• search hit 9 of 1543
Back to Result List

Orthogonal Procrustes and Machine Learning: Predicting Bill of Materials errors on time

  • In an industrial product development process, the Bill of Materials (BOM) is a hierarchical, multi-level representation of all components, parts and quantities of a product. With increasing complexity of industrial products, also BOMs become more complex and thus prone to errors, for example when the individual parts of a product are changed during the product development process. Frequently, these Bill of Materials errors have to be identified manually or by using simple, rule-based schemes. In this paper, we provide a technical background of BOMs, showing the intricacy of temporal BOMs errors in an industrial product development process. The work of other authors, which focused on association mining and tree reconciliation to detect Bill of Materials errors, is analysed. We found that there is currently no system being able to prescribe where in a Bill of Materials and when in the product development process, errors are probable to occur. Also, Machine Learning (ML) methods have not been applied yet. Based on these findings, we formalize the notions Bill of Materials and Bill of Materials errors. Furthermore, we present a deterministic distance measure for BOMS. We provide an answer to the main question of how to represent a Bill of Materials for Machine Learning tasks by solving the orthogonal Procrustes problem for dynamic, hierarchical datasets. Then, we describe an isolation forest based approach to temporal anomaly detection, which points at potential errors in a Bill of Materials at a specific timestamp. Furthermore, we apply Machine Learning and present a multi-output Multi Layer Perceptron for the prediction of temporal Bill of Materials errors. The model predicts where and at which point of time Bill of Materials errors are probable to occur, which renders it a prescriptive system. Eventually, we optimize the performance of our model using contextualization via -means clustering. Finally, we apply our prescriptive pipeline to a real world dataset and show its superiority to existing methods using a qualitative comparison.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Simon Schramm, Matthias Pieper, Stefanie VoglORCiDGND
DOI:https://doi.org/10.1016/j.cie.2023.109606
Parent Title (English):Computers & Industrial Engineering
Publisher:Elsevier
Document Type:Article
Language:English
Year of first Publication:2023
Release Date:2023/12/14
Volume:185
Article Number:109606
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Produktion und Systeme
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG