存在异方差、自相关及重尾预测误差的安全库存计算：一种自适应参数方法
Safety stock calculation with heteroscedastic, autocorrelated and heavy-tailed forecast errors, an adaptive parametric method.e
AI总结：
针对预测误差存在自相关、异方差等特征的安全库存计算问题，提出自适应参数方法，经仿真验证可降低库存规模且不损害服务水平，可集成至现有ERP环境。
摘要：
本研究开发并验证了一种自适应参数方法，用于在预测误差存在自相关、异方差、非对称性及重尾特征时确定安全库存规模。通过某快速消费品公司四年的周度预测数据，分析发现存在显著的正自相关性、偏离正态性、系统性预测乐观偏差，以及预测量与误差方差之间的异质性关系。所提方法将多项修正整合至模块化、顺序式的安全库存架构中：构建运营行动周期内的累积预测误差，选择特定SKU的参数分布，采用与目标β满足率一致的预期缺货条件确定基准保护点；当误差方差依赖于预测量时，采用预测缩放模块调整离散程度；当补货批量增大导致周期内暴露风险提升时，采用周期末暴露保障机制限制周期内缺货风险。通过离散事件仿真，将所提方法与该公司现有方法对比评估性能：所提方法使平均库存降低9.9%，最大库存降低19.5%，且β满足率或平均缺货时长无统计学意义上的恶化；无缺货的周数占比略有提升，未出现更持久或运营层面更严重的缺货情况；异方差模块可提供选择性收益，且方差依赖程度越高，服务水平改善越显著。该框架具有可解释性，可适配特定SKU的统计特征，将分布特征与安全库存结果关联，且可集成至现有ERP环境中。
英文摘要：
This study develops and validates an adaptive parametric method for sizing safety stock when forecast errors are autocorrelated, heteroscedastic, asymmetric, and heavy-tailed. Using four years of weekly forecasts from a fast-moving consumer goods company, the analysis identifies substantial positive autocorrelation, departures from normality, systematic forecast optimism, and heterogeneous relationships between forecast volume and error variance. The proposed method integrates several corrections into a modular, sequential safety-stock architecture. It constructs cumulative forecast errors over the operational action period, selects an SKU-specific parametric distribution, and determines a baseline protection point using an expected-shortage condition consistent with the target beta fill rate. A forecast-scaled module adjusts dispersion when error variance depends on forecast volume, while a Cycle-End Exposure safeguard limits within-cycle shortage risk when large replenishment lots increase exposure. Performance is evaluated through discrete-event simulation against the company's current method. The proposed method reduces average inventory by 9.9% and maximum inventory by 19.5%, with no statistically significant deterioration in beta fill rate or average stockout duration. The proportion of weeks without stockouts declines slightly, without evidence of more persistent or operationally severe shortages. The heteroscedasticity module provides selective benefits, with greater service improvements as variance dependence intensifies. The resulting framework is interpretable, adapts to SKU-specific statistical profiles, links distributional features to safety-stock outcomes, and can be integrated into existing ERP environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
