Editor Len Tashman's preview of the Winter 2016 issue of Foresight

This 40th issue of Foresight begins with a review of the new book by Philip Tetlock and Dan Gardner with the enticing title Superforecasting: The Art and Science of Prediction. Reviewer Steve Morlidge explains that

…the “superforecasters” of the title are those individuals who consistently outperformed other members of Tetlock’s team, and the book sets out to answer the question, “What makes these people so effective as forecasters?”

Perhaps no issue has received more attention in the forecasting literature than that of the relative merits of simple vs. complex forecasting methods. Although the definitions of simple and complex have varied, many studies report evidence that simple methods are often as accurate – and occasionally more accurate – than more complex counterparts.

Now, the two articles in our section on Forecasting Principles and Methods help us to better understand why simplicity in method choice can be a virtue.

In our first piece, Bias-Variance Trade-offs in Demand Forecasting, Konstantinos Katsikopoulos and Aris Syntetos begin by illustrating how a forecast-accuracy metric can be decomposed into two distinct attributes: bias, or a tendency to systematically over- or under-forecast; and variance, the magnitude of fluctuation in the forecasts. They then illustrate that

…simple methods tend to have large bias but lower variance, while complex methods have the opposite tendency: small bias but large variance. So we might prefer a method with smaller variance even if it has larger bias: that is, we can reduce the (total error) by replacing an unbiased but high-variance forecast method with a biased but low-variance forecast method. More generally, we should seek the right amount of complexity.

Stephan Kolassa, for his article in this section, not only endorses their argument but goes a step further to show why Sometimes It’s Better to Be Simple than Correct:

…correct models—those that include all the important demand-influencing factors—can yield bad forecasts. What’s surprising is that a correct model can yield systematically worse forecasts than a simpler, incorrect model!

The bottom line is that complexity carries dangers, and so it is particularly unwise to tweak out a small increase in model performance—a practice to which we often succumb.

Foresight has devoted much space in the past five years to descriptions and evaluations of efforts to promote Collaborative Forecasting and Planning, such as sales and operations planning and information sharing across supply-chain partners. But many firms report they’re not satisfied with the results, that the integration they seek across functional areas—forecasting, sales, marketing, operations, finance—has not occurred, and that the often expensive systems they’ve installed have not overcome the functional silos that impede achievement of promoting company-wide objectives. Dean Sorensen draws upon his decades of experience in advising firms on integrated planning to offer an explanation for this corporate dissatisfaction. He observes that

…as (organizational) complexity rises, capability gaps are exposed in processes that are supported by separate S&OP, financial planning, budgeting, and forecasting applications. What’s missing is a planning and forecasting process that breaks down functional silos by integrating strategic, financial, and operational processes, and extending beyond manufacturing to broader supply chain, selling, general, and administrative activities.

New, integrative technologies are emerging, and Dean describes how these technology innovations provide incremental capabilities that stand-alone S&OP and financial planning, budgeting, and forecasting applications do not.

Dean discusses his experience in integrative planning in our Forecaster in the Field interview.

In our section on Forecasting Practice, sales-forecasting specialist Mark Blessington challenges the conventional systems for setting sales quotas, which are based on annual business plans with sales targets for the corporation and its divisions and territories. In Sales Quota Accuracy and Forecasting, he reports evidence that

…quotas are better set on a quarterly rather than annual basis, and quarterly exponential smoothing methods yield far more accurate quotas than traditional quota-setting methods. However, firms must anticipate implementation barriers in converting from annual to quarterly quotas, as well as the possibility that sales representatives may try to game the system by delaying sales orders to maximize future bonus payouts.

Our Strategic Forecasting section addresses forces that transcend short-term forecasts to look beyond the current planning horizons. TechCast Global principals William Halal and Owen Davies present TechCast’s Top Ten Forecasts of technological innovations and social trends over the next 15 years and beyond.

They see several disruptive technological developments:

(1) Artificially intelligent machines will take over 30% of routine mental tasks. (2) Major nations will take firm steps to limit climate-change damage. (3) Intelligent cars will make up 15% of vehicles in less than 10 years. (4) The “Internet of Things” will expand rapidly to connect 30% of human artifacts soon after 2020.

Tune to page 50 of this issue for the rest of their top ten.