Design and strategy selection for quality incentive mechanisms in the public cloud manufacturing model

Abstract

Cloud Manufacturing (CMfg) is growing rapidly but facing challenges of uncontrollable quality caused by “random matching” transactions. This study concentrates on how a CMfg platform operator can offer quality incentives to capability providers, thereby facilitating the delivery of high-quality services. Initially, game theory is employed to construct the decision-making objective functions for both platform operator and capability providers. Building on this, three incentive mechanisms are proposed: direct subsidy (DS), cost sharing (CS), and quality reward and punishment (RP); furthermore, the conditions necessary for effectively implementing these mechanisms are analyzed. Concurrently, the incentive effects of the three mechanisms are examined and compared to offer guidance for the platform operator in selecting appropriate quality incentive strategies. Ultimately, employing numerical simulation analysis, the incentive effects of the three mechanisms and a sensitivity analysis of crucial parameters affecting the selection of incentive strategies are conducted, thereby validating the theoretical model’s analytical conclusions. The study reveals that these mechanisms can effectively motivate capability providers to enhance quality, yet under identical incentive intensities, the RP strategy outperforms the DS strategy. Furthermore, the platform operator tends to favor the CS strategy under conditions such as higher price set, advanced technology level of the platform, fewer capabilities, greater emphasis on QoS, lower cost coefficient of QoS, and a larger number of incentivized providers.