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Information about ShipToAverage

Published on November 7, 2007

Author: UpBeat


Slide1:  “Ship-to-Average“ by Matthias Pauli Thomas Drtil Claus Reeker Stefan Lier Christopher Vine Fernando Cruz BMW Project Plant Spartanburg:  Plant Spartanburg ~140,000 vehicles in 2004 Over 6,000 part numbers for X5 70% option driven 40% of parts from Europe Slide3:  Supply Chain Challenges:  Challenges Demand Variability*:  Demand Variability* Standard Deviation: 42/day Mean Demand: 78/day *) Data of engine #7781905-00, high runner BMW policy: Ship-to-forecast:  Order Arrival BMW policy: Ship-to-forecast Inventory:  Inventory On-hand inventory* with ship-to-forecast: constant level? *) Data of engine #7781905-00, high runner Forecast error:  Forecast error Why try to chase the daily forecast? % Different forecasts*:  Different forecasts* *) Data of engine #7781905-00 , high runner Approach: Ship-to-average:  Approach: Ship-to-average Don’t ship to daily forecast Consider a longer forecast period instead “Keep shipments constant, let the inventory swing“ Goals: #1) Minimum impact on total avoidable costs #2) More stability for the supply chain Basic Implementation:  Basic Implementation Always ship average quantity! What happens to the inventory*? *) Data of engine #7781905-00, high runner How to control the inventory?:  How to control the inventory? Inventory Position Time Max. Inventory Position Inflate shipments: Avg. forecast (x weeks) * inflation factor Deflate shipments: Avg. forecast (x weeks) * deflation factor (almost) constant shipment quantities ! Which Part analyzed?:  Which Part analyzed? Part Engine #7781905-00 High runner Policy # of weeks for average: 3 Max. Inventory Position: 2509 Inflation/deflation: 1.8% Performance Overview:  Performance Overview How does ship-to-average perform for this engine: Shipment Comparison:  Shipment Comparison ship-to-forecast ship-to-average (shipment adjustment: 66%) (shipment adjustment: 14%) = shipment quantity changes more than 10% compared to previous one Shipment adjustments happen in 14% of all shipments What’s next?:  What’s next? Goals achieved! Optimized policy works. But how robust is the result? What are the trade-offs? How do the 3 parameter… # of weeks for average Max. inventory position Inflation/deflation factor … influence the result? Sensitivity Analysis:  # of weeks for average: Sensitivity Analysis Sensitivity Analysis:  Sensitivity Analysis Max. Inventory Position: Sensitivity Analysis:  Sensitivity Analysis Inflation/deflation factor: Summary Table :  Summary Table Advantages:  Advantages Small cost reduction compared to current ship-to-forecast policy Less variation in order quantities Less bullwhip effect Easier operations for Spartanburg/ Wackersdorf/ upstream suppliers Facilitates negotiation with transportation partner Limitations of the study:  Limitations of the study Simulation vs. reality Restricted original data sets provided Small number of parts considered Constant shipment frequency assumed (once per week) Recommendations:  Recommendations Run pilot to check performance: pick high runner with relatively stable demand over time Analyze larger set of parts Evaluate cost savings upstream Evaluate trade-off between higher savings and increasing expediting Q&A:  Q&A

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