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 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.

Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status

“…The number of machines and the degree of flexibility for a particular configuration with different instances and flexibilities is shown in Table 1. An effective arrangement of machines in the configurations has an impact on increasing the performance [47]. In this study, the machine arrangement is planned for each configuration in a particular instance in such a way that maximum production and highest productivity must be achieved.…”