Foresight kicks off its 10th year with the publication of a new survey of business forecasters: Improving Forecast Quality in Practice. This ongoing survey, designed at the Lancaster Centre for Forecasting in the UK, seeks to gain insights on where the emphasis should be put to further upgrade the quality of our forecasting practices. Initial survey results, presented by Robert Fildes, Director of the Lancaster Centre, and Fotios Petropoulos, former member of the Centre, examine these key aspects of forecasting practice: organizational constraints, the flow of information, forecasting software, organizational resources, forecasting techniques employed, and the monitoring and evaluation of forecast accuracy.
The survey is an important update to that conducted more than a decade ago by Mark Moon, Tom Mentzer, and Carlo Smith of the University of Tennessee. In his Commentary on the Lancaster survey, Mark Moon applauds the broad focus of the survey but raises the issue of whether the “practicing forecasters” surveyed are “developers” or “customers” of the forecasts.
We often find a significant difference in perception between those who are responsible for creating a forecast and those that use the forecast to create business plans.
In our section on Collaborative Forecasting and Planning, Foresight S&OP Editor John Mello writes that S&OP can not only improve collaboration within an organization, but also “change the company’s operational culture from one that is internally focused to one that better understands the potential benefits of working with other companies in the supply chain.” His article, Internal and External Collaboration: The Keys to Demand-Supply Integration, identifies and compares several promising avenues of external collaboration, including vendor-managed inventory (VMI); collaborative planning, forecasting, and replenishment (CPFR); retail-event collaboration; and various stock-replenishment methods currently in use by major manufacturers and retailers. The critical factor, John finds, is trust:
These processes all require the sharing of information between companies, joint agreement on the responsibilities of the individual companies, and a good deal of trust between the parties, since the responsibility for integrating supply and demand is often delegated to the supplier.
In a Commentary on the Mello article, Ram Ganeshan and Tonya Boone point out that the challenges of external collaboration arrangements are much greater when we consider their Extension beyond Fast-Moving Consumer Goods, especially those goods with short life cycles. For these products, they argue, a different mind-set is required to achieve demand-supply integration.
Financial Forecasting Editor Roy Batchelor distills the lessons forecasters should learn from the failures to predict and control our recent global financial meltdown. A 2014 International Monetary Fund (IMF) report, Financial Crises: Causes, Consequences, and Policy Responses, examined the world economies’ 2007-09 financial crises to establish their causes and impacts, as well as the initiatives governments and central banks undertook to deal with them. The overall impression from this report, Roy writes in his review, entitled Financial Crises and Forecasting Failures, is that the authorities could have been speedier and more imaginative in their interventions in the financial sector. Importantly, however, our forecasting models could have given a clearer picture of how economies might emerge from these crises. Roy probes into why the models didn’t see the crisis coming, and what upgrades to the models’ financial sectors might improve predictive performance in the future.
Jeffrey Mishlove’s Commentary on Roy’s review article argues that the real problem did not emanate from predictive failures, but rather from the inclination toward austerity that pervaded economic thinking, especially in Western Europe. Jeff says that, while he can’t argue with Roy’s conclusions that refinements in the scientific method and the gathering of empirical data are appropriate responses to financial crises, forecasts will always be vulnerable to confounding influences from unanticipated variables – no matter how much we refine and improve our methodologies.
Seasonality – repeating intra-year patterns that in turn repeat year after year – is a dominant and pervasive contributor to variations in our economy. But, as Roy Pearson writes in Giving Due Respect to Seasonality in Monthly Forecasting, the seasonal adjustments we make to economic data are poorly understood and lead to confusion in interpreting sales changes. Improved accounting for seasonality for monthly forecasts over 12-24 months can lead to better understanding of the forces behind sales forecasts, and very likely to some reduction in forecast errors.
This article is Roy’s final contribution for Foresight, the capstone of nearly one dozen invaluable examinations – under the rubric of Forecasting Intelligence – of forecasting information sources and forecast credibility.