Data-driven decision support for process quality improvements
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TL;DR: In this paper, a cross-process data analysis of the process and quality data is carried out using decision trees and the results are visualized in a comprehensible form for the worker.
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About: This article is published in Procedia CIRP. The article was published on 01 Jan 2021. and is currently open access. The article focuses on the topics: Decision support system & Data quality.
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Citations
Entscheidungsunterstützung im Produktionsmanagement
Kai F. Müller,Daniel Buschmann,Simon Cramer,Chrismarie Enslin,Markus Fischer,Tim Janke,Marco Kemmerling,Lukas Pelzer,Mahsa Pourbafrani,V. Samsonov,Peter Schlegel,Seth Schmitz,Marco Schopen,Robert Schmitt,Thomas Gries +14 more
TL;DR: In this paper , the Fokus der Gruppe Short-Term Production Management liegt dabei insbesondere auf der Erhöhung von Entscheidungsqualität and -geschwindigkeit im Produktionsumfeld durch the datenbasierte Unterstützung der Anwender:innen.
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Evaluation Methodology for Interpretation Methods of Predictive Quality Models
Tobias Schulze,Daniel Buschmann,Robert Schmitt +2 more
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Konstantin Muehlbauer,Stephan Schnabel,Sebastian Meißner +2 more
- 01 Jan 2024
Enhancing Logistics with Data-Driven Process Analysis and Knowledge-Based Methods
Konstantin Muehlbauer,Sebastian Meissner,Konstantin Muehlbauer,Sebastian Meissner +3 more
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References
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A review of multivariate control charts
TL;DR: A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s as mentioned in this paper, where several recent articles that give methods for interpreting an out-of control signal on a multivariate control chart are analyzed and discussed.
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Peihua Qiu
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Quality Prediction in Interlinked Manufacturing Processes based on Supervised & Unsupervised Machine Learning
TL;DR: In this paper, the authors presented a methodical framework based on data mining for predicting the physical quality of intermediate products in interlinked manufacturing processes in rolling mill case study, and showed how the combination of supervised and unsupervised data mining methods can be applied to identify most striking operational patterns, promising quality-related features and production parameters.
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