food mfg co. case study v2.21.ppt
TRANSCRIPT
FoodManufacturingCompanyCaseStudy
DemandWiseCu-ngEdgeSolu3onsinDemandManagement
TheBeginningofBeginning‐to‐EndManagement
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Background
CompanyisthelargestfoodmanufacturinganddistribuDoncompanyinIsrael.
ShufersalisthelargestfoodretailerinIsrael. Companyoperates“directstoredelivery”vendorrelaDonshipwithShufersal: AccesstoPOSdata Controlsdeliveryandon‐shelfavailability end‐to‐endcontrolofsupplychain
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TwoChallenges
EvaluatetheoutcomesofpromoDonsofYoplaitproductsoveratwo‐yearperiod. EsDmatesalesliRduetopromoDonsand Provideindicatorsofthereasonsforfailuresorsuccesses.
EsDmateandforecastdemandforthesesameproductsinspecificpromoDonalpricingscenarios. EsDmatepriceelasDcityundervariouspromoDonrules EsDmateandforecastdailydemandforspecificitemsinspecificstores.
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TheOpera3onalchallenges
Numberofstores:24 NumberofSKUs:192
Numberofdays:455
NumberoftransacDons:909,175
NumberofpromoDons:4
OtherconsideraDons: Dayofweek Seasonality
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TheResultsinThreeAreas
1. AggregateperformanceofpromoDonsi. BypromoDonii. ByStoreiii. EconomicImpactofeachpromoDon
2. Drill‐downtolevelofSKUperstoreoranylevelofaggregaDondesired
3. DailyforecastsofdemandinpromoDonsforeachSKUineachstore,givenaspecificpricingscenario
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AggregatePromo3onResults
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(31,549)
15,005
(25)
13,769
(2,800)
(40,000)
(30,000)
(20,000)
(10,000)
‐
10,000
20,000
2 4 5 6 Total
UnitChangePerPromo
Tnuva–AggregatePromo3onResults
ResultsbyPromoDon:
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MoreAggregatePromo3onResults
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‐30.00%
‐25.00%
‐20.00%
‐15.00%
‐10.00%
‐5.00%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
1194
1359
1925
4610
5748
7371
10143
41317
55471
55713
55900
55914
55931
55940
55964
GrandTotal
%UnitSalesChangebyStore
MoreAggregatePromo3onResults2
ResultsbyStore:
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EconomicResults
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(231,408)
(143,298)(180,873)
(146,388)
(701,965)
(800,000)
(700,000)
(600,000)
(500,000)
(400,000)
(300,000)
(200,000)
(100,000)
‐2 4 5 6 Total
DollarOutcomesofPromoJons
EconomicResults
EconomicImpactofPromoDons
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PromoAnalysisConclusions
Nearly1milliontransacDonswereprocessedandanalyzedautomaDcallyatthemostgranularlevelinminutes.
Unitsalesdecreasedmateriallyfromnon‐promoaverageinPromo2impliesfrequent,endemicout‐of‐stockevents.
Twostores—10143and5590—experiencedthemajorityofout‐of‐stockevents.
AllpromoDonslostmoney‐$701,965overonly8weeks.
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ForecastsandMethod
WhatDemandWise“sees”:
DWThursdayEsDmate–promo6
DW
Saturday
EsDmate
Usualleast‐squaresesDmate
ObviousOOS
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ForecastandMethod
DemandWise“sees”day‐of‐weekandseasonalpaierns.
DemandWisedifferenDatesproductsandlocaDonsthatpromosimpactfromthosethatpromosdon’timpact.
DemandWiseanalyzespriorsalestransacDonstobuildpriorprobabiliDesonwhichtoapplyBayes’Theorem
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ForecastandMethod
ClustertransacDonsbyhistoricalpaierns Seasonality
Withstrongpromoeffect
Withnopromoeffect
DayofWeek
Promo/nopromo
Frequent/infrequentpromo Holidays
AppliesRelaDonalBayesianNetworktechnologieswithinclusters AcrossstoresandlocaDonsinsameperiods
AcrossstoresandlocaDons
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ForecastDetail
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TnuvaDemandForecastPromoJon:10%‐25%Discount
1Weekofproposed4‐WeekDuraJon
ForecastDetail
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‐
500
1,000
1,500
2,000
2,500
3,000
StoreNumber
Forecastvs.AverageUnitSales
One‐weekForecast
Avg.UnitSales
Parameter: 25% discount
ROIonProposedPromo3on
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1194 1194 1925 1925 4610 4610 7371 7371 55713 55713 55940 55940
ContribuJon $(983) $(1,398) $(295) $(649) $(434) $(589) $(257) $(435) $(278) $(512) $(344) $(457)
$(1,600)
$(1,400)
$(1,200)
$(1,000)
$(800)
$(600)
$(400)
$(200)
$‐
StoreNumber
PromoROI
Parameter: 25% discount
DemandWiseForecastsYieldAc3onableInforma3on
DemandWiseforecastsare: OperaDonal–daily,weekly,monthly,custom TacDcal–monthly,quarterly,custom
DemandWisemodelspromoDonscenarios Price(in‐storedisplay,circular) Coupons(storeandmanufacturer) Deals(e.g.,buyonegetonefree)
DemandWiseesDmatespriceelasDcityofdemand ConDnuous Stepwise/pricepoints
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DemandWiseTransformsDataintoAc3onableBusinessIntelligence
Determineweeklydeliveryschedules Drivequarterly,seasonalpurchasesandlogisDcsplans DrivepromoDons
Stockingplans PricingtacDcs In‐StoreposiDon CompeDDveDming OpDmizeseasonaleffects(liRornoeffect)
DriveplanogramsoRwaremodels GranularspaceallocaDontoopDmizeOSA Granularanalysisofin‐storelocaDons
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DemandWiseImpactUnderstandDemand—Sa3sfyDemand:Manageyourbusinessprocessesfromthecustomertotheproductandbacktothecustomer
MinimizeOOSevents–achieve<5%OOS
PlanandExecuteprofitablepromoDons–turnlossleadersintoprofitleaders
OpDmizeinvestmentininventory
MinimizelogisDcscosts Therightproducts Therightplaces TherightDme ThefirstDme AlltheDme
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CharlesBroming,President/CEODemandWisePartners,[email protected]‐592‐0060Orlando,FL32819
DemandWise
UnderstandDemand—SaJsfyDemandManageYourProcessesFullCirclefrom
DemandthroughFulfillment
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