This component export division of a major automobile manufacturer had grown revenues from less than $500,000 to over $1 billion in less than three years.
There was a lack of proper systems and forecasting was based largely on forward release information from the manufacturing plants scattered around Europe and US.
Inventory levels were high and growing, yet shortages were also increasing. Inventory was aging, indicating too much of the wrong product and too little of the needed products.
Warehouses were located close to assembly facilities. These warehouses were contractually required to meet necessary demand and any shortages that resulted in line stoppage generated fines in excess of $1 million per hour of downtime.
Inventory management was based on total demand expectation sourced from the assembly line projected production schedules. These changed daily. Overall draw of each SKU was consistent with demand plans over the long term (product life cycle) but varied widely from these projections in the short term. Variations in the range of +/- 300% were common.
Communication with assembly lines was poor and generally through e-mail.
Airfreight was used to top-up supply where imminent shortages were expected. These late stage air freight contracts were expensive and often difficult to secure.
The client was in the midst of implementing BaaN as an ERP/MRP solution.
System revisions had not been undertaken due to the BaaN implementation, which was running over 16 months behind schedule. Hence, all forecasting, order tracking, inventory control and planning for the component export division was completed using spreadsheets and no linkages between product managers existed increasing risks of error due to lack of version control.
A completely new methodology of forecasting was developed and implemented. The forecasting methodology used a hybrid approach which combined market information, historical data and customer data to more accurately forecast future demand.
Demand variation was also assessed, and safety stock levels were dynamically set each week based on several critical supply chain factors.
Optimum inventory levels were established, and set based on desired service levels comparing holding costs to negotiated airfreight alternatives. This drove down on-site storage levels by over 45%.
Airfreight was planned and contracts negotiated for 95% of air freight demand driving down airfreight costs by over 27%.
Communication with the assembly lines was significantly improved allowing better understanding of forward release information.
Cooperation with late-stage production plan changes helped reduce fines for line stoppages by over 85%.
