Lead time forecasting for supplier management using machine learning

Abstract

Forecasting lead times in the supply chain of manufacturing companies is a businesscritical, yet time-consuming task. Typically, suppliers are contacted regularly to provide lead time estimations for parts, a process often prone to bias, especially if suppliers have an incentive to sell express deliveries. To address this issue, a machine learning pipeline was developed and tested using a case-study involving Maxon, a high-precision electric drive manufacturer. The pipeline considers the steps of data collection, filtering, feature engineering, selection, and model training. Multiple machine learning models were evaluated, and the best-performing model achieved a prediction accuracy of 0.804 on a test data set, where the supplier’s estimation reached an accuracy of 0.343. This is a promising result. Furthermore, a time series analysis indicates that the model’s performance improves with larger data sets, suggesting the pipeline’s potential for real-world manufacturing environments.