There are unavoidable tradeoffs between inventory cost and item availability. The Smart Inventory Optimization (SIO) app calculates all the key metrics to expose those tradeoffs. You can try “what-if” experiments such as “What happens to shortage cost if we raise the reorder point from 5 to 10?”. Better yet, you can let SIO find the optimal operating policy, e.g., the lowest cost combination of reorder point and order quantity that guarantees a 95% service level.
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Head to Head: Which Service Parts Inventory Policy is Best?
Our customers have usually settled into one way to manage their service parts inventory. The professor in me would like to think that the chosen inventory policy was a reasoned choice among considered alternatives, but more likely it just sort of happened. Maybe the inventory honcho from long ago had a favorite and that choice stuck. Maybe somebody used an EAM or ERP system that offered only one choice. Perhaps there were some guesses made, based on the conditions at the time.
Leveraging ERP Planning BOMs with Smart IP&O to Forecast the Unforecastable
In a highly configurable manufacturing environment, forecasting finished goods can become a complex and daunting task. The number of possible finished products will skyrocket when many components are interchangeable. A traditional MRP would force us to forecast every single finished product which can be unrealistic or even impossible. Several leading ERP solutions introduce the concept of the “Planning BOM”, which allows the use of forecasts at a higher level in the manufacturing process. In this article, we will discuss this functionality in ERP, and how you can take advantage of it with Smart Inventory Planning and Optimization (Smart IP&O) to get ahead of your demand in the face of this complexity.
Why Inventory Planning Shouldn’t Rely Exclusively on Simple Rules of Thumb
For too many companies, a critical piece of data fact-finding ― the measurement of demand uncertainty ― is handled by simple but inaccurate rules of thumb. For example, demand planners will often compute safety stock by a user-defined multiple of the forecast or historical average. Or they may configure their ERP to order more when on hand inventory gets to 2 x the average demand over the lead time for important items and 1.5 x for less important ones. This is a huge mistake with costly consequences.