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Keywords

cloud manufacturing, resource sharing, tripartite evolutionary game

Abstract

In the context of rapid advancements in Internet of Things (IoT) and cloud computing technologies, cloud manufacturing has emerged as a new manufacturing model that offers a platform for resource optimization and collaborative manufacturing for small and medium-sized enterprises (SMEs). Currently, SMEs face widespread issues such as low manufacturing resource utilization efficiency and significant resource waste. Cloud manufacturing addresses these problems by integrating and sharing manufacturing resources, thereby effectively enhancing production efficiency and resource allocation capabilities, reducing production costs, and improving market competitiveness. However, despite its notable advantages, some SMEs still exhibit reluctance towards resource sharing. The main reasons for this reluctance include low trust between enterprises, high sharing risk costs, and inadequate reward and punishment mechanisms on platforms. These issues not only weaken the willingness of enterprises to share resources but also adversely affect the management strategies and operational efficiency of cloud platforms.One of the major obstacles to resource sharing among enterprises is low trust. Companies are concerned about risks such as technology leakage and data security during the sharing process, which directly impacts their core competitiveness. Furthermore, high-risk costs are another key factor deterring companies from participating in sharing, especially when the returns on innovation are uncertain, leading enterprises to adopt a more conservative approach. Meanwhile, the current reward and punishment mechanisms on cloud platforms are often inadequate, lacking scientific and reasonable methods for rewards and penalties. This inadequacy makes it difficult for companies to achieve expected benefits from sharing and may lead to additional costs, further reducing their enthusiasm for resource sharing.To address these issues, this paper constructs a tri-party evolutionary game model that includes cloud platform service management and resource sharing among enterprises with different resource types. The model simulates the strategic choices of cloud platforms, manufacturers, and suppliers during the resource-sharing process, analyzes the behaviors and interactions of these parties, and explores the implementation process and influencing factors of cloud manufacturing resource sharing. The research indicates that the final willingness of manufacturers and suppliers significantly affects their resource-sharing strategy choices. Specifically, the cost of information technology investment and the returns on innovation are key factors influencing enterprise decisions: high information technology investment costs can suppress the willingness to share, while higher innovation returns can effectively encourage participation in resource sharing. Additionally, for cloud platforms, management costs and penalty mechanisms significantly impact their management enthusiasm. A reasonable reward and punishment mechanism can motivate cloud platforms to adopt proactive management measures, thereby promoting cooperation and sharing among enterprises.In summary, this research provides important theoretical support for resource sharing among SMEs in the cloud manufacturing environment and offers practical suggestions for optimizing cloud platform management strategies and incentivizing enterprise participation in resource sharing. Future efforts to encourage SMEs to actively engage in cloud manufacturing resource sharing should address multiple aspects, including enhancing enterprise trust, reducing information technology costs, and improving platform reward and punishment mechanisms. Establishing a more open, transparent, and efficient cloud manufacturing ecosystem will enable the effective allocation and utilization of manufacturing resources, helping SMEs overcome current barriers and fully leverage the advantages of cloud manufacturing, thereby providing ongoing impetus for their smart transformation.

DOI

10.16315/j.stm.2024.05.002

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