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

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

Supply Chain

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

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

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

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

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!

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

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

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

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

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!

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!

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: Total avoidable costs [$] Air costs [$]

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

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

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

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

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