Michael Gilliland

Michael Gilliland

In the movie Slingblade, there is a great scene where they bring an apparently broken lawnmower to Karl (Billy Bob Thornton): “Karl, see if you can figure out what’s wrong with this. It won’t crank up and everything seems to be put together right.”  After a brief inspection, Karl responds “It ain’t got no gas in it.”

Sometimes it’s the simplest things that are most effective. And this certainly holds true in business forecasting.

There are several easy to understand, and easy to implement tools for evaluating the forecasting process. These tools utilize data you should already have.

One of these useful tools is the “comet chart,” which illustrates the relationship between demand volatility and forecast accuracy. As you would expect, when products have smooth and stable demand, we tend to forecast them more accurately than products with wild, erratic demand.

By creating a scatterplot of all products, showing their volatility and the achieved forecast accuracy (or error), you get a quick sense of the magnitude of your forecasting challenge. While volatility is not a perfect indicator of forecastability (there are volatile (yet well behaved) patterns that can be forecast accurately), it is of practical value in assessing your organization’s performance.

In a “forecastability matrix,” products are segmented into categories that are more (or less) forecastable, and more (or less) profitable. Since forecasting resources are always limited (no organization can afford an army of forecast analysts), forecasters can deliver more value by first focusing on those items that are more profitable and more forecastable. Those items that are difficult to forecast and have little contribution to profits have the lowest priority, and should receive little or no attention from the forecasting staff.

Forecast Value Added (FVA) analysis is another simple tool that has gained wide industry adoption. FVA looks at each step in the forecasting process, to make sure forecasting activities are “adding value” by making the forecast more accurate and less biased.

FVA identifies the waste and worst practices in a forecasting process. Many organizations have found things they are doing that just made the forecast worse! Such non- (or negative-) value adding steps can be eliminated, resulting in more effective use of company resources, and potentially more accurate forecasts.

Finally, a new approach for determining the “avoidability” of forecast error seems to be showing promise. This concept was proposed by Steve Morlidge, forecasting thought leader from the UK.  This approach seeks to determine the smallest amount of forecast error you can reasonably expect. This is another easy to apply tool, that can save you a lot of time by knowing when to stop trying to improve a forecast that has reached its limit of accuracy.

Michael Gilliland
Product Marketing Manager – Forecasting
SAS Institute

Hear Michael speak on simple, yet effective tools for evaluating the forecasting process at IBF’s Business Planning & Forecasting: Best Practices Conference in Orlando Florida, November 4-6, 2013.