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Using social network analysis for industrial plant layout analysis in the context of industry 4.0

Author

Listed:
  • M.L.R. Varela
  • Vijay Kumar Manupati
  • Suraj Panigrahi
  • Eric Costa
  • Goran D. Putnik

Abstract

Social network analysis (SNA) is a widely studied research topic, which has been increasingly applied for solving different kinds of problems, including industrial manufacturing ones. This paper focuses on the application of SNA to an industrial plant layout problem. The study aims at analysing the importance of using SNA techniques to study the important relations between entities in a manufacturing environment, such as jobs and resources in the context of industrial plant layout analysis. Here, performance measures such as maximum completion time of jobs (makespan), resource utilisation, and throughput time have been considered to evaluate the system performance. Later, with the simulation analysis, the relationships between entities and their impact on the system performance are evaluated. The experimental results revealed that the proposed SNA approach supports to find the key machines of the systems that ultimately lead to the effective performance of the whole system. Finally, the identification of relations among these entities supported the establishment of an appropriate plant layout for producing the jobs in the context of industry 4.0.

Suggested Citation

  • M.L.R. Varela & Vijay Kumar Manupati & Suraj Panigrahi & Eric Costa & Goran D. Putnik, 2020. "Using social network analysis for industrial plant layout analysis in the context of industry 4.0," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 34(1), pages 1-19.
    • Handle: RePEc:ids:ijisen:v:34:y:2020:i:1:p:1-19
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    Cited by:

    1. Thirupathi Samala & Vijaya Kumar Manupati & Bethalam Brahma Sai Nikhilesh & Maria Leonilde Rocha Varela & Goran Putnik, 2021. "Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status," Sustainability, MDPI, vol. 13(9), pages 1-20, May.

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