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.

翻译：弹性供应链至关重要，尤其对于支撑当今数字经济的原始设备制造商（OEM）而言。安全库存量化——即计算适当的安全库存数量——是确保供应链弹性的关键机制之一，它能抵御需求与供应的不确定性。然而，当前主流的安全库存量化方法严重依赖需求服从正态分布的假设，且忽视未来需求波动性，这限制了其在需求呈非正态、间歇性及高度偏态分布的生产环境中的适用性。本文提出一种数据驱动方法，该方法放宽了正态性假设，通过核密度估计对每个库存项目的需求分布进行解析确定。同时，我们将分析范围从历史需求波动扩展至预测需求波动。我们在近现实库存补货仿真中，将所提方法与正态分布模型进行了对比评估，随后采用线性优化模型确定最优安全库存配置。仿真与线性优化模型的结果表明，数据驱动方法优于传统方法，尤其在实现相同服务水平时，所需安全库存水平低于传统方法。