Forecasting new products (Part 5): Model, forecast, override


We ended last time having selected a cluster of surrogate products -- a subset of the original selection of like-items that had the same attributes as the new product. Judgment has been used throughout the process so far, in specification of the relevant attributes, filtering the original candidate pool of like-items, and selecting a cluster to best represent the new product.

In the Model step, we specify a statistical model that fits the surrogate cluster. Again, we are using judgment to select a type of model and set its parameters. Below, we see a model that has been fit to the surrogate data, and the Forecast step provides the weekly forecast along with upper and lower limits to represent the most likely range of outcomes.

Model fitted to surrogate cluster

In the final Override step, judgment is used to manually adjust the model's forecasts. Once any overrides are made, the new DVD forecasts can be exported to downstream planning systems.

Applying the Structured Analogy Approach

The structured analogy approach can be useful in many (but not all) new product forecasting situations. It augments human judgment by automating the historical data handling and extraction, by incorporating statistical analysis, and by providing visualization of the range of historical outcomes.

Software makes it possible to quickly extract candidate products based on the user-specified attribute criteria. It aligns, scales, and clusters the historical patterns automatically, making it easier to visualize the behavior of past new products. This visualization helps the forecaster realize the risks, uncertainties, and variability in new product behavior.

Expectations for the accuracy of new product forecasts should be modest, and acknowledgment of this uncertainty should be at the forefront. The structured analogy approach allows the organization to both statistically and visually assess the likely range of new product demand, so that it can manage accordingly. Rather than lock in elaborate sales and supply plans based on a point forecast that is likely to be wrong (and possibly very wrong), the organization can use the structured analogy process to assess alternative demand scenarios and mitigate risk.

Judgment is always going to be a big part of new product forecasting. A computer will never be able to tell us whether Lime Green or Day-Glo Orange is going to be the hot new fashion color, but judgment needs assistance to expose biases and keep it as objective as possible.

While the structured analogy approach can be used to generate new product forecasts, I find its greatest value in assessing the reasonableness of forecasts that are provided from elsewhere in the organization. The role of structured analogy software is to do the heavy computational work and provide guidance—making the NPF process as automated, efficient, and objective as possible.

Think of it as your BS detector for new product forecasting.

[The structured analogy approach to new product forecasting was developed by my colleagues Michael Leonard, Tom Dickey, Michele Trovero, and Sam Guseman. For more information see the downloadable white paper "New Product Forecasting Using Structured Analogies," or the article "Forecasting New Products by Structured Analogy " in the Winter 2009-10  issue of Journal of Business Forecasting.]




About Author

Mike Gilliland

Product Marketing Manager

Michael Gilliland is a longtime business forecasting practitioner and formerly a Product Marketing Manager for SAS Forecasting. He is on the Board of Directors of the International Institute of Forecasters, and is Associate Editor of their practitioner journal Foresight: The International Journal of Applied Forecasting. Mike is author of The Business Forecasting Deal (Wiley, 2010) and former editor of the free e-book Forecasting with SAS: Special Collection (SAS Press, 2020). He is principal editor of Business Forecasting: Practical Problems and Solutions (Wiley, 2015) and Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Wiley, 2021). In 2017 Mike received the Institute of Business Forecasting's Lifetime Achievement Award. In 2021 his paper "FVA: A Reality Check on Forecasting Practices" was inducted into the Foresight Hall of Fame. Mike initiated The Business Forecasting Deal blog in 2009 to help expose the seamy underbelly of forecasting practice, and to provide practical solutions to its most vexing problems.

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