Lead time forecasting for supplier management using machine learning

dc.contributor.authorSitje, Fredrik
dc.contributor.authorWaldschock, Felix
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerWahde, Mattias
dc.contributor.supervisorWahde, Mattias
dc.date.accessioned2025-07-03T11:53:36Z
dc.date.issued2025
dc.date.submitted
dc.description.abstractForecasting 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.coursecodeMMSX60
dc.identifier.urihttp://hdl.handle.net/20.500.12380/309918
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectMachine learning
dc.subjectSupply chain
dc.subjectForecasting
dc.subjectFeature selection
dc.subjectFeature engineering
dc.titleLead time forecasting for supplier management using machine learning
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc

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