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Optimizing Supply Chain with Ship-to-Average: A Case Study by BMW Project Team

Explore the BMW Project "Ship-to-Average" by Matthias Pauli, Thomas Drtil, Claus Reeker, Stefan Lier, Christopher Vine, and Fernando Cruz. The study analyzes the impact of demand variability on supply chain management and proposes a ship-to-average approach to improve supply chain stability. Results show reduced costs, minimized inventory swings, and improved supply chain performance. Learn about the implementation, performance overview, sensitivity analysis, advantages, limitations, and recommendations for future implementations.

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Optimizing Supply Chain with Ship-to-Average: A Case Study by BMW Project Team

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  1. BMW Project “Ship-to-Average“ by Matthias Pauli Thomas Drtil Claus Reeker Stefan Lier Christopher Vine Fernando Cruz

  2. Plant Spartanburg • ~140,000 vehicles in 2004 • Over 6,000 part numbers for X5 • 70% option driven • 40% of parts from Europe

  3. Supply Chain

  4. Product Variety Built-to-order Demand Variability What Impact ? Long Lead-Times 100% Customer Satisfaction Late Order Changes Fixed Delivery Dates Challenges

  5. Demand Variability* Standard Deviation: 42/day Mean Demand: 78/day *) Data of engine #7781905-00, high runner

  6. Order Placement Forecast Shipping ≠ Demand Demand Demand Demand Day 1 Day 10 Day 40 BMW policy: Ship-to-forecast Order Arrival

  7. Inventory • On-hand inventory* with ship-to-forecast: • constant level? *) Data of engine #7781905-00, high runner

  8. Use forecast accuracy over longer period of time! Use forecast accuracy over longer period of time! Use forecast accuracy over longer period of time! Forecast error • Why try to chase the daily forecast? %

  9. Different forecasts* *) Data of engine #7781905-00 , high runner

  10. 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

  11. High inventory level: 3300 units Low inventory level: 600 units Basic Implementation • Always ship average quantity! • What happens to the inventory*? *) Data of engine #7781905-00, high runner

  12. How to control the inventory? Deflate shipments: Avg. forecast (x weeks) * deflation factor Inflate shipments: Avg. forecast (x weeks) * inflation factor Max. Inventory Position Inventory Position (almost) constant shipment quantities ! Time

  13. Which Part analyzed? • Part • Engine #7781905-00 • High runner • Policy • # of weeks for average: 3 • Max. Inventory Position: 2509 • Inflation/deflation: 1.8%

  14. Goal #1 achieved! Performance Overview • How does ship-to-average perform for this engine:

  15. Goal #2 achieved! Shipment Comparison ship-to-forecast (shipment adjustment: 66%) = shipment quantity changes more than 10% compared to previous one ship-to-average (shipment adjustment: 14%) Shipment adjustments happen in 14% of all shipments

  16. 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?

  17. Total avoidable costs [$] Air costs [$] Sensitivity Analysis • # of weeks for average:

  18. Total avoidable costs [$] Air costs [$] Sensitivity Analysis • Max. Inventory Position:

  19. Total avoidable costs [$] Air costs [$] Sensitivity Analysis • Inflation/deflation factor:

  20. Part # 1092396-00 HIGH 6756673-00 HIGH 6762958-00 HIGH 7781905-00 HIGH 7783354-00 HIGH 1552166-00 LOW 6753862-00 LOW 7759119-00 LOW 7781903-00 LOW Total avoidable cost (incl. air cost) -0.36% -6.47% -5.70% -0.56% -0.45% -3.87% -1.67% -0.94% -3.85% Air cost +25.25% -77.14% +461.28% -57.71% -5.36% +15.22% -14.00% -60.45% 0.00% Shipment changes -47.11% -32.22% -16.32% -51.46% -55.83% -29.60% -48.84% -21.79% -56.04% Summary Table

  21. 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

  22. Limitations of the study • Simulation vs. reality • Restricted original data sets provided • Small number of parts considered • Constant shipment frequency assumed (once per week)

  23. 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

  24. Q&A

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