International Supply Case Study bmw outline

BMW USA

• ~140,000 vehicles in 2004.
• KOVP (Customer-oriented production and sales)
• Over 6000 part numbers for X5
• 70% are option driven
• Flexibility for order change
• 40% of parts from Europe

KOVP

• Process Monitoring and Target Control

• Distribution Process
• and Hand-over

• Sales Processes and
• Online Ordering

• Production- and Supply- Processes

• Dealer

• Sales
• System

• Production
• System

• Sales
• System

• Dealer

• Planning

• Dealer order

• Purchasing

• Logistics

• Production

• Distribution

• Hand-over

• Ordering

• Delivery

• Sales Processes and
• Online Ordering

• Optimize the whole process

• KOVP The Push-Pull Interface

• Production System before KOVP

• Start Order Assignment

• Sort

• Sort

• Early
• Order Assignment

• Bodyshell work Paint shop Assembly

• Production System with KOVP

• Frozen
• Horizon

• Sort

• Late
• Order Assignment

• Start order assignment

• OSM

• Bodyshell work Paint shop Assembly

• Push

• Pull

Reduction of Leadtime

• Flexibility for Order Change

• Ordering/Scheduling

• Production/Distribution

• 15 WD

• 28-32 WD

• 13-17 WD

• Before KOVP:

• Order freeze

• Process Feasibility

• Supplier /
• Body shell work and
• Paint shop

• Change flexibility till 6 WD

• Distri-bution

• Assem-bly

• Hand-over to
• Sales

• 3 WD

• 10 WD

• 1

• 2 WD

• 4 WD

• Breakthrough target KOVP :

BMW USA

• ~140,000 vehicles in 2004.
• KOVP (Customer-oriented production and sales)
• Over 6000 part numbers for X5
• 70% are option driven
• Flexibility for order change
• 40% of parts from Europe

Sourcing

• Why source from Europe
• Relationship with suppliers
• Tooling is already there
• Social responsibility issues

Why serve global markets?

• Tooling
• Volume
• …

BMW Sourcing

• Wackersdorf

• Receive, Sort, Package
• Handles >14,000 part numbers from other BMW plants and over 500 European suppliers.
• Receives ~ 160 truckloads of parts per day
• Ships ~ 75- 80 containers per day to the BMW assembly plants in Rosslyn, South Africa, Spartanburg, South Carolina and Shenyang, China.

BMW: Capacity

• Capacity is a major investment
• Labor is highly skilled/ organized
• Production set at “takt time”
• “A vehicle every 50 seconds”
• Capacity adjustments through adjustments to takt time, adding/reducing shifts, shutdowns…
• Same number of cars/day

Manage Capacity

• From day to day
• Mix of vehicles vary
• Usage of parts vary

Manage Capacity

• Mix of vehicles

• Capacity oriented
• Production planning

• Seasonality

• Source: Goudiano CSCMP 2005

Manage Supply

• Over 6000 part numbers
• 70 % option driven
• Order changes
• 40% from Europe

Usage

• Average Usage 32/day

• Standard Deviation in Usage 18/day

• SAME NUMBER OF CARS/DAY

Managing Supply

• Forecast

• Shipments
• Arrive

• Decide Shipment
• Quantities

• Demand

• Day 1

• Day 10

• Day 40

• Demand

• Demand

• Demand

• Prepare
• Shipments

Challenge

• Huge number of parts: Complexity
• Order Flexibility: Variability
• Long LeadTimes: Variability

Levers for managing uncertainty

• Capacity
• Capacity on Supply
• Production Capacity
• Inventory
• Time
• Order due date

• Infinite

• Constant

• Given/Strict

Manage Inventory

• “Infinitely” many end products from finite number of parts
• Stochastic demand
• Variable long leadtimes
• No shortages allowed:
• Production in a predetermined sequence
• Expedite

Demand Modeling

• Infinitely many end products
• Not enough data points to estimate distribution of product demand
• Instead: Components

Challenge

• Huge number of parts: Complexity
• Order Flexibility: Variability
• Long LeadTimes: Variability
• No shortages allowed

Some Tools & Mechanisms

• Safety Stock
• Forecast Accuracy
• Frequency
• Global Supply process
• …

Safety Stock

• Protection against variability
• Variability in demand and
• Variability in lead time
• Typically described as days of supply
• Should be described as standard deviations in lead time demand

Traditional basics

• Time

• Stock on hand

• Reorder Point

• Order placed

• Lead Time

• Reorder Point

• Actual Lead Time Demand

• Actual Lead Time Demand

• Order Quantity

• Actual Lead Time Demand

• Actual Lead Time Demand

• Order-up-to level

• T

• L

Safety Stock Basics

• Lead time demand N(, )
• Safety stock levels
• Choose z from N(0,1) to get correct probability that lead time demand exceeds z,
• Safety stock is z

Safety Stock in Periodic Review

• Probability of stock out is the probability demand in T+L exceed the order up to level, S
• Set a time unit, e.g., days
• T = Time between orders (fixed)
• L = Lead time, mean E[L], std dev L
• Demand per time unit has mean D, std dev D
• Assume demands in different periods are independent
• Let Ddenote the standard deviation in demand per unit time
• Let Ldenote the standard deviation in the lead time.

Only Variability in Demand

• If Lead Times are reliable
• Average Lead Time Demand
• (T+L) * D
• Standard Deviation in lead time demand
• (T+L)D

Lead Time Variability

• If Lead Times are variable
• D = Average (daily) demand
• D = Std. Dev. in (daily) demand
• L = Average lead time (days)
• L = Std. Dev. in lead time (days)
• Average lead time demand
• D(T+E[L])
• Std. Dev. in lead time demand
• (T+E[L])2D + D2 2L
• Remember: Std. Dev. in lead time demand drives safety stock

Levers to Pull

• Std. dev in lead time demand
• (T+E[L])2D + D2 2L

• Reduce Lead Time

• Reduce Variability in Lead Time

• Reduce Variability in Demand

• Reduce Time between orders

Safety Stock

• Protection against variability
• Variability in demand and
• Variability in lead time
• Typically described as days of supply
• Should be described as standard deviations in lead time demand

• Example: BMW safety stock
• For axles only protects against lead time variability
• For option parts protects against usage variability too