Safety Stock to Bridge the Forecast-Accuracy Gap

(From David McPhetrige, founder of TopDown Lean Systems, LLC, providing correct, comprehensive, multi-attribute safety stock analysis,

Surveys indicate that even world-class companies have average forecast accuracy in only the high 70%’s, especially at the SKU or component level that makes or breaks service levels and financial performance. Contributors to forecast inaccuracy include factors that can be addressed through best practices, and factors that will always be unpredictable:

  • Chronic bias, which can be minimized with effective Sales/Inventory/Operations Planning, or SIOP. (It’s a safe bet that world-class businesses are already doing this, and they are still achieving only high 70%’s.)
  • Unforeseen and unforeseeable special causes, such as natural disasters.

The reality is that forecasting can be only so accurate, in part because it predicts the timing and magnitude of only three types of variation:

  1. Trend
  2. Seasonality
  3. Certain foreseeable special causes, such as promotions

A forecast can predict the timing and magnitude of these variations only so well. Realistically, then, many businesses – and even world-class companies – are left with a significant forecast-accuracy gap that, if not bridged, compromises target fill rates, inventory performance and financial goals.

So first, how do you bridge this gap today? Obviously, you strive continuously to improve your forecasting accuracy, and you always will. That said, though, here are some bridging “techniques” that I’ve observed and even been part of, and likely you can think of some that I’ve missed:

  • Mind-set: Complain about, but begrudgingly resign yourself to, suboptimal inventory and fill-rate performance, and related costs and risks
  • P&L: Increase the budget for airfreight, expediters or other expediting costs and expenses
  • Balance Sheet: Increase safety-stock levels
  • Obsolete/Slow-moving: Increase efforts to return excess materials to suppliers

Some of these techniques can and do help achieve service-level targets. But at a minimum, they rarely help, and may in fact hurt, financial performance. Fortunately, a financially-beneficial service-level bridge from forecast to reality is right there in your data! How so?

Well, there is still variation that’s left over after bias, unforeseeable special causes and the three variations with predictable timing: Common-cause random variation in demand (or usage, for components) and replenishment lead time.

The good news is that it is possible to quantify the magnitude of random variation by analyzing historical demand and/or lead-time data. Of course, the timing of random variation is unpredictable (that’s what makes it random), and this means that common-cause random variation must be addressed with properly-determined safety stock.

At this point, you may be saying, “I’m already doing that. I use a statistical technique to determine my safety stock levels.” Forgive the blunt and clichéd reply, but – how’s that working for you? Honestly, is your technique consistently achieving your fill-rate targets on an item-by-item basis? Or does it often seem to put too much inventory in place? And in many cases, does it put too little inventory in place, and you have to subjectively override or increase the calculated safety-stock level?

The fact that your safety-stock calculation is unreliable does not mean that there is no statistical solution. What it does mean, however, is that you must

  1. Properly identify and isolate the common-cause random variation from all the other variations, and
  2. Use a correct, comprehensive statistical safety-stock approach that includes not just common-cause random variations in demand and lead time, but all six factors that affect safety stock, service levels, inventory performance and expediting.

We aren’t talking about a spreadsheet, or perhaps an unused ERP feature. We’re talking about outside expertise in safety-stock analysis.

A recent CSCO Insights executive brief (from Supply Chain Digest and Cognizant) entitled “Five Strategies for Improving Inventory Management Across Complex Supply Chain Networks” recommends this as the first of five strategies: “Get Much More Granular with Safety Stock Management.” (

This brief advises using “many more attributes associated with each SKU.” The result of this expanded effort is compelling: “The greater the [safety-stock] precision a company will have.” The CSCO Insights authors advise that to do this “obviously requires a lot more work,” and that this increased analysis offers “rich dividends.” The brief concludes that pursuing the strategy of a “higher level of safety stock management” may require a “relatively uncommon skill set” that may best be provided by outside expertise.