Inventory management with leading indicator augmented hierarchical forecasts

Abstract

Inventory management relies on accurate demand forecasts. Typically, these are univariate forecasts extrapolating patterns from past demand. The disaggregate nature of demand at the Stock Keeping Unit (SKU) level makes the incorporation of external information challenging. Nonetheless, such leading information can be critical to identifying disruptions and changes in the demand dynamics. To address the inventory planning needs of a global manufacturer we propose a methodology that identifies predictively useful leading indicators at an aggregate demand level, and translates that information to SKU-demand by leveraging on the hierarchical structure of the problem. Therefore, the proposed methodology provides probabilistic forecasts enriched by leading indicator information at SKU-level, as inputs for inventory management. The methodology automatically adjusts the choice of indicators for different required lead times, with some being more informative about the short-term demand dynamics and others for the long-term. We demonstrate the benefits both in the case of backorders and lost-sales, for a variety of lead times. We further benchmark the solution against solely using leading indicators or hierarchical forecasts, demonstrating that the benefits appear primarily by the proposed blending of the modelling approaches. The outcome is demonstratively better forecasts and inventory management for the case company. Additionally, management gains insights into the main drivers of their short and long-term demand, and the ability to adjust inventory replenishment accordingly. The ability to account for diverse macro and market information in operations is paramount for firms with a global reach that face different market conditions across countries. Additionally, the transparency of which leading indicators are influencing forecasts of different lead times is conducive to increased forecast trustworthiness.