“Ship-to-Average“ by Matthias Pauli Thomas Drtil Claus Reeker Stefan Lier Christopher Vine

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