Enhancing Demand Forecast Precision for Long Lead-Time Finished Goods Using Bayesian Deep Learning: Driving Competitive Advantage Through Data-Driven Inventory Decisions

The FWMAE (Floor-adjusted Weighted Mean Absolute Error) is a forecasting evaluation metric developed by Prinal Kapadia as part of his Doctoral research on demand forecasting for long lead-time finished goods using Bayesian Deep Learning. This metric introduces a novel weighting and stabilization method for evaluating forecast error in volatile, low-demand, or intermittent demand environments.经楼层调整的加权平均绝对误差（Floor-adjusted Weighted Mean Absolute Error, FWMAE）是普里纳尔·卡帕迪亚（Prinal Kapadia）在其针对长交货期成品需求预测所开展的贝叶斯深度学习（Bayesian Deep Learning）博士研究中提出的一项预测评估指标。该指标针对波动、低需求或间歇性需求场景，提出了一种新颖的加权与稳定化方法，用于评估预测误差。