How Good Is a "Good" Forecast?: Forecast Errors and Their Avoidability

With this article, Foresight continues its examination of forecastabilityÐ the potential accuracy of our forecasting efforts Ð which is one of the most perplexing yet essential issues for the business forecasting profession. We first tackled the subject with a special feature section in our Spring 2009 issue. The introduction there indicated that assessing the forecastability of a historical time series can give us a basis for judging how successful our modeling has been (benchmarking), and how much improvement we can still hope to attain. ForesightÕs Summer 2012 issue advanced the discussion with a feature article showing how to use a productÕs DNA Ð product and market attributes such as the length, variability, and market concentration of sales Ð to develop benchmarks for forecast accuracy. The essential idea here is to better understand the specifics of those items we are trying to forecast and to set expectations accordingly. Certain key concepts emerged from the articles in that section that helped clarify the meaning of forecastability and the challenges underlying its analysis: ¥ The lower and upper bounds of forecast accuracy Ð the worst and best accuracy to be expected ¥ The relationship between the volatility of our sales histories over time and their forecastability ¥ The limitations of the coefficient of variation in measuring forecastability and a potentially better alternative in a metric of entropy Now Steve Morlidge offers a tantalizing new perspective on forecastability. His approach seeks to determine what portion of forecast error for any item is avoidable, in principle and in practice. The simplicity of the metric he creates should be very appealing to business forecasters, seeing that it offers a convenient way to compare accuracy results across products. Copyright International Institute of Forecasters, 2013