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Demand Forecasting for Retail:
A Genetic Approach
Bruce Ratner, Ph.D.

Accurate demand forecasting is essential for retailers to minimize the risk of stores running out of a product, or not having enough of a popular brand, color or style. Preseason and in-season forecast errors account for 20 to 25 percent of losses in sales. Traditional demand forecasting methods for all stock-keeping units (SKUs) across all stores and all geographies have an inherent weakness of no ability to data mine the volumes of time-series data at the SKU-level. The purpose of this article is to present a machine learning approach – the GenIQ Model© – for demand forecasting that has demonstrated superior results compared to the traditional techniques.

For more information about this article, call Bruce Ratner at 516.791.3544,
1 800 DM STAT-1, or e-mail at