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

Leonilde Varela, Vijay Manupati, Suraj Panigrahi, Eric Costa, Goran Putnik

Research output: Contribution to journalArticle

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 on 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 is 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.
Original languageEnglish
JournalInternational Journal of Industrial and Systems Engineering
Publication statusAccepted/In press - 6 Apr 2018

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Plant layout
Electric network analysis
Industrial plants
Industry

Cite this

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title = "Using social network analysis for industrial plant layout analysis in the context of Industry 4.0",
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 applicationof SNA on an industrial plant layout problem. The study aims at analysing theimportance of using SNA techniques to study the important relations betweenentities in a manufacturing environment, such as jobs and resources in thecontext of industrial plant layout analysis. Here, performance measures such asmaximum completion time of jobs (makespan), resource utilisation, andthroughput time have been considered to evaluate the system performance.Later, with the simulation analysis, the relationships between entities and theirimpact on the system performance is evaluated. The experimental resultsrevealed that the proposed SNA approach supports to find the key machines ofthe systems that ultimately lead to the effective performance of the wholesystem. Finally, the identification of relations among these entities supportedthe establishment of an appropriate plant layout for producing the jobs in thecontext of industry 4.0.",
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Using social network analysis for industrial plant layout analysis in the context of Industry 4.0. / Varela, Leonilde; Manupati, Vijay; Panigrahi, Suraj; Costa, Eric; Putnik, Goran.

In: International Journal of Industrial and Systems Engineering, 06.04.2018.

Research output: Contribution to journalArticle

TY - JOUR

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AU - Varela, Leonilde

AU - Manupati, Vijay

AU - Panigrahi, Suraj

AU - Costa, Eric

AU - Putnik, Goran

PY - 2018/4/6

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AB - 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 applicationof SNA on an industrial plant layout problem. The study aims at analysing theimportance of using SNA techniques to study the important relations betweenentities in a manufacturing environment, such as jobs and resources in thecontext of industrial plant layout analysis. Here, performance measures such asmaximum completion time of jobs (makespan), resource utilisation, andthroughput time have been considered to evaluate the system performance.Later, with the simulation analysis, the relationships between entities and theirimpact on the system performance is evaluated. The experimental resultsrevealed that the proposed SNA approach supports to find the key machines ofthe systems that ultimately lead to the effective performance of the wholesystem. Finally, the identification of relations among these entities supportedthe establishment of an appropriate plant layout for producing the jobs in thecontext of industry 4.0.

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