Policy Making to Encourage Platform Thinking in Manufacturing: A System Dynamics-based Multi-Objective Optimization Approach

Abstract

Platform thinking involves creating an ecosystem that enables different participants to interact and co-create value. This mindset shift moves away from the traditional linear value chain model toward a more collaborative, networked approach. In the manufacturing sector, platform thinking has given rise to new business models like manufacturing as a service (MaaS) and cloud manufacturing (CMfg). In this paper, building upon existing literature, a system dynamics (SD) model is developed to analyze the acceptance of platform thinking as an innovative solution in manufacturing. In contrast to previous studies, the model considers the dynamics of customer behavior in addition to the roles of the platform provider (government) and service providers (manufacturers). The model incorporates four decision variables: commission rates, user membership fees, and subsidies for both cloud platform users and service providers, which can be adjusted to inform policymakers. Studying the effect of individual decision variables over a 30-year time frame using the SD model reveals that increasing commission rates and user membership fees negatively affect the platform size (number of manufacturers joining and staying on the platform), yet can result in increased long-term platform revenue. On the other hand, providing subsidies for cloud platform users and service providers has a positive impact on the platform size but leads to increased costs for the platform provider. A non-dominated sorting genetic algorithm (NSGA-II) is employed to optimize the system dynamics model and identify optimal policy settings that balance the interests of all stakeholders. This approach enables policymakers to make informed decisions that support the growth of CMfg and MaaS, fostering innovation and competitiveness in the manufacturing sector.