Posted By Mark Symonds, October 13, 2011 at 11:39 AM, in Category: The Adaptive Organization
Efforts to cut costs in these financially trying times, coupled with Lean initiatives that strive to remove waste from the supply chain, have resulted in many manufacturers reducing inventory to the point where they may be more vulnerable to supply disruptions than they realize.
Cutting inventory is easy – just buy less or build less and let demand reduce the amount remaining on hand. One would hope that few companies would arbitrarily cut inventory without a strategy or an understanding of why inventory is there in the first place, and the impact of removing inventory. Nevertheless, companies are sometimes surprised when shortages disrupt production schedules, or cause customer service problems whose cost far exceeds the savings garnered from the inventory reductions.
There is a definitive relationship between inventory, customer service, and uncertainty (also called variability, risk, or forecast error). For a given level of uncertainty, an amount of inventory will deliver a certain level of customer service (risk of stock-out). Changing any one of these factors necessitates a change in at least one other. If one were to reduce inventory with no change in uncertainty, customer service would go down. Want a higher customer service level? Reduce uncertainty or add inventory.
If the goal is to reduce inventory, as it is in somany instances today, be prepared to pay the price in service level unless you can do something about uncertainty. For finished goods, the uncertainty is mostly determined by forecast accuracy. Improve the forecast, and you can reduce inventory without increasing the risk of stock-out.
But forecasting is typically a difficult thing to improve. Few people really like to forecast due to fear of being wrong and looking bad. The one certainty is that you know it will be wrong. The most important thing is to reduce the size of the errors.
Several strategies are essential if you are to reduce forecast error. The first is to get more information sooner, preferably from as far down the distribution chain as possible. Point-of-sale data for consumer goods products is the ultimate measure of demand. It captures actual product consumption and signals what consumers want and don’t want. In other environments, collaborative forecasting with distributors, retailers, and other trading partners helps companies get closer to the source of demand.
Another uncertainty-reduction strategy is to cut lead time. Shorter lead time means that the supply chain can react more quickly to changes in demand. If I can cut the lead time in half, I only need to forecast half as far into the future.
Manufacturers can reduce lead times by implementing more flexible manufacturing, allowing rapid changeover from one product or variety to another, and by streamlining the production process, removing waste and inefficiency. Manufacturers need to look beyond their own walls and collaborate with suppliers to find ways they can contribute to lead time reduction.
Inventory reduction is an important tool for managing through difficult economic times, and also a major target of Lean initiatives. Just be sure that as you reduce inventory, you fully understand the consequences. An inventory reduction unaccompanied by a proportional reduction in uncertainty will inevitably lead to a higher risk of shortages.
Written by Mark Symonds
With a background in IT business consulting from Arthur Andersen (now Accenture), Symonds is also a Certified Public Accountant, and is certified in production and inventory management by the American Production and Inventory Control Society. He is also a member of many industry associations, including the Precision Metalforming Association, Industrial Fastener Institute, the Forging Industry Association, the Automotive Industry Action Group and the Original Equipment Suppliers Association. Symonds holds an MBA in finance and accounting from Cornell University's Johnson Graduate School of Management and a bachelor's degree in economics and French from the University of Rochester.