Taming Uncertainty: A Critical Role for Informed Judgment in the Forecasting Process

Statistical techniques are an integral part of an effective demand planning and forecasting process. They can provide a framework for gaining knowledge around which analytical skills and judgment can be applied in order to a sound, agile forecasting? process. Users of econometric models have come to realize that their models can only be relied upon to provide a first approximation—a set of consistent forecasts which then must be ‘massaged’ with intuition and good judgment to take into account those influences on economic activity for which history is a poor guide.”

Consider buying an automobile. A car buyer has purchased a car, subjective judgment comes into play if the car buyer realizes that the purchase was not a good decision. For example, during verification and confidence checks, suppose that the car buyer discovers a flaw so great that the dealer agrees either to repair the car or to exchange it for a comparable model—the buyer needs to exercise judgment not called for in the original forecast in order to reconcile expectations with reality.

In an actual demand forecasting situations in the supply chain, it may become apparent, through rolling simulations with hold-out or learning samples, that the actuals have exceeded the estimates for several successive periods, or that the forecasts for a given period under-predict the held-out actual values. Experience suggests that a model’s projections be modified (adjusted upward or downward) by a given amount or percentage to account for the current deviation (bias) and the forecaster’s expectation of whether that forecast profile pattern will prevail.

Informed judgment should be based on all available information, including changes in company policy, changes in economic conditions and government regulations, and contacts with customers. Such judgment is a real measure of the skill and experience of the forecaster. For this reason, data and forecasting processes are only as good as the person interpreting them.

Informed judgment plays a critical role in the determination of the final forecast numbers and, later on, in the determination of when a forecast should be revised

Informed judgment is, by far, the most crucial element when we are trying to predict the future. Informed judgment is what ties the forecasting process and the extrapolative techniques into a cohesive effort that is capable of producing realistic predictions of future events or conditions. Informed judgment is an essential ingredient of the selection of the forecasting approach; the selection of data sources; the selection of the data collection and data cleaning methodologies; the selection of preliminary data analysis and extrapolative techniques; the use of exception-handling and root-cause analysis techniques during the forecasting process; the identification of forward-looking market and company factors that are likely to affect the future of the item to be forecast; the determination of how those factors will affect the item in terms of the direction, magnitude (amount or rate), timing, and duration of the expected impact; and the selection of the forecast presentation methodology.

Informed judgment plays a significant role in taming the uncertainty associated with demand forecasting

Automatic processes, models, and statistical algorithms are now increasingly used in computing future demand from a set of key factors. However, no such approach should reduce substantially the reliance upon sound judgment. Judgment must be based on a comprehensive analysis of market activities and a thorough evaluation of basic assumptions and influencing factors.

The limitations of a purely statistical modeling approach should be kept clearly in mind. Statistics, like all tools, may be valuable for one job but of little use for another.?An exploratory data analysis and forecast profile analysis is basic to a demand forecasting and planning process along with a number of different statistical procedures that may be employed to make this analysis more meaningful. However, the human element is required to understand the differences between what was expected in the past and what actually occurred and to predict the likely outcome of future events.

Hans Levenbach, PhD is Owner/CEO of Delphus, Inc and Executive Director,?CPDF Professional Development Training and Certification Programs.

Hans is the author of a business forecasting book (Change&Chance Embraced) recently updated with the new LZI method for intermittent demand forecasting in the Supply Chain.

With endorsement from the International Institute of Forecasters (IIF), he created CPDF, the first IIF certification curriculum for the professional development of demand forecasters. and has conducted numerous, hands-on?Professional Development Workshops?for Demand Planners and Operations Managers in multi-national supply chain companies worldwide.

The 2021 CPDF Workshop manual is available for self-study, online workshops, or in-house professional development courses.

Hans is an elected Fellow, Past President and former Treasurer, and member of the Board of Directors of the?International Institute of Forecasters.

He is Owner/Manager of these LinkedIn groups: (1)?Demand Forecaster Training and Certification, Blended Learning, Predictive Visualization, and (2)?New Product Forecasting and Innovation Planning, Cognitive Modeling, Predictive Visualization.

I invite you to join these groups and share your thoughts and practical experiences with demand data quality and demand forecasting performance in the supply chain. Feel free to send me the details of your findings, including the underlying data without identifying proprietary descriptions. If possible, I will attempt an independent analysis and see if we can collaborate on something that will be beneficial to everyone.

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