Challenges in business forecasting, such as increasing accuracy and reducing bias, are best met through effective management of the forecasting process. Effective management, we believe, requires an understanding of the realities, limitations, and principles fundamental to the process. When management lacks a grasp of basic concepts like randomness, variation, uncertainty, and forecastability, the organization is apt to squander time and resources on expensive and unsuccessful fixes: There are few other endeavors where so much money has been spent, with so little payback.
This chapter provides general guidance on important considerations in the practice of business forecasting. The authors deal with:
- Recognition of uncertainty and the need for probabilistic forecasts
- The essential elements of a useful forecast
- Measurement of forecastability and bounds of forecast accuracy
- Establishing appropriate benchmarks of forecast accuracy
- The importance of precisely defining demand when making demand forecasts
- Guidelines for improving forecast accuracy and managing the forecasting function
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Although we were unable to secure rights to include it in this book, Makridakis and Talebâs âLiving in a World of Low Levels of Predictabilityâ from the International Journal of Forecasting is an important piece worth mentioning in any consideration of fundamental issues.
Spyros Makridakis is very well recognized as lead author of the standard forecasting text, Forecasting: Methods and Applications, and of the M-series forecasting competitions. Through his books, Fooled by Randomness and The Black Swan, Nassim Nicholas Taleb has drawn popular attention to the issue of unforecastability of complex systems, and made âblack swanâ a part of the vernacular. Their article, published in the International Journal of Forecasting (2009), speaks to the sometimes disastrous consequences of our illusion of controlâbelieving that accurate forecasting is possible.
While referring to the (mostly unforeseen) global financial collapse of 2008 as a prime example of the serious limits of predictability, this brief and nontechnical article summarizes the empirical findings for why accurate forecasting is often not possible, and provides several practical approaches for dealing with this uncertainty. For example, you canât predict when your house is going to burn down. But you can still manage under the uncertainty by buying fire insurance.
So why are the editors of a forecasting book so adamant about mentioning an article telling us the world is largely unforecastable? Because Makridakis and Taleb are correct. We should not have high expectations for forecast accuracy, and we 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 (nonforecasting) solutions to our underlying business problems. The method of forecast value added (FVA) analysis (discussed in several articles in Chapter 4) can be used to identify and eliminate forecasting process activities that do not improve the forecast (or may even be making it worse). And in many situations, large-scale automated software can now deliver forecasts about as accurate and unbiased as anyone can reasonably expect. Plus, automated software can do this at relatively low cost, without elaborate processes or significant management intervention.
For business forecasting, the objective should be:
The goal is not 100% accurate forecastsâthat is wildly impossible. The goal is to try to get your forecast in the ballpark, good enough to help you make better decisions. You can then plan and manage your organization effectively, and not squander resources doing it.
