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Using the trendline (or the formula) a planner can determine the inventory requirements for a given service level. For example, a service level of 95 percent should be assured with 3.3 MOH. The problem is that with the very low R2 value of 0.1021, only 10 percent of the historical service level was caused by the MOH inventory. In fact, 95 percent was frequently achieved with much less inventory, and the only month with more than 3.3 MOH actually had a service level below 95 percent! Obviously we needed a better method to determine our inventory requirements. Before we continue, I want to give you a quick overview of pyramid forecasting. Historical data is captured at the SKU level, SKU ratios are noted, and the data is aggregated by product family. Future demand is forecasted by factoring in such things as planned promotions, competitive activity and the product life cycle. The forecasted totals are then disaggregated back to the SKU level using the historical ratios. Inventory targets are determined by adding safety stock to the forecasted demand. The key point here is that inventory targets are established at the SKU level. We hypothesized that the relationship of the inventory to the SKU targets, rather than to the months on hand total, would be a better indicator of the expected service level. We conducted a test, and the result was a new analytical technique, which we call inventory profile analysis. First, we need some definitions: • SKU: Stockkeeping unit, a part number at a location • target: the desired inventory at the SKU level • actual: the projected or current inventory at the SKU level • coverage: the actual SKU inventory up to the SKU target • shortage: the additional SKU inventory needed to reach target • excess: any SKU inventory above target • index: coverage, shortage, or excess as a percentage of target • balance: the shortage (or excess) index when actual = target
•
IPA Formulas: Actual = Coverage + Excess I will demonstrate with a hypothetical product. Product family Y200 has five part numbers and is stocked at two DCs (Chicago and Oakland) for a total of 10 SKUs. The total target for all 10 SKUs is 100 units. Actual inventory is only 90 units. Conventional aggregation techniques would tell us we are at 90 percent of plan. The target for Y201 in Chicago is 10 units, but we only have three available there. This shortage of seven will cause service-level problems in Chicago despite the fact that we have seven excess units of Y201 in Oakland. Speaking of Oakland, excess quantities ofY201 and Y202 will not offset shortages of Y203, Y204, and Y205.
Figure 2 is an IPA chart. SKUs are on the X axis and quantity is on the Y axis. The bars are actual inventory and the line is the target. Coverage is the amount of any bar below the line, and excess is any amount above target. Shortage is the compliment of coverage and is seen here as the gap between a bar and the line. We have excess in four SKUs and shortages in six SKUs.
We are short 27 units, or 27 percent of target. We would say the IPA shortage index is 27. Coverage, the complement of shortage, is 73 units, so the coverage index is 73, which is probably a better indicator of expected service level than the 90 percent we had using traditional analysis.
Despite these serious shortages, product Y200 has an excess index of 17. Excess inventory has only a minimal impact on service level, known as the excess effect, and is a very inefficient use of resources. I will illustrate. Let's assume normal distribution and only two SKUs. Our target is two standard deviations, which will give us a 95 percent service level. But our actual inventory is only one standard deviation for one SKU and three for the other. Our total inventory is at target, but we are out of balance by one standard deviation. The excess effect of 3 percent (98 -95) and the shortage effect of 10 percent (95 - 85) will cause a net reduction in service level of 7 percent (10 - 7), from 95 to 88 percent. The same situation exists when demand is not normal. As long as we include safety stock in our target, we will be on the righthand side of the curve, and the excess effect will be smaller than the shortage effect. The actual amount will depend on the slope of the curve. The lesson to leam here is that balanced inventories will always have higher service levels than unbalanced inventories. IPA can be used to measure and report the degree of our inventory balance. To test IPA against MOH, SOLA conducted a parallel study of seven products, each consisting of several hundred SKUs, from July through December 1997. We downloaded data weekly, discarding the Christmas-New Year week as atypical. This time period included heavy backorders to full recovery. We will look at one product in detail and then a summary of all seven. SOLA proprietary information has been disguised. A 95 percent service level objective will be used as an example. To Be Continued STAY CONNECTED To stay current on Lean Management Basics and Best Practices, subscribe to our weekly MBBP Bulletin... and we'll send you our PowerPoint presentation "How to Survive in an Entirely New Economy." All at no cost of course. Your personal information will never be disclosed to any third party. privacy policy
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