An alternative to using a single forecasting method is to average the forecasts obtained from several methods. In this paper we investigate empirically the impact of the number and choice of forecasting methods on the accuracy of simple averages. It is concluded that the forecasting accuracy improves, and that the variability of accuracy among different combinations decreases, as the number of methods in the average increases. Thus, combining forecasts seems to be a reasonable practical alternative when, as is often the case, a “true” model of the data-generating process or a single “best” forecasting method cannot be or is not, for whatever reasons, identified.

The formal practice of forecasting and planning (F&P) has risen to prominence within a few decades and now receives considerable attention from both academics and practitioners. This paper explicitly recognizes the nature of F&P as future-oriented decision making activities and, as such, their dependence upon judgmental inputs. A review of the extensive psychological literature on human judgmental abilities is provided from this perspective. It is argued that many of the numerous information processing limitations and biases revealed in this literature apply to tasks performed in F&P. In particular, the “illusion of control,” accumulation of redundant information, failure to seek possible disconfirming evidence, and overconfidence in judgment are liable to induce serious errors in F&P. In addition, insufficient attention has been given to the implications of numerous studies that show that the predictive judgment of humans is frequently less accurate than that of simple quantitative models. Applied studies of F&P are also reviewed and shown to mirror many of the findings from psychology. The paper subsequently draws implications from these reviews and suggests reconceptualizing F&P through use of decision-theoretic concepts. At the organizational level this involves recognizing that F&P may perform many, often conflicting, manifest and latent functions which should be identified and evaluated through a multi-attribute utility framework. Operationally, greater use should be made of sensitivity analysis and the concept of the value of information.