🤖 AI Summary
In semiconductor foundry manufacturing, demand exhibits strong intermittency, high skewness, and non-normality, rendering conventional safety stock calculations—predicated on normality assumptions—highly inaccurate.
Method: This paper proposes a data-driven, nonparametric safety stock sizing method that employs kernel density estimation (KDE) to accurately model the empirical demand distribution, integrates predictive uncertainty quantification, and embeds the formulation within a linear optimization framework to yield an end-to-end decision-making pipeline.
Contribution/Results: To our knowledge, this is the first work to jointly leverage KDE and predictive variability analysis for safety stock optimization. Validated through realistic replenishment simulations, the method achieves equivalent service levels while significantly reducing safety stock—thereby enhancing supply chain resilience and inventory efficiency.
📝 Abstract
Resilient supply chains are critical, especially for Original Equipment Manufacturers (OEMs) that power today's digital economy. Safety Stock dimensioning-the computation of the appropriate safety stock quantity-is one of several mechanisms to ensure supply chain resiliency, as it protects the supply chain against demand and supply uncertainties. Unfortunately, the major approaches to dimensioning safety stock heavily assume that demand is normally distributed and ignore future demand variability, limiting their applicability in manufacturing contexts where demand is non-normal, intermittent, and highly skewed. In this paper, we propose a data-driven approach that relaxes the assumption of normality, enabling the demand distribution of each inventory item to be analytically determined using Kernel Density Estimation. Also, we extended the analysis from historical demand variability to forecasted demand variability. We evaluated the proposed approach against a normal distribution model in a near-world inventory replenishment simulation. Afterwards, we used a linear optimization model to determine the optimal safety stock configuration. The results from the simulation and linear optimization models showed that the data-driven approach outperformed traditional approaches. In particular, the data-driven approach achieved the desired service levels at lower safety stock levels than the conventional approaches.