Inventory is one of the largest assets on the balance sheet of most manufacturing and distribution companies ― understandably making it a favorite cost reduction target by financial executives. My perception is that too often the effort is placed on reducing inventory levels without a good, manageable plan. To support both the financial and customer service plans, there are two elements to optimizing inventory and managing to that level.
The first step is to determine the optimum inventory in units at the item / SKU level. I have found that most companies tend to place a relatively low priority on this or go through the painful guesswork process no more than once per year. In fact it seems that the optimizing process is left to the production team and unfortunately that team approaches the problem in a “suboptimal” way.
Many times they build inventory as a byproduct of longer production runs for absorbing labor and overhead. Those longer runs also lessen the change over time which can build too much inventory of one item. Their colleagues, the production planning folks, understand the issues but rarely have analytical tools that can easily create an inventory plan in terms of safety stock and reorder points.
Solving the planning problem in a make-to-stock environment is relatively straight forward if you have the right tools. But first what and how do we want to optimize? There are two root causes of suboptimal inventory – first is an inaccurate demand forecast (forecast error) and second is the lack of consistency (lead time variability) by the production or procurement people.
If a reasonable customer service level (say, 95%) is agreed upon, then the task at hand is to draw on a large collection of detailed data stored in the operational data repository to calculate first the safety stock (or useless inventory to make up for mistakes in the forecast and production / procurement execution). Once that’s been done, the reorder point can be calculated to trigger new work orders. APICS defines these relationships clearly but the challenge is to have access to all the up-to-date detailed data along with a user-friendly tool that can do the calculations and what-if scenarios.
If the calculations are not run frequently (monthly), then the plan becomes outdated and operations manages to the wrong targets. Good execution by operations against a bad plan produces a bad result. Low inventory produces poor customer service, and bad fill rates and high inventory produces high carrying costs and increased cost from product obsolescence. Based on the variables driving safety stock and reorder point, the required levels of inventory can change significantly due to improvements in the forecasting process, reduction in production lead time, and improved consistency in supply as time goes on.
Executing an Inventory Plan
Obviously to execute the plan and manage to the targeted levels a company must have a robust operations data repository containing both the following current and historical data at the finished goods item level:
- Units by item and warehouse / distribution center
- $ by item and warehouse / distribution center
- Inventory trends or various time intervals
- Coverage by item and warehouse to meet the replenish plans
- Stock status (in, on order, in transit, allocated)
- Available To Promise
In today’s big data connected world it is an absolute must for any manufacturing and distribution company to have the infrastructure in place to support detailed analysis and reporting from a user’s perspective of all aspects of the inventory problem. Once available, executing an optimized inventory plan becomes much easier and much more profitable.
This post was written by John Hughes