THE PERILS OF DEMAND FORECASTING Craig Cather President

THE PERILS OF DEMAND FORECASTING Craig Cather President & CEO Automotive News Manufacturing Conference 14 June 2005

Today’s Presentation • Why Build-to-Order? • Challenges of Build-to-Order • Forecasting Demand • Opportunities for the Future

WHY BUILD-TO-ORDER? Customer Satisfaction Inventory Reduction

CHALLENGES OF BUILD-TO-ORDER "By 2003 or 2004, up to 80 percent of our buyers could be specifying the vehicles they want to buy" Harold Kutner, General Motors July 31 st, 2000 Source: The. Car. Connection. com

CHALLENGES TO BUILD-TO-ORDER • • Implications vary by Market Supply Chain is not structured for BTO U. S. Dealers want Inventory Specifications are less critical to customers than price and delivery • Forecasting Demand is a Daunting Task

FORECASTING DEMAND Current Industry Performance • • Capacity Planning Volumes (CPVs) Monthly Production Variance Inventories Option Complexity

FORECASTING DEMAND Capacity Planning Variation – GM Annual average volume variance from CPV expectations over the life of the program

FORECASTING DEMAND Capacity Planning Variation – Ford P 221 F-Series P 131 F-Series C 170 Focus V 229 Freestar M 205 T-Bird U 204 Escape EN 53 CV/GM J 56 Mazda 6 Annual average volume variance from CPV expectations over the life of the program

FORECASTING DEMAND Capacity Planning Variation – DCX DR Ram WJ G. Cher JR Sedans KJ Liberty NPL Neon CS Pacifica Annual average volume variance from CPV expectations over the life of the program

FORECASTING DEMAND Capacity Planning Variation – New Domestics Annual average volume variance from CPV expectations over the life of the program

FORECASTING DEMAND Lowest Production Variance Coefficient of Variation • Lowest production variability among North American produced vehicles • MDX had lowest cumulative production variation over past 3 years • Honda and Toyota dominated due to flexible manufacturing May 2002 – April 2005 • Big 3 vehicles not far behind: Ram, FSeries Super Duty, Silverado

FORECASTING DEMAND Greatest Forecast Variance Coefficient of Variation • Greater variation in production due to seasonality of vehicle, rampup/ramp-down scenarios or weak product Average MDX Existing Programs Launch Programs May 2002 – April 2005

FORECASTING DEMAND US Light Vehicle Inventory Source: Autodata

FORECASTING DEMAND Delivery Delays due to Option Availability Vehicle Option Delay May Inventory (days) Moonroof 4 weeks Safety Package 4 weeks AWD 4 weeks Ford Expedition Rear Entertainment System 4 weeks 98 Lincoln Navigator Rear Entertainment System 4 weeks 116 Ford Explorer Black Running Boards 4 weeks 96 Mercury Mountaineer Black Running Boards 4 weeks 96 Cadillac STS AWD 5 -speed auto 4 weeks 88 Duramax Diesel Engine 4 weeks Two-tone Paint 8 weeks Side Airbags 4 weeks Onstar 4 weeks 2. 4 L L 4 Engine 4 weeks Ford Five Hundred Ford Freestyle Mercury Montego Chevrolet Silverado Hummer H 2 Pontiac G 6 78 118 133 83 67 59

FORECASTING DEMAND Synopsis • Variation from “the plan” is creating great inefficiencies throughout the system • Inflated inventories are forcing OEMs to provide incentives or sell to low-margin fleet markets in order to move product • Forecasting content mix accurately is a significant problem

OPPORTUNITIES FOR THE FUTURE Stability, Commonization & Flexibility • • • Improve Product Cadence Stability Increase Parts Commonization & Re-use Standardize Processes Stabilize Procurement Increase Plant Flexibility Bring Logistic Providers into the planning process earlier • Push Vehicle Contenting Downstream

Craig Cather President & CEO CSM Worldwide, Inc. +01 (248) 380 -9000 craigcather@csmauto. com