Ford 供應(yīng)鏈管理

1、Supply Chain Monitoring Ford Parts Supply & Logistics Case StudyRoger MerkleFormer Manager, North American Inventory Planning DepartmentFord Parts Supply & Logistics (PS&L)Business OverviewPS&L operates a network consisting of: 198K unique part numbers, over 1M SKUs (part/location) W

2、ide mix of velocity, size, and value Vehicle base - 50 million vehicles on the road, 35 model years Logistics network - 2,000 suppliers and 5,900 authorized dealerships 18 HVCs, 3 HCCs, 1 LV/LC, 1 PRC, and 1 NPD Service Parts - US, Canada, Mexico and direct global export Complex logistics hubs, many

3、 containers, railcars, suppliers, packagers, sources/destinations, and pathsHigh degree of magnitude and complexitySUPPLIERSPACKAGERSREDISTRIB.CENTERREGIONALDISTRIBUTIONDEALERSFord PS&L Gearing Up for Change Acquired and centralizedrelevant data sources New systems for forecasting, inventory pla

4、nning, DRP, electronic supplier communication and management Implemented SupplierPerformance Monitoring Reduced inventory bytwo thirds Record customer servicelevels Record turn ratesPURCHASINGLow purchase price (High Inventory)(Stable schedules)(Long lead times)SOURCE(MAKE)DISTRIBUTION(Process-focus

5、ed)Stable Fixed CostsLow inventoryStable Part MixStable Schedules Low Transportation Cost DELIVERReturnsCUSTOMERFULFILLMENTVoice of the customer(High inventories)(High safety stock)(High Costs)expediting, overtimeRETURNSELLSUPPLYReliable Suppliers(High inventories)(Long lead times)FlexibleTransporta

6、tionPURCHASINGLow purchase priceLow InventoryFlexible schedulesShort lead timesDISTRIBUTIONHigh customerserviceStable Fixed CostsLow inventoryStable Part MixStable Schedules Low Transportation Cost CUSTOMERFULFILLMENTVoice of the customerHigh Service LevelsManaged inventoriesManaged safety stockMana

7、ged Costsexpediting, overtimeSUPPLYReliable SuppliersLow inventoriesShort lead timesFlexibleTransportationFord - Lack of Integrated DataFords Business ChallengesFord PS&L Supply Chain OverviewRETURNRETURNRETURNStocking PDC ConsumptionStocking PDC ConsumptionStockingPDC Consumptionfrom any PDCRec

8、lassx-ferEmergencyBypass /RedirectEmergencyEmergencyBypassReclassConsumptionDEALERSCount - 5,900Bypass /RedirectDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDto any PDCPARTSREDISTRIBUTIONCENTERNATIONALPARTSDEPOTCarrierCarrierCarrierPACKAGERSCount -7SUPPLIERSCount - 2,000SSSSSSSSSSSS

9、SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSREGIONALDISTRIB.CENTERSCount - 8PDCPDCPDCPDCPDCPDCPDCPDCDELIVER(MAKE) / DELIVERSOURCEPLANAnalytical Data updated weekly, at bestHighly Reactive - focus on backorders and blame assessment* Voluminous reports - both paper and electronic* Labor-intensi

10、ve to collectHigh levels of processing time variability+ Forecast error over supply chain process=High safety stock levelsIncreasing complex supply chain including external partners and sources as well as non traditional channelsDifferent cultures, processes and practices at each nodeMetrics not ali

11、gned - Data not commonMaterial expedited by teams of people at headquartersLittle ability to prioritize which actions are critical to the business requirementsForecast accuracy and safety stock managementFords RequirementsCulture Change Enable lean performance of existing systems No disruption to ex

12、isting operationsPrediction Support intelligent, proactive analysis vs. reactive Predict impact of current plan within lead time for resolutionPrioritization Drive data to lowest actionable level in organization Identify high-impact opportunities Combine forecasted and actual demand levels Manage ma

13、terial velocity based upon any desired variable of prioritization Manage escalationModel-based analytics Adapt to any supply chain, any level of data availability Calculate metrics across “white spaces” where data availability is poor Combine varying sources of dataClosed-loop issue management Suppo

14、rt analysis of current operating business systems Manage variability in real time w/ feedback to analysts / source systems Manage approval process for recommended changesComprehensive visibility Identify segments and processes with biggest problems Locate specific material throughout the supply chai

15、n Assess historical performanceSupport segmentationBuild vs. Buy DecisionWhy not custom? Integrated solution (data acquisition, data model, analysis, prediction, ad-hoc OLAP capability, security, alerting, administration) Investment in complex algorithm development Speed of implementation (rapid ROI

16、) Proven business value Teradata Supply Chain Intelligence (SCI) provides Standard KPIs, Reports & Alerts SCI Based on Industry Standards & Best PracticesTechnology Benefits Scalable database architecture Operational use of analytics Expandable and configurable data model and analytics Reduc

17、ed Support Costs Multiple database repository support Developed Exception Management System(alerting, escalation, message broadcasting)Ford purchased Supply Chain Intelligence (SCI) from Teradata, a division of NCR Process the analytic results into actionable information in the format and level appr

18、opriate for the operationProvide analytic results for 4 distinct audiences: Management - personalized for responsibility Performance metrics and trends for product, processes (including alerts themselves) and lines of business. Analysts Performance metrics identifying exceptions and outliers.Predict

19、ive performance and opportunities based upon statistics. Specific reports that address points of interestRecalls, missing, new product, new processes, new facilities etc. OperationsReactive alerts (standards) events that exceed standardProactive alerts (critical) product to be re-prioritized to prev

20、ent an issue PartnersLate shipment reports, trend analysesSCI Creates Actionable InformationSolution ExampleFords Three-Pronged SolutionFORD SUPPLY CHAIN EXAMPLE: PS&L OperationSupply Chain Modeling Utilize daily product position and business requirements snapshot Re-calculate projected quantiti

21、es and time via models. Projected customer service levels via variability analysis Comparison to expected aggregated Demand by family, SKU, path Detailed analysis of segment or aggregate performance by time, yield, capacity, constraints, . Model management at segment levels include:o Segment lead ti

22、meo Yieldo Split/merge/path selection Long term highly accurate forecasts not required Track and tune standards over timeSUPPLIERS(Count: 2,000)DemandINTERVALSSegmentAllocationSUPPLIERSCUSTOMERSPACKAGERS(Count: 7)REDISTRIB.CENTER(Count: 1)REGIONALDISTRIBUTION(Count: 10)DEALERS(Count: 5,900)REDISTRIB

23、UTIONCENTERREGIONALDISTRIBUTIONShip to In-YardIn-Yard toReceiptReceipt toStock-KeepSCI Modeling & SegmentationCritical Alerts In YardCritical Alerts In YardOver Standard AlertsInventory VisibilitySCI From Two To Eight Opportunities To Avoid Back OrdersChangeShipDateSUPPLIERSChangeShipDateNonePAC

24、KAGERS 1. Receive 2.Process1.Normal/CriticalREDISTRIB.CENTER1.Receive2.Stockkeep3.Normal/CriticalNoneREGIONALDISTRIBUTION1.Receive2.StockkeepDEALERSThe actual cycle time for this O/D pair averages over 6 days, so the assumptions for segment cycle time are not modeled properly.For the shipments throu

25、gh this segment, the average cycle time is compared to the standard.Parameter Management scores actual cycle time vs. the current model parameter to detect segments with a poor fit.Standards ManagementThe current model does take average segment variability into account. Now actual variability can be

26、 incorporated into the model to drive accurate safety stockNew model parameter evaluated for fit with actual shipment transaction cycle timesDrilling into the detailed data for each segment supports outlier identification and users can evaluate how well the model fits each segment within the supply

27、chain.All detailed data supporting the evaluation is available in the drill.Histogram shows the distribution of shipment cycle times as a percentage of the total shipmentsFord: Growth in User BaseFORD USER COUNTS - ACTUALS THROUGH PROJECTIONS050100150200250300350ANALYSTSPLANNERSDETROIT, LA, KANSAS CITYATLANTA, DALLASMEMPHISSAN FRANCISCOCHICAGOADDITIONAL USERSADDITIONAL USERSNEW PDC (location unknown)ADDITIONAL USERS2 NEW PDCs (location unknown)ADDITIONAL USERS3 NEW PDCs (location unknown)2 NEW PDCs (location unkn