Cloud-based manufacturing (CBM), also referred to as cloud manufacturing, has the potential to allow manufacturing enterprises to be rapidly scaled up and down by crowdsourcing manufacturing tasks or sub-tasks. To improve the efficiency of the crowdsourcing process, the material flow of CBM systems needs to be managed so that several manufacturing processes can be executed simultaneously. Further, the scalability of manufacturing capacity in CBM needs to be designed, analyzed, and planned in response to rapidly changing market demands. The objective of this paper is to introduce a stochastic petri nets (SPNs)-based approach for modeling and analyzing the concurrency and synchronization of the material flow in CBM systems. The proposed approach is validated through a case study of a car suspension module. Our results have shown that the SPN-based approach helps analyze the structural and behavioral properties of a CBM system and verify manufacturing performance.

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