Business Metric-Aware Forecasting for Inventory Management
This addresses the issue of sub-optimal business performance in inventory management due to forecast misalignment, though it is incremental as it adapts existing methods to incorporate business metrics.
The paper tackled the problem of forecasts being misaligned with business goals by proposing an end-to-end differentiable method to optimize business metrics in inventory management, resulting in performance improvements of up to 45.7% for a scaling model and 54.0% for an LSTM model.
Time-series forecasts play a critical role in business planning. However, forecasters typically optimize objectives that are agnostic to downstream business goals and thus can produce forecasts misaligned with business preferences. In this work, we demonstrate that optimization of conventional forecasting metrics can often lead to sub-optimal downstream business performance. Focusing on the inventory management setting, we derive an efficient procedure for computing and optimizing proxies of common downstream business metrics in an end-to-end differentiable manner. We explore a wide range of plausible cost trade-off scenarios, and empirically demonstrate that end-to-end optimization often outperforms optimization of standard business-agnostic forecasting metrics (by up to 45.7% for a simple scaling model, and up to 54.0% for an LSTM encoder-decoder model). Finally, we discuss how our findings could benefit other business contexts.