Using worker position data for human-driven decision support in labour-intensive manufacturing

Ayse Aslan, Hanane El-Raoui, Jack Hanson, Gokula Vasantha, John Quigley, Jonathan Corney, Andrew Sherlock

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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.
Original languageEnglish
Article number4928
Number of pages20
JournalSensors
Volume23
Issue number10
DOIs
Publication statusPublished - 20 May 2023

Keywords

  • electrical and electronic engineering
  • biochemistry
  • instrumentation
  • atomic and molecular physics, and optics
  • analytical chemistry

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