Towards structured performance analysis of industry 4.0 workflow automation resources

Abstract

Automation and the use of robotic components within business processes is in vogue across retail and manufacturing industries. However, a structured way of analyzing performance improvements provided by automation in complex workflows is still at a nascent stage. In this paper, we consider the common Industry 4.0 automation workflow resource patterns and model them within a hybrid queuing network. The queuing stations are replaced by scale up, scale out and hybrid scale automation patterns, to examine improvements in end-to-end process performance. We exhaustively simulate the throughput, response time, utilization and operating costs at higher concurrencies using Mean Value Analysis (MVA) algorithms. The queues are analyzed for cases with multiple classes, batch/transactional processing and load dependent service demands. These solutions are demonstrated over an exemplar use case of automation in Industry 4.0 warehouse automation workflows. A structured process of automation workflow performance analysis will prove valuable across industrial deployments.