Shiju Li

The production and leasing of electric construction machinery play a critical role in the low-carbon transition. However, from a multi-cycle dynamic perspective, there is a lack of targeted research on how to enhance electric goodwill and AI-enabled maintenance service levels while maximizing enterprise profits. To fill this gap, this study incorporates AI-enabled O&M effort, R&D technology, AI-enabled maintenance effort, and advertising effort into a long-term dynamic framework to examine optimal decisions for the manufacturer and the lessor. We assume that the information in the leasing supply chain is symmetric, that the marginal profits of the manufacturer and the lessor are fixed parameters, and that the AI-enabled maintenance service effort level and the electric goodwill are taken as state variables. We develop differential game models across four decision cases: centralized (Case C), decentralized (Case D), unilateral cost-sharing contract (Case U), and bilateral cost-sharing contract (Case B). Results demonstrate monotonic state variable trajectories. Both Case U and Case B can achieve supply chain coordination, with the profit-sharing mechanism in Case B proving superior. In addition, the optimal cost-sharing proportion depends on the relative sizes of the manufacturer’s and the lessor’s marginal profits in both Case U and Case B. The AI-enabled maintenance service plays a significant role in enhancing equipment reliability and supply chain resilience. In addition, the impacts of key parameters on optimal decision variables, state variables, profits, and coordination of the leasing supply chain are comprehensively discussed.