New product family demand planning: Addressing SKU-level spread bias

New product supply chain planning is challenging, primarily due to the lack of historical demand data. Rarely, however, do the academic literature or companies differentiate the demand forecasting process for new products from existing ones, despite their increased reliance on judgmental estimates. This research focuses on how judgmental errors lead to an under-estimation of the difference between the highest- and lowest-demand stock-keeping units (SKUs), and consequently negatively impact supply chain planning for new product family introductions. A generalized empirical model and accompanying discrete event simulation are developed and applied to data from a major consumer packaged goods (CPG) firm during the launch of a new cosmetics product family. This application allows us to identify a focal type of judgmental error (identified as the SKU-level spread bias) inherent to new product forecasting and to provide a new theoretical understanding of how this type of bias harms supply chain performance. Via an empirically driven theory-building approach that iterates between the simulation outcomes and existing literature, SKU-level spread bias is demonstrated to harm demand forecasts and, thereby, supply chain plans. Our unique theory-building approach advances theory by identifying planner SKU-level spread bias as a new source of bias that firms should seek to mitigate when introducing new product families.