Manufacturer's Selection between Capacity Investment and Capacity Sharing Considering Random Yield
The rapid development of the sharing economy,especially its innovative applications in the manufacturing sector,has provided manufacturers with additional ways to supplement capacity.Through the capacity-sharing platform,accurate matching between the capacity supply and demand can be achieved.However,utilizing capacity from platform suppliers incurs inherent risks,such as uncertainties in quality and yield rates.In practice,manufacturers have two different attitudes toward supply uncertainty and the difference lies in whether the actual capacity supply must strictly match the ordered quantity.Collaborative sharing facilitates the efficient integration of underutilized manufacturing capacities and effectively addresses societal conflicts stemming from escalating land resource costs and mounting pressures on resources and the environment,which significantly improves social production efficiency and economic benefits.This paper considers a three-tier supply chain consisting of a manufacturer,a capacity supplier,and a sharing platform,where the capacity supplier's output is subject to randomness.We establish a three-stage Stackelberg game model,designating the supplier as the leader and the manufacturer as the follower,and the perfect equilibrium of the subgame is solved by backward induction.All supply chain members make independent decisions to maximize their own profits.We compare two capacity replenishment modes from the manufacturer's perspective:investing in capacity expansion and participating in capacity sharing.In the capacity sharing mode,the distribution of the supply delay penalty income from the supplier is examined.We also explore the ordering strategy of the manufacturer and the production strategy of the capacity supplier.Numerical analysis is employed to discuss the influence of some key parameters on the optimal decisions and profit of each supply chain player.The main research findings are as follows:(1)The impact of platform service fee on its profit is non-monotonic,no matter how the sharing platform allocates the penalty income from capacity supply delays.Specifically,the profit of the sharing platform always increases first and then decreases as the service fee increases.(2)It is not always optimal for the sharing platform to keep the supply delay penalty income for itself,especially when the potential market demand is small or the endowed capacity of the manufacturer is large.(3)There is a linear relationship between the quantity of capacity ordered by the manufacturer and the production quantity of the capacity supplier.This study provides the following managerial implications.First,when formulating the service rate,the platform should carefully balance service income per unit capacity with the overall service volume.That is,the service fee should be set at a reasonable level,as extremely high or low service fees could result in a loss in the platform's profit.Second,the platform needs to make informed decisions regarding the distribution of supply delay penalty income based on the manufacturer's endowed capacity and the potential market demand.Specifically,when the manufacturer's original production capacity is relatively high,allocating the delay penalty income to the manufacturer incentivizes a reduction in the retail price,thereby boosting capacity transactions on the platform.For the platform,higher transaction volume and service fees help offset the loss caused by distributing the delay penalty income to the manufacturer,and thereby enhance overall profitability.This finding offers practical implications for capacity-sharing platforms,aiding in the formulation of trading rules and regulatory mechanisms.Third,manufacturers should determine the capacity replenishment strategy based on the unit capacity expansion cost.In cases of high expansion costs,investing in capacity expansion blindly may hurt manufacturers,participating in capacity sharing through platforms is a more advantageous option.The above conclusions enrich the research on platform-based capacity sharing in the sharing economy,providing valuable decision-making suggestions for platform operators and manufacturers with capacity constraints.
万方数据
维普
