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

Ayse Aslan1, Hanane El-Raoui2, Jack Hanson3

  • 1The School of Computing, Engineering and The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK.

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Summary
This summary is machine-generated.

This study introduces a new method for optimizing worker capacity in manufacturing. By analyzing real work data, it shows how small adjustments can significantly cut production time without adding staff.

Keywords:
completion timediscrete event simulationflexible capacity allocationindoor positioning systemsindustrial productivityprocess mining

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Area of Science:

  • Industrial Engineering
  • Operations Research
  • Manufacturing Systems

Background:

  • Labour-intensive manufacturing systems rely heavily on human workers, making their actual working practices crucial for productivity improvements.
  • Implementing changes based on theoretical models can be ineffective; data-driven insights into real worker behaviour are essential.
  • Capacity allocation in these systems requires a methodology that reflects actual operational dynamics.

Purpose of the Study:

  • To present a novel methodology for human-driven decision support in capacity allocation for labour-intensive manufacturing.
  • To leverage worker position data and process mining for creating data-driven process models.
  • To utilize discrete event simulation for evaluating capacity allocation adjustments based on observed working practices.

Main Methods:

  • Collecting worker position data using localisation sensors.
  • Applying process mining algorithms to generate a data-driven process model of manufacturing tasks.
  • Building a discrete event simulation model based on the data-driven process model.
  • Investigating the impact of capacity allocation adjustments on system performance.

Main Results:

  • A 7% reduction in completion time was achieved with minor capacity adjustments, without additional workers.
  • A 16% reduction in completion time was realized by adding one worker and optimizing bottleneck tasks.
  • The methodology demonstrated the effectiveness of data-driven simulation for capacity allocation in manual assembly lines.

Conclusions:

  • Worker position data and process mining provide valuable insights into actual manufacturing operations.
  • Discrete event simulation, informed by real data, is effective for optimizing capacity allocation.
  • Targeted capacity adjustments, especially at bottleneck tasks, can significantly improve manufacturing efficiency and reduce completion times.