Beyond recency and magnitude: Learning-aware uncertainty estimation for safety stock under evolving forecast-error distributions
Abstract
We develop a non-parametric approach to refine forecast-error histories for safety stock estimation without relying on error magnitude or recency alone. Using forecast-error data from a fast-moving consumer goods environment, we show that one year of errors is insufficient to characterize service-level risk, while pooling several years is problematic because error distributions change over time. Non-parametric annual comparisons indicate a progressive reduction in bias and dispersion, consistent with learning in the forecasting process. We propose LOWDII (Leave-One-Out Wasserstein Distributional Influence Index), a diagnostic that evaluates the distributional influence of each historical forecast error on the empirical uncertainty distribution used for safety stock calibration. LOWDII identifies observations whose influence is disproportionate to their representativeness, separating transient distortions from persistent tail behaviour without imposing parametric assumptions. We evaluate the method in a discrete-event simulation using subsequent-year demand realizations as validation. The results show that LOWDII achieves the target service level while reducing average stock by 5.1% to 22.6% relative to benchmark methods. From a managerial perspective, LOWDII helps firms translate improvements in the forecasting process into safety stock decisions, avoiding the projection of obsolete historical errors into the future and capturing the economic benefit of lower uncertainty requirements earlier.