In an earlier article, we talked about how supply chain managers should be “working their tails off” when it comes to reshaping the profile of the overall inventory in their supply chain.
By this (pun intended), we meant attacking the tails of the typical inventory profile (on the left in the figure above). The “tails” to which we referred are the all too common “too much stock” and the “out-of-stock” tail ends of the typical bi-modal inventory distribution found in most supply chains. Inventory and supply chain management teams can work effectively at these “tails,” and with each effective attack, an increasing number of SKUs can be pushed into the middle range—the “yellow zone”—of the stock profile.
In order to identify offending SKUs and locations, inventory and supply chain executives and managers need to have effective metrics for measuring “the tails.” We suggest considering two KPIs that others have found effective.
The first metric provides information on the depth and breadth of overstock positions—the right-hand tail. Days OTOG (over top of green) is usually configured to report the number of days each SKU spent with quantities in their stock buffers that were over-the-top of green (or their designated maximum stock level). In most cases, in order to screen out the occasional offender, some minimum number of days is screened out in applying the metric. In the example shown in the accompanying diagram, a 15-day base is selected.
In such a case, then, OTOG Days becomes days spent OTOG in excess of 15 days out of the last 180 days. The KPI basis values may vary from enterprise to enterprise and should be managed by the inventory and supply chain management team.
By reporting on the, and charting them (as illustrated), it becomes a simple matter to track down the worst offenders (SKUs #304P and #101, for example) and to have a cross-functional team identify why these SKUs are ending up in this condition. Were bad buying decisions made? Were production and replenishment orders placed based on forecasts instead of actual demand? Has demand suddenly fallen off?
Whatever the cause, the matter should not be taken lightly nor simply shrugged off. Instead, concerted action should be undertaken to help assure that the causes affecting these SKUs are mitigated so as not to affect other SKUs, as well.
Days OOS and OSWD
The metrics used to attack the other tail end—the left-hand tail—deals with out-of-stocks (OOS) and, the worst case, OSWD (out-of-stocks with [actual] demand).
In a fashion similar to the OTOG metric above, OOS and OSWD count the number of days any item spends in an out-of-stock position (over the last 180 days), and also the number of OOS days that were accompanied by actual consumer-driven demand. Here again, the baseline parameters—like the number of days in the look-back period—should be decided upon by the inventory and supply chain management team.
Also, by charting the SKUs in a fashion similar to that shown, the worst offenders can be addressed in priority order. Root-causes should be determined and each SKU-Location (SKU-L) systematically attacked in order to prevent recurrences or from similar outcomes hitting other SKU-Ls.
Your business isn’t static—your supply chain settings shouldn’t be either
Most of the companies and supply chains that we meet for the first time have relatively static stocking levels. Stocking minimums, maximums, and safety stock levels are set relatively infrequently. In some cases, they have not been changes—literally and sadly—in years!
Just like the KPIs above collect data on stock positions daily, we generally encourage inventory and supply chain managers to automate the adjusting of stock positions based on the latest, most up-to-date, information available to the system on average daily usage (ADU) and lead times.
While it may seem overkill to update statistics on ADU and (actual) lead-times on a daily basis, we find that it is not. Some worry that daily updates are likely to increase system nervousness, but actually just the opposite is true. If you are updating weekly or monthly, the change in ADU or lead-time is likely to be larger with each update. However, if these are updated daily, the incremental change is likely to be very small.
These ideas were adapted from Demand Driven Performance Using Smart Metrics by Debra Smith and Chad Smith. I would strongly encourage you to get a copy of this book. It will be well worth it—if you take the advice in the book.
We would like to hear your comments or field your questions, as well. Please leave your comments / questions below, or feel free to contact us directly, if you prefer.