inbuilt function whichrecordsthetimethattheentitypassesthrough.

2. CustomerproceedstoshopAprocessmodulewith‘delay’actionisusedwiththeexpressionfoundthroughinputanalyzertomodeltheshoppingbehaviorofthecustomerwithanormaldistribution.

3. CustomerWaitsintheQueueforacounter

Adecidemodule isused to fixprobabilitiesof30% for Self-Check-outand20% forExpresscountersandtheremaining50%leaveforManualcounters.

4. CustomerBillsthepurchase-Self/Express/ManualDependingonthedecidemodule,threeprocessmoduleswith‘SeizeDelayRelease’actionareusedbasedontheexpressionsderivedfromtheinputanalyzerswhichareallassignedaresourceeachwithmaximumcapacitiesasSelf-12Manual-16Express-4Thereisafourthprocessmodulewith‘SeizeDelayRelease’actioncalledResolvingforresolvingtheissuesfacedby30%oftheSelf-CheckOutcustomerswitharesourceandmaximumcapacityasResolution-2Thedialogboxesareallgivenbelow:

Thereisalsoadecisionmodulebeforeresolvingmoduletosegregatethe30%whowillgoresolvingtheissuewithSelfCheckOutCounters:

There are three recordmodules to count the number of customers whichwill berecordingthenumberofcustomersundereachcheck-outcategory.

5. TheitemsarepackedinthecounterAccordingtothecheck-outcounters,thetimeforpackingismodeledasthetimetakenforself-check-outishigherversusthetimetakenforapersonworkingatthestoreforExpressandManualCountersandthisismodeledusingtheexpressionsprovidedbytheinputanalyzer.

TwoRecordmodulesarealsousedtorecordtheaveragetimetakenforthepurchaseinSelf-CheckoutVersustheManual/ExpressCounters.One additional Recordmodule is used to record the average spent by a customershoppingirrespectiveofthecountertheytake.

6. CustomerpaysforthepurchaseA process module with ‘Delay’ action with the expression provided by the inputanalyzerisusedtomodelthepaymentprocess.

7. CustomerleavesthestoreAsimpleDisposemoduleisusedtospecifythatthecustomerleavesthestoreandoutofthemodelcreated.

ThesnapshotsoftheQueuesareasgivenbelow:

ThoughtheCountershavecapacityasfollows:Self-12Manual-16Express-4Resolution-2Butatanyparticulartime,thefollowingistheoccupationcapacity/resourceallocationofthesecountersandwillbeusedforourBaseModelinArena:Self-CheckOutCounters-8Manual-9Express-2Resolution-1

SIMULATINGTHEMODELWeusetheRunSet-uptorunfor10replicationssimulating10daystoconsiderallthevariabilitiesassociatedwiththemodelandeachreplicationwith4hoursofoperationeachdayfrom3-7pm.BelowisthesnapshotoftheRunSetupoption.

Thesnapshotoftherunmodewhenanimatedwiththequeuesareasfollows:

CHAPTER-4

RESULTSANDINTERPRETATIONRESULTSTheArenasoftwareproducesdetailedresultspostrunningofthemodelwhichhelpsus to view the results by Entity,Queue, Resource andUserDefinedwhich are thedefaultsettings.Therearealsoadditionalreports likeprocessreportswhichcanbegeneratedbasedonneeds.ThenumberofEntitiesout/numberofcustomers’outisadefaultkeyperformanceindicatorinthecategoryoverviewreport.Forthismodel,thenumberofcustomersoutofastoreinasimulationof4hoursover10daysis437.

BYENTITYEntity/Customerisdefinedbytwoparameters,thenumberofcustomers(entering,outandinthesystem)andbythetimetakenbythecustomer(queueandtotaltimes).Arenagivesaverydetailedoutputwiththemean,halfwidthorconfidenceintervals,maximum and minimum for the various times that are observed in the modelsimulation. IntheKrogerStoremodel,themajoroutputneededwasthetotaltimespentbyacustomerinthesystem,theservicetimesateachtypeofcheckoutandthewaittimeforeachcheck-outcounter.TimeRecords:

Wefindthatonanaverageacustomerspendsabout48minutesinthegrocerystorewithahalfwidthof+/-1.32minutes.Theaverageservicetime(valueaddedtime)forthecustomerisnearly11.67minuteswithanaveragewaittimeofabout4.43minuteswhichaddsupto16.1minutesofserviceandwaittimeswhichisbeingaddressedtoreduceintheproblemstatement.Theshoppingtime(nonvalueaddedtime)forthecustomerisabout31.81minutesonanaverage.NumberofCustomers:

Wealsofindthatonanaveragearound550customersarrivein4hoursinagrocerystorewitharound99customersonaverageinsidethestoreatanypointintime.BYQUEUE

Wegetthetimeandnumberparametersbythequeuetoowithaveragewaitingtimesandaveragenumberofwaitingpersonsinthequeue.

WefindthattheaveragewaitingtimeismaximumfortheManualandSelfCheckOutCountersandweaimtodecreasethesetimeswhichwillimprovetheefficiencyofthebillingcountersandreducethetimespentinqueueingandmanagingthequeues.BYRESOURCEResourceUtilizationisaveryimportantparameterwhichwilldeterminethepointsatwhich the system gets chocked up and does not allow the free passage of theentities/customers. The Scheduled utilization is the most important parameter ofconsiderationandbelowistheoutput.

WecanobservethattheManualandSelf-CheckoutCountershaveveryhighutilizationlevelwhileResolutionhaslesserutilizationlevel.BYUSERSPECIFIED

Arena gives separate outputs for all the parameters collected by the users. In thismodel,wehavespecifiedtocollectthecountofthreedifferenttypesofcustomersduringarunalongwithtimeintervalsforshopping,totaltimeinthesystemandtimetakenfordifferentcheckoutmodes.

Wecanseethatthenumberofpeopleusingmanualcountersisthehighestwhichisinaccordancewiththedecisionmoduledecisionssuppliedbyus.Wealsofindthatthetotaltimespentbyacustomerinthesystemisaround48minuteswhichwasfoundinthe entity table andwill be used as a parameter to analyze alternate scenarios toreducethetotaltimespentinthesystem.INTERPRETATIONS

• WefindthattheManualandSelfCheckOutCountershaveveryhighutilizationratesalongwithhighwaitingtimes.Sobyaddingadditionalresourcesforthesetwocounterswewillbeabletogettheefficiencyofthestoretoincreaseandalsodecreasetheaveragewaitingtimeinthesystem.

• We also find that the utilization levels of Resolution counters are very lessdespitetheaboveaveragewaitingtimesandcanalsobeconsideredtogetabetterscenario.

CHAPTER05

ALTERNATESCENARIOSANDIMPROVEMENTSINTHESYSTEMALTERNATESCENARIOSSCENARIO1-Self-ManAsmentioned in the interpretations, we find that the Self andManual Check OutCounters have very high utilization levels along with waiting times, so in the firstscenario,weincreasethecapacityofthesecounterstotheirmaximumlevels.Self-Check-out–12ManualCheckOut-19whilekeepingtheothercountersatthesamelevel.SCENARIO2-Res-ManWe increase the Resolution counters and Manual Check Out Counters to theirmaximumcapacityinthesecondscenariotoreducetheutilizationandwaitingtimes.ManualCheckOut-19Resolution-2SCENARIO3-Res-ExpressWe increase the Resolution counters and Express Check Out Counters to theirmaximumcapacity in the thirdscenario to reduce theutilizationandwaiting timeswithoutchangingthenumberofoperationalcountersofSelfandManualCheckout.ExpressCheckOut-4Resolution-2SCENARIO4-Res-SelfWeincreasetheResolutioncountersandSelfCheckOutCounterstotheirmaximumcapacityinthefourthscenariotoreducetheutilizationandwaitingtimes.Self-CheckOut-12Resolution-2SCENARIO5-Man-ExpressWeincreasetheExpresscountersandManualCheckOutCounterstotheirmaximumcapacityinthefifthscenariotoreducetheutilizationandwaitingtimes.ManualCheckOut-19ExpressCheckOut-4

PROCESSANALYZEROUTPUTSBelowisthescreenshotoftheentireallocationsystemalongwithouroriginalbasecaseinProcessAnalyzer

AverageTotaltimespentbyacustomer:Wefindthatthetotaltimespentbythecustomerisreducedbynearly3minutesonaveragebyincreasingtheSelf-CheckoutandManualCheckoutCounters.Thegraphsareplottedforallthescenariosasbelow:

Asexpected,thebestscenarioispresentedbyincreasingtheSelfandManualCheckOutCountersduetothenumberofcustomerswhousethemandalsothedependencyofResolutiononSelfCheckOuttoreducethebuild-upofcustomers.UtilizationLevels:Wealsotrytobringintheutilizationlevelsintothepicturetodetermineifthescenariopresentedaboveisreallythebestone.Wefindthatattheabovementionedscenarioatleast3ofthe4processesoperateatanefficiencylevelofabout50%,whichdoesn’t

entirelyallowbuild-upofahugewaitingqueuebutat thesametime isnotunder-utilizedaswiththeotherscenariosandalsohasanaddedadvantageoflowestaveragetimetakenforthecustomertoleavethestoremakingitabetterscenario.

STATISTICALANALYSISWealsohave the statisticswhichwereused toplot theboxplots above from theProcessAnalyzer:Scenario Min Max Low High 95%CI-HalfWidthBase 36.11 82.61 46.60 49.24 +/-1.32Self-Man 36.15 70.87 44.39 45.68 +/-0.65Res-Man 36.64 87.60 44.91 48.02 +/-1.56Res-Express 35.78 77.00 46.24 48.68 +/-1.22Res-Self 36.25 64.69 45.43 47.45 +/-1.01Man-Express 36.12 81.12 44.13 46.41 +/-1.13We find that for the best scenario alongwith the lowest average time spent by acustomerinthesystem,wealsohavelowesthalfwidth.Wefindthatourresultisstatisticallysignificantatthe95%Confidenceleveloratasignificancelevelofalpha=0.05.

CHAPTER06

CONCLUSION

TheKroger’sStoreatKenardAvenuewasmodelledusingArenaSimulationSoftwarein an attempt to suggest the bestway to reduce the queueing times and averagewaitingtimeforthecustomertocheckouttheirproducts.DifferentpiecesofsoftwarewereusedtodesigntheinputsandanalyzetheoutputsprovidedbyArenalikeinputanalyzerwhichfitthedistributionofthedataandprocessanalyzerwhichhelpedinstatisticalanalysisoftheoutputs.ByprobingintotheoutputsprovidedbyArena,wewereabletoobservethewaitingtimeinthequeuewaslargeinSelfCheckoutandManualCountersfollowedbytheothers.Weimplemented5differentscenariosinProcessAnalyzerforunderstandingtheeffectsofchangesinresourceallocationacrossdifferentprocesses.WefoundthroughtheProcessAnalyzeroutputsthatutilizingalltheinstalledmanualand self-checkout counters with additional man power during the busy hours willincrease the efficiency of the store by decreasing the average wait time for thecustomerswhilemaintainingabout40-50%utilizationinallthecounters.Wealsofindthestatisticalsignificanceofourbestscenariofromtheoutputsprovidedwhileplottingchartsinprocessanalyzer.Wethusfindthatemployingmoremanpowerduringthebusyhourscouldreducetheaveragewaittimeforthecustomerandcanalsoincreasethebusinessofthisparticularstore.Therearemanyothermodificationswhichcanbemadetothemodeltogetbetterresults and this is just one part of the best solution taking into consideration ourassumptionstobuildthemodel.

REFERENCES

1. SimulationwithArena-6thEdition-DavidKeltonW.,RandallP.Sadowski,NancyB.Zupick,RockwellAutomation

2. Data-Krogers,KenardAvenue,Cincinnati,Ohio3. Logo-TrademarkofKroger