Iâm going to put âAn Operational Definition of âDemandâ â Part 3â on hold for a moment, to announce a new favorite article on forecasting, âLiving in a world of low levels of predictability,â by Spyros Makridakis and Nassim Taleb…
Iâm going to put âAn Operational Definition of âDemandâ â Part 3â on hold for a moment, to announce a new favorite article on forecasting, âLiving in a world of low levels of predictability,â by Spyros Makridakis and Nassim Taleb (International Journal of Forecasting 25 (2009) 840-844. IJF is a publication of the International Institute of Forecasters, and if not already a subscriber you can purchase the article from ScienceDirect.)
Many of you already know Makridakis as co-author of the standard forecasting text Forecasting: Methods and Applications, and Taleb for his Fooled by Randomness and The Black Swan. Taleb, in particular, has drawn attention to the issue of the un-forecastability of complex systems, and the sometimes disastrous consequences of our âillusion of control, pretending that accurate forecasting was possibleâ (p.841).
While referring to the (mostly unforeseen) global financial collapse of 2008 as a âprime example of the serious limits of predictabilityâ (p.240), this brief and non-technical article summarizes the empirical findings for why accurate forecasting is often not possible, and provides several practical approaches for dealing with this uncertainty.
So why am I, a vendor of forecasting software, so excited by an article telling us the world is largely unforecastable? Because Makridakis and Taleb are correct â we should not have high expectations for forecast accuracy, and should not expend heroic efforts trying to achieve unrealistic levels of accuracy. Instead, by accepting the reality that forecast accuracy is ultimately limited by the nature of what we are trying to forecast, we can instead focus on the efficiency of our forecasting processes, and seek alternative (non-forecasting) solutions to the business problem. Methods I have touted, such as Forecast Value Added analysis, can be used to identify and eliminate forecasting process activities that do not improve the forecast (or may even make it worse). Large-scale automated software, such as SAS Forecast Server, can deliver forecasts about as accurate and unbiased as anyone can reasonably be expected â and do this without elaborate processes or significant management intervention. For business forecasting, the objective should be:
To generate forecasts as accurate and unbiased as can reasonably be expected â and to do this as efficiently as possible.
The goal is not perfect forecasts â that is wildly impossible. The goal is to try to get your forecast in the ballpark, so you can plan and manage your business effectively, and not waste a lot of company resources doing it. And when, because of the nature of demand or other behavior, you cannot forecast with the degree of accuracy needed for effective planning, then seek alternative approaches to address the underlying business problem. In the past Iâve suggested things like demand smoothing (to make the demand forecastable), or supply chain re-engineering (to minimize your reliance on accurate forecasts). You can find more discussion of these in a 2001 article I co-authored with Drew Prince of NCR, âNew Approaches to Unforecastable Demandâ (Journal of Business Forecasting, Summer 2001, pp. 9-12), available for download from the Institute of Business Forecasting.