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Zara uses operations research to reengineer its global distribution process

Zara uses operations research to reengineer its global distribution process. GLOBAL DISTRIBUTION PROCESS OF ZARA. Sophisticated operations research models Two central warehouses 1500 stores worldwide 3-4 percent sale increase. Outline. Introduction

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Zara uses operations research to reengineer its global distribution process

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  1. Zara usesoperationsresearchtoreengineerits global distributionprocess
  2. GLOBAL DISTRIBUTION PROCESS OF ZARA Sophisticatedoperationsresearchmodels Twocentralwarehouses 1500 storesworldwide 3-4 percentsaleincrease
  3. Outline Introduction The problem – ManagerialChallenge Legacyprocessandnewprocess Thesolution AnalyticalDevelopment IT implementation Impact of work Model
  4. Introduction 1500 stores in 68 countries €6.26 billlion in annualsales Fast-fashionbusiness model Frequent in-seasonassortmentchanges Everytrendyitemswithcompetitiveprices Twice a weekshippment
  5. Whatwasthe problem? Stores WarehousesDesigner Suppliers
  6. Whatwasthe problem? Determiningtheexact number of units of each size of eacharticle Controlling Problem
  7. Challenging Reasons for the control problem Display merchandise when the of available sizes is complete enough Time limitation for decision shipment The amount of relevant data The available warehouse inventory is often limited
  8. Legacy Process(until 2006) Thestatementwouldn’t mentionedthe total quantity in the warehouseforeach articlelisted.
  9. Thesolution Zara usesshipmentrequestfromstoremanagersandpasthistoricalsales Theseareused as input; 1)Demandforecasts 2)theinventory in thewarehouseandstore 3)theassortmentdecisions Maindecisionvariable: shipmentquantities Objective : maximization of global sales
  10. Analyticaldevelopment Theforecasting model, theoptimization model
  11. FORECASTING MODEL Prediction of theweeklydemandforeach size of eacharticle in eachstore in Zara’s network Thestandardmethodology of regressionanalysis Inputdatas of forecasting model Theobjectiveandcentralized time-series data of historicalsalesforthearticle Thesubjectiveanddecentralizedshipmentrequestbythestoremanager
  12. Optimization MODEL Constructing a predictive model fortheexpectedweeklysales of allsizes of a givenarticle in a givenstore Demandforecastsandstartinglevel of inventory in thatstore Predictshipmentdecisions Thequalitativeandmanagericalimplications of thisfunction Complete size profile
  13. Exposureeffect Theaggregateeffectforallsizes of an articlecauses a risetoexposureeffect Theexposureeffectwillpushtheoptimization model toshiptothosestores.
  14. Exposureeffect
  15. Saturationeffect
  16. Saturationeffect Thesecondcriticalfeature Reflectsthedecrease of marginalprobability of sale Whenconsideredjointly, theexposureandsaturationeffectsenablethe model toselectstoresthatwillbringabovetheexposurethreshold
  17. Mip model Piecewise-linearapproximations of manyinventory-to-salesfunctions Computeshipmentdecisions Ignorestheimportantissuessuch as forecastuncertainity, the time horizon, opportunitycost of storagespace at thewarehouse
  18. K:aggresiveness factor(remainingwarehouseunits) P:actual unitsellingprice
  19. IT IMPLEMENTATION Solvesapproximately 15000 instances of MIP Distribute 6 millionunits Thecorecomputationalapplication Visual FoxPro
  20. Snopshotapplication
  21. Impacts Measurement Methodology Financial Impact Organizational Impact Cultural Impact
  22. Impact
  23. FinacialImpact Revenue: €310(2007) €353 (2008) Net Income:€37.2(2007) €42.4(2008)
  24. OrganizationalImpact -Enthusiasticusers of thenewtools CulturalImpact -volatile market trends, recruiting top designers, creatingfashionableclothes -Welldesigned OR model = betterperformance+norescalableoperations
  25. Single-StoreInventoy-to-Sales Model
  26. Thankyouforyourattention
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