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A data analytics model for improving process control in flexible manufacturing cells


ThuBa Nguyen

Chris Hinton

Cripps Professor of Production Engineering & Head of Research Division


With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology based on data analytics that simplifies the monitoring process for the operator, allowing the practitioner to concentrate on the relevant issues, anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine’s settings are needed (tool replacement, repositioning the axis, etc.). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.


Martínez-Arellano, G., Nguyen, T., Hinton, C., & Ratchev, S. (2022). A data analytics model for improving process control in flexible manufacturing cells. Decision Analytics Journal, 3, Article 100075.

Journal Article Type Article
Acceptance Date May 25, 2022
Online Publication Date Jun 2, 2022
Publication Date 2022-06
Deposit Date Aug 25, 2022
Publicly Available Date Aug 25, 2022
Journal Decision Analytics Journal
Print ISSN 2772-6622
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 3
Article Number 100075
Public URL
Publisher URL
Additional Information This article is maintained by: Elsevier; Article Title: A data analytics model for improving process control in flexible manufacturing cells; Journal Title: Decision Analytics Journal; CrossRef DOI link to publisher maintained version:; Content Type: article; Copyright: © 2022 The Author(s). Published by Elsevier Inc.


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