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Sensors
Volume 23
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10.3390/s23104928
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Article

Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

by
Ayse Aslan
1,*,
Hanane El-Raoui
2,
Jack Hanson
3,
Gokula Vasantha
1,*,
John Quigley
2,
Jonathan Corney
3 and
Andrew Sherlock
4
1
The School of Computing, Engineering and The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
2
Strathclyde Business School, University of Strathclyde, Glasgow G1 1XQ, UK
3
School of Engineering, The University of Edinburgh, Edinburgh, EH8 9YL, UK
4
National Manufacturing Institute Scotland, Glasgow PA3 2EF, UK
*
Authors to whom correspondence should be addressed.
Sensors 2023, 23(10), 4928; https://doi.org/10.3390/s23104928
Submission received: 14 April 2023 / Revised: 13 May 2023 / Accepted: 19 May 2023 / Published: 20 May 2023
(This article belongs to the Special Issue Human-Centred Smart Manufacturing - Industry 5.0)
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Abstract

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.
Keywords: industrial productivity; process mining; discrete event simulation; indoor positioning systems; completion time; flexible capacity allocation industrial productivity; process mining; discrete event simulation; indoor positioning systems; completion time; flexible capacity allocation

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MDPI and ACS Style

Aslan, A.; El-Raoui, H.; Hanson, J.; Vasantha, G.; Quigley, J.; Corney, J.; Sherlock, A. Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing. Sensors 2023, 23, 4928. https://doi.org/10.3390/s23104928

AMA Style

Aslan A, El-Raoui H, Hanson J, Vasantha G, Quigley J, Corney J, Sherlock A. Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing. Sensors. 2023; 23(10):4928. https://doi.org/10.3390/s23104928

Chicago/Turabian Style

Aslan, Ayse, Hanane El-Raoui, Jack Hanson, Gokula Vasantha, John Quigley, Jonathan Corney, and Andrew Sherlock. 2023. "Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing" Sensors 23, no. 10: 4928. https://doi.org/10.3390/s23104928

APA Style

Aslan, A., El-Raoui, H., Hanson, J., Vasantha, G., Quigley, J., Corney, J., & Sherlock, A. (2023). Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing. Sensors, 23(10), 4928. https://doi.org/10.3390/s23104928

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