关键词: 共享经济, 产能共享, 随机产出, 供应弹性

Abstract: Capacity constraints and deviation of demand prediction pose challenges for many manufacturing enterprises, preventing them from achieving a perfect match between supply and demand. The rapid development of the new generation of information technology has given rise to the emergence of the “sharing economy” business model. Sharing manufacturing, as one of the most important application fields of sharing economy, leverages industrial Internet platforms to realize the efficient integration of geographically dispersed idle manufacturing resources and capacity through the sharing of the right to use these resources. This innovative model can improve production efficiency in the manufacturing industry and yield notable economic and social benefits. It is worth noting that risks such as random yield and production variability present significant challenges that seriously hinder the development and scalability of sharing manufacturing. These risks arise from factors such as fluctuating production quality, equipment breakdowns, and unforeseen delays, all of which contribute to the unpredictability of output. On this basis, this paper considers a widely adopted supply contract—quantity flexibility, which allows for a certain level of deviation between the actual supply quantity provided by the capacity supplier and the ordering quantity specified by the demander. To address the challenges arising from output/quality instability and the unpredictability of market demand, the capacity demander leases the production capacity with a lower price through the platform on the one hand, and signs capacity reservation contracts with the backup capacity suppliers with reliable supply on the other hand. This dual strategy aims to mitigate the risk of insufficient production capacity and safeguard smooth operations. If the capacity offered on the platform is insufficient to fulfill the demander's production requirement, then the reserved capacity will be utilized. Taking into account the inherent yield randomness of the platform capacity supplier, this paper investigates the impacts of some key parameters on the optimal dual-channel capacity supplement strategy for the capacity demander, the production decision of the platform supplier, and the profit of each supply chain player when supply flexibility is allowed. By considering various market conditions and their interaction, we provide a more comprehensive understanding of the operational dynamics.
This paper constructs the Stackelberg game model, where the capacity demander acts as the leader, the backup supplier and the platform supplier are the followers. The sequence of events goes as follows: (i)in the first stage, the demander decides how much capacity to reserve from the backup supplier and how much to lease from the platform supplier; (ii)in the second stage, the platform supplier determines the production inputs. All players are independent decision-makers, and each aims to maximize its own expected profit. We establish a two-stage analytical model and determine the subgame perfect equilibrium following a standard backward induction procedure. This paper generates several important findings. First, we show that the demander can derive benefits by granting the platform supplier some supply flexibility. Allowing a certain degree of fluctuation between the capacity demand and the actual supply reduces inventory risks for the platform supplier, enhancing the motivation to share idle capacity. This finding validates that a little quantity flexibility goes a long way. Second, the backup supplier who is perfectly reliable in capacity supply should set the capacity reserve price at an intermediate level, so as to achieve a trade-off between the unit capacity reserve price and the total reservation quantity. Setting the capacity reserve price too low may attract more capacity reservations, but it does not necessarily result in higher returns for the backup supplier. Conversely, setting the capacity reserve price too high significantly weakens the demander's willingness to reserve capacity. Finally, it is important to note that, although a higher service fee may increase the platform's revenue from each successful transaction, excessively high service fees can potentially drive the platform supplier away from the capacity sharing market. Therefore, it is essential to achieve a balance between maximizing the platform's profit and guaranteeing the profitability of other stakeholders involved in capacity sharing when determining the service fee. From a managerial perspective, moderate supply flexibility and balanced pricing strategies are key to optimizing capacity sharing. Flexible supply reduces inventory risks and motivates suppliers to share idle capacity, while intermediate reservation prices balance demand and supplier returns, ensuring long-term profitability and sustainability for all parties.

Key words: sharing economy, capacity sharing, random yield, supply flexibility