Analysis and correction of supply chain design through machine learning

摘要

A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, and to divide historical lead time data into clusters based on seasonality and linearity. The machine learning results are further processed to adjust future planned lead times and to identify sources in the supply chain that contribute to large deviations between historical planned lead times and actual lead times.