Authors Amit Panditrao and Kishore Adiraju Advisor Dr Chris Caplice Sponsor Niagara Bottling LLC 1 Introduction Methodology Data analysis Transportation model Safety stock model Recommendations ID: 1031279
Download Presentation The PPT/PDF document "Strategy for Direct to Store Delivery" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
1. Strategy for Direct to Store DeliveryAuthors: Amit Panditrao and Kishore AdirajuAdvisor: Dr. Chris CapliceSponsor: Niagara Bottling, LLC1
2. IntroductionMethodology Data analysisTransportation modelSafety stock modelRecommendationsFuture research2MIT SCM ResearchFestAgenda
3. 3MIT SCM ResearchFestWhere do these products come from?
4. 4DC DeliveryPitfallsBulky and fast selling productsWarehousing costsMIT SCM ResearchFest
5. 5Direct To Store (DTS) deliveryBenefitsSales growthCompetitive advantageBetter in-stock levelsReduction in total supply chain costMIT SCM ResearchFest
6. Research questionWhat is the impact of DTS on supply chain costs? What is the best supply chain strategy to rollout DTS?Sponsor companyLargest private label bottled water manufacturer in the US6Thesis focusMIT SCM ResearchFest
7. Methodology 7MIT SCM ResearchFest
8. 8Methodology – OverviewMIT SCM ResearchFest
9. 9Methodology – DTS scenarios100% DCSingle-retailer 100% DTSMulti-retailer 100% DTSPartial DC and DTSMIT SCM ResearchFest
10. Data Analysis10MIT SCM ResearchFest
11. 11Data analysis – Network and storesGeographical dispersion of stores of Customer AAZ, CA, NV473 stores4 DCs1 Plant Plant DCsMIT SCM ResearchFest
12. 12Data analysis – DemandDTS demand: Lognormal (6.31, 0.54)Mean: 630 casesMedian: 560 casesRange: 0 – 5,124 casesStandard Dev: 370 casesMIT SCM ResearchFestPOS data of two product families
13. Transportation model13MIT SCM ResearchFest
14. Monte Carlo Simulation Annual cost and capacity estimatesBasic period – one weekAssumptionsRate per mileStop-off chargeTruck speedsLoading/Unloading timesOrder size / Stop distribution14Transportation modelMIT SCM ResearchFest
15. 15Transportation cost estimationMIT SCM ResearchFestLine haul3 Components: Line haul, local tour, stop-offStop-offStore clusterPlant13 store clusters in AZ, NV and CALocal tourStop-offStop-off
16. 16MIT SCM ResearchFestTransportation cost42% increase
17. 17MIT SCM ResearchFestTransportation cost reduces by 4%Local trip costStores more closely spacedMulti-customer delivery
18. 18Sensitivity – Order sizeMIT SCM ResearchFestStop-off contributes the maximum
19. 19Transportation vs Safety stockMIT SCM ResearchFest
20. Safety stock model20MIT SCM ResearchFest
21. InputsDemand per storeCustomer response time (CRT) – Time window in which the manufacturer must deliver product to the storeManufacturing lead time distribution (MLT)21Niagara’s safety stock modelMIT SCM ResearchFestOrder receiptDelivery dateCustomer response timeManufacturing lead timeManufacturing lead timeOn-time shipmentBack orderProbability (MLT > CRT)
22. 22Retailer’s safety stock modelLTDCSLDCLTStoreSLStoreLTDTSSLStoreSafety StockDCSafety Stock1StoreSafety Stock2StoreMIT SCM ResearchFestRetailer’s Safety Stock
23. Niagara’s safety stockMIT SCM ResearchFest23InputsDC CRT = 5 daysDTS CRT = 3 daysService level = 98.5%76% increase
24. Retailer’s safety stockMIT SCM ResearchFest24InputsDC to store = 1 dayNiagara to DC =5 daysNiagara to Store =3 daysDC service level = 75%Store service level = 99%65% increase
25. Recommendations25MIT SCM ResearchFest
26. Large order sizesLess complexity in scheduling store deliveryTransportation cost savings vs. inventory cost increaseMulti-customer deliveryConfidentiality issuesAdditional truck loading/unloading timeLead time reductionLesser safety stock in the systemTrade-off with transportation cost26RecommendationsMIT SCM ResearchFest
27. Faster and flexible productionFulfill smaller and frequent DTS ordersManufacture within customer response timeCollaborative partnershipForecasting, Promotion planning, Store orderingShare benefitsChange ManagementInternally – Sales, Logistics, Inventory planning, Demand planning, ITExternally – Retailer (Merchandising, Stores, Supply chain), Carrier management27RecommendationsMIT SCM ResearchFest
28. For a manufacturer Re-design network - Should the warehouse be located closer to metro areas?Own transportation fleet – With increased truck utilization, is owning a fleet worthwhile?Develop production flexibility – How much flexibility is needed for frequent and smaller DTS orders?For a retailer Evaluation methods for DTS proposal28Future researchMIT SCM ResearchFest
29. Q&A29MIT SCM ResearchFest