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.