@article{SchrammPieperVogl, author = {Schramm, Simon and Pieper, Matthias and Vogl, Stefanie}, title = {Orthogonal Procrustes and Machine Learning: Predicting Bill of Materials errors on time}, series = {Computers \& Industrial Engineering}, volume = {185}, journal = {Computers \& Industrial Engineering}, publisher = {Elsevier}, doi = {10.1016/j.cie.2023.109606}, abstract = {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a real world dataset and show its superiority to existing methods using a qualitative comparison.}, language = {en} } @unpublished{SchrammPieperVogl, author = {Schramm, Simon and Pieper, Matthias and Vogl, Stefanie}, title = {Orthogonal Procrustes Based Anomaly Detection and Error Prediction for Vehicle Bills of Materials}, series = {SSRN}, journal = {SSRN}, publisher = {Elsevier}, doi = {10.2139/ssrn.4120321}, pages = {21 S.}, abstract = {Industrial Bill of Materials (BOM) suffer from an surging complexity and cause errors in production which have detrimental effects on a product's profitability. Currently, BOM anomalies have to be identified manually and errors have to be detected in the same way. This preprint describes a combination of data analysis and Machine Learning methods, such as hierarchical and agglomerative clustering, an isolation forest algorithm, association mining and a multi-output Artificial Neural Network, all based on a deterministic distance measure for an industrial BOMs. Solving the orthogonal Procrustes problem for complex, multi-level matrices, a distance measure for real world industrial BOMs was derived. A multi-output MLP was used in order to predict error probabilities with a time- reference. Our results show how to detect anomalies and predict errors in a complex, multi-level BOM based on historical, labelled data. While other authors focus on the mere comparison of BOMs, we aimed at a holistic approach, combining descriptive and predictive methods in order to forecast where in a BOM and at what time of BOM creation process errors occur. The resulting, prescriptive system was tested using real world data and has shown to effectively predict where and when BOM errors are probable to occur. Consequently, the prescriptive system is superior to prior, purely predictive systems, can help to decrease errors and thereby decreases product development time and cost in real world companies.}, language = {en} }