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

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

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

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

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

9. Different forecasts*
*) Data of engine # , 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. Basic Implementation Always ship average quantity!
What happens to the inventory*?
Low inventory level: 600 units
High inventory level: 3300 units
*) Data of engine # , 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 Policy Engine #7781905-00 High runner
# of weeks for average: 3
Max. Inventory Position: 2509
Inflation/deflation: 1.8%

14. Performance Overview How does ship-to-average perform for this engine:
COSTS
TRANSPORTATION
Total
avoidable
costs
Inventory
Pipeline
Air costs
Air count
Air volume
Reduction
shipment
changes
-0.56%
+4.9%
-0.37%
-57.71%
-50.55%
-51.46%
Goal #1 achieved!

15. Shipment Comparison ship-to-forecast ship-to-average Goal #2 achieved!
(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
Goal #2 achieved!

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

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

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

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

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