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Data-driven process adjustment policies for quality improvement
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0001-6184-8951
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0002-0577-123X
2024 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 237, article id 121524Article in journal (Refereed) Published
Abstract [en]

Common objectives in machine learning research are to predict the output quality of manufacturing processes, to perform root cause analysis in case of reduced quality, and to propose intervention strategies. The cost of reduced quality must be weighed against the cost of the interventions, which depend on required downtime, personnel costs, and material costs. Furthermore, there is a risk of false negatives, i.e., failure to identify the true root causes, or false positives, i.e., adjustments that further reduce the quality. A policy for process adjustments describes when and where to perform interventions, and we say that a policy is worthwhile if it reduces the expected operational cost. In this paper, we describe a data-driven alarm and root cause analysis framework, that given a predictive and explanatory model trained on high-dimensional process and quality data, can be used to search for a worthwhile adjustment policy. The framework was evaluated on large-scale simulated process and quality data. We find that worthwhile adjustment policies can be derived also for problems with a large number of explanatory variables. Interestingly, the performance of the adjustment policies is almost exclusively driven by the quality of the model fits. Based on these results, we discuss key areas of future research, and how worthwhile adjustment policies can be implemented in real world applications.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 237, article id 121524
Keywords [en]
Process adjustment policy, Quality improvement, Cost reduction, Prediction, Local explanations, Simulation
National Category
Probability Theory and Statistics Reliability and Maintenance
Identifiers
URN: urn:nbn:se:umu:diva-208103DOI: 10.1016/j.eswa.2023.121524Scopus ID: 2-s2.0-85171612846OAI: oai:DiVA.org:umu-208103DiVA, id: diva2:1755589
Funder
Vinnova, 2015-03706Umeå University
Note

Originally included in thesis in manuscript form.

Volume 237, Part B.

Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2023-10-02Bibliographically approved
In thesis
1. Data-driven quality management using explainable machine learning and adaptive control limits
Open this publication in new window or tab >>Data-driven quality management using explainable machine learning and adaptive control limits
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Data-driven kvalitetskontroll genom förklarlig maskininlärning och adaptiva styrgränser
Abstract [en]

In industrial applications, the objective of statistical quality management is to achieve quality guarantees through the efficient and effective application of statistical methods. Historically, quality management has been characterized by a systematic monitoring of critical quality characteristics, accompanied by manual and experience-based root cause analysis in case of an observed decline in quality. Machine learning researchers have suggested that recent improvements in digitization, including sensor technology, computational power, and algorithmic developments, should enable more systematic approaches to root cause analysis.

In this thesis, we explore the potential of data-driven approaches to quality management. This exploration is performed with consideration to an envisioned end product which consists of an automated data collection and curation system, a predictive and explanatory model trained on historical process and quality data, and an automated alarm system that predicts a decline in quality and suggests worthwhile interventions. The research questions investigated in this thesis relate to which statistical methods are relevant for the implementation of the product, how their reliability can be assessed, and whether there are knowledge gaps that prevent this implementation.

This thesis consists of four papers: In Paper I, we simulated various types of process-like data in order to investigate how several dataset properties affect the choice of methods for quality prediction. These properties include the number of predictors, their distribution and correlation structure, and their relationships with the response. In Paper II, we reused the simulation method from Paper I to simulate multiple types of datasets, and used them to compare local explanation methods by evaluating them against a ground truth.

In Paper III, we outlined a framework for an automated process adjustment system based on a predictive and explanatory model trained on historical data. Next, given a relative cost between reduced quality and process adjustments, we described a method for searching for a worthwhile adjustment policy. Several simulation experiments were performed to demonstrate how to evaluate such a policy.

In Paper IV, we described three ways to evaluate local explanation methods on real-world data, where no ground truth is available for comparison. Additionally, we described four methods for decorrelation and dimension reduction, and describe the respective tradeoffs. These methods were evaluated on real-world process and quality data from the paint shop of the Volvo Trucks cab factory in Umeå, Sweden.

During the work on this thesis, two significant knowledge gaps were identified: The first gap is a lack of best practices for data collection and quality control, preprocessing, and model selection. The other gap is that although there are many promising leads for how to explain the predictions of machine learning models, there is still an absence of generally accepted definitions for what constitutes an explanation, and a lack of methods for evaluating the reliability of such explanations.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2023. p. 24
Series
Research report in mathematical statistics, ISSN 1653-0829 ; 74/23
Keywords
quality management, machine learning, local explanation methods, process adjustment policies, simulation
National Category
Other Engineering and Technologies not elsewhere specified Probability Theory and Statistics Computer Sciences
Research subject
Mathematical Statistics; data science
Identifiers
urn:nbn:se:umu:diva-208105 (URN)978-91-8070-095-5 (ISBN)978-91-8070-096-2 (ISBN)
Public defence
2023-06-02, UB.A.210, Lindellhallen 1, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2023-05-12 Created: 2023-05-08 Last updated: 2023-05-09Bibliographically approved

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Fries, NiklasRydén, Patrik

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