IdentifyingInventoryExcessAndServiceRiskInMedicalDevices:

ASimulationApproach

AlbertXu MariaRey

Agenda• Motivation&Background

• GoalStatements

• Methodology• DataAnalysis

• Simulationmodel

• Results

• Insights

• Conclusion

Motivation• Distributioncenters• Highinventoryholdingcosts

• Medicaldevices• Highvalue• Non-interchangeable• Criticality

Background• MedCorecentlycollectedlargeamountsoftransactionaldata• Inventory• Demand• Supply• Forecastaccuracy• Location

• Needtoreduceinventorywithoutaffectingservice• Currentlyuseclassical(s,S)inventorymodel,assumingnormality

GoalStatements•Determineinventorylevelforamaterialnumber,thatensuresaLIFRof98%

•Gaininsightsfromthedata• Understandwhatdrivesserviceperformance

𝐿𝐼𝐹𝑅 = 𝑁𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑙𝑖𝑛𝑒𝑠𝑎𝑙𝑙𝑜𝑐𝑎𝑡𝑒𝑑𝑡𝑜𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦

𝑇𝑜𝑡𝑎𝑙𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑜𝑟𝑑𝑒𝑟𝑙𝑖𝑛𝑒𝑠

Methodology

Demand/SupplyCharacterization• Usetransactionaldatatofindrealdemanddistribution

• FindmetricsthataffectLIFRperformance

Summarystatistics

Clustering• DemandCoefficientofVariation(COV)• NumberofcountriestheSKUisshippedto(Countries)• Averageordersizequantity(OrderQty)• Daysofsupply(DOS)• Orderfrequency(Transactions)

Cluster N Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max1 987 0.929 0.00 1.00 23,784 28 180,480 8 1 22 91 1 1269 0.45 0.12 2 255 3 13372 13 0.943 0.88 0.97 1,128,427 608,436 1,625,940 30 21 41 34 19 113 0.39 0.18 1 4552 1105 93523 168 0.822 0.00 1.00 3,588 3 121,500 3 1 12 824 0 7160 2.10 1.09 4 21 1 2224 11 0.994 0.94 1.00 656 12 2,700 2 1 5 15648 8660 31839 3.06 1.27 3 10 1 595 230 0.939 0.63 1.00 188,832 22,452 600,876 21 8 35 45 14 139 0.33 0.12 1 1433 291 4850

LIFR OrderQuantity Countries DOS DemandCOV Transactions

Commodities HighVolume ServiceRisk

Sparsedemand HighVolumeCommodities

CLUSTERS

1. Commodities2. HighVolume3. Servicerisk4. Sparsedemand5. Highvolumecommodities

CLUSTERS- MEANCOMPARISON

SimulationModel

OrderQuantity

OrderFrequency

ReplenishmentLT

LIFRTarget:98%

IOHLevel

1 2

SimulationModel

Cluster1 Cluster2 Cluster3 Cluster4 Cluster51stPlace Pareto Gamma Gamma Pareto Exponential2ndPlace Gamma Pareto Pareto Triangular Triangular3rdPlace Triangular Triangular Gamma

OrderQuantitydistribution:~Gamma

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12 211 410 610 809 1008 1207 1406 1606 1805 2004 2203 2402 2602 2801More

Frequency

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Cluster1:XCW9567

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Frequency

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Orderfrequencydistribution:~Normal

Issueswithmaterialnumberswithfewdemandpoints- Hardtofitadistribution- TheyhavehighDOSlevels

1 Distributions

MaterialNumber Cluster Shape Scale Shift Mean StDev Mean StDevXCW9567 1 0.714 175.83 12 4.7 1.9 7 2XAVCP359H 2 0.331 2,761.20 36 49.4 8.3 3 1XAV475G 3 0.569 147.68 36 1.0 - 3 1XNMIC572H 4 0.232 125.00 36 1.2 0.4 12 3XZW9252 5 0.421 1,367.10 12 7.5 2.2 3 1

GammaDistributionOrderFrequency ReplenishmentLeadTimeOrderQuantity

NormalDistribution NormalDistribution

SimulationModelAssumptions• Customerordersareexogenousandindependentofinventorylevel.Orderscancomeinevenifthereisnoinventoryonhand.• Customerordersarenotcorrelated• Assuminglotforlotpolicy.• Assumingsupplierisnotoutofstock.• Inventoryavailabilityistheonlyfactoraffectingservice.• Rawdatadonothavesystematictrend/fluctuationacrossseasons.Therandomnessofthevariablesusedinthesimulationaccountforanyseasonalitypresentintherawdata.• Assumingcountries’demandsareindependent.• Unfilledinventorywillbebacklogged.Customerswilleventuallyacceptinventoryregardlessofwhentheyreceiveit.• Orderallocationissequential,onafirst-come-first-servebasis.

SimulationProcess

0 10050 98LIFR

Inv=1k Inv=2kInv=3k

2

Createdemand

distribution

Initializeparameters

Simulateweek-by-week

orderallocationand

receipt

Placeorder

Advancetime

ReportLIFR

Results

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1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

Inventoryo

nHa

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IOHComparisonCluster2:MaterialB

OrderQty ActualIOH Base SS TotalEntitlement Simulated

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Inventoryo

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IOHComparisonCluster5:MaterialE

OrderQty ActualIOH Base SS TotalEntitlement Simulated

SKU ACTUALLIFR AVERAGEIOH WEEKSW/OINV. CURRENT INV.LEVEL SIMULATEDLEVEL

A 85.83% 3,160 2 4,100 3,500

B 96.94% 740,928 0 800,000 180,000

C 53.85% 632 1 300 1,000

D 100% 1,142 0 1,500 800

E 90.05% 49,923 0 61,000 26,000

Samplematerialnumberpercluster

Insights

COUNTRYA(BLUE) COUNTRYB(ORANGE)

NO.OFORDER 620 45

TOTALUNITSORDERED 51,696 121,860

LARGESTORDERSIZE 504 23,040

DEMANDCOV 0.21 1.07

Hypothesis:Erraticorderingpatternscausesstraininthesupplychainperformance.Itleadstohigherinventoryrequirementsgiventhesameriskexposure.

Insight1:DemandPatternAnalysis

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ty

Month

Country ComparisonMaterialB

CountryA CountryB

SimulatedOrderPatternChanges

• Recommendation:reduceordervariability,advancednoticesbeforeorderingabovethreshold

Insight2:Forecasting• Weincrease1periodofforecastingbyassumingweknow80%ofthenextperiod’sdemand

• Resultsshowitmoderatelyimprovesinventoryperformanceundercurrent1weekleadtime

Insight3:Leadtime• Wesimulateda‘supplyshock’whereourreplenishmentlead-timesuddenlyinflatesfrom1weekto1month

• Inventoryperformancedropsdrastically.Weneedtodoublethebaselineinventorytoguaranteeservicelevel

Conclusion• Data-drivenapproachtoinventorymanagement

• Understandingdemandcharacteristics• Clustering• Machinelearning

• Applicabletomanyindustries• Criticalityofproductavailability• RiskManagement• Manageorderpatterns

Q&A