Lead time forecasting for supplier management using machine learning
dc.contributor.author | Sitje, Fredrik | |
dc.contributor.author | Waldschock, Felix | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper | sv |
dc.contributor.department | Chalmers University of Technology / Department of Mechanics and Maritime Sciences | en |
dc.contributor.examiner | Wahde, Mattias | |
dc.contributor.supervisor | Wahde, Mattias | |
dc.date.accessioned | 2025-07-03T11:53:36Z | |
dc.date.issued | 2025 | |
dc.date.submitted | ||
dc.description.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. | |
dc.identifier.coursecode | MMSX60 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12380/309918 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Machine learning | |
dc.subject | Supply chain | |
dc.subject | Forecasting | |
dc.subject | Feature selection | |
dc.subject | Feature engineering | |
dc.title | Lead time forecasting for supplier management using machine learning | |
dc.type.degree | Examensarbete för masterexamen | sv |
dc.type.degree | Master's Thesis | en |
dc.type.uppsok | H | |
local.programme | Complex adaptive systems (MPCAS), MSc |