Maximizing the benefits of an on-demand workforce: Fill rate-based allocation and coordination mechanisms - LU, Tao (Institutional Knowledge at Singapore Management University, 2023)
Problem definition: With the rapid growth of the gig economy, on-demand staffing platforms have emerged to help companies manage their temporary workforce. This emerging business-to-business context motivates us to study a new form of supply chain coordination problem. We consider a staffing platform managing an on-demand workforce to serve multiple firms facing stochastic labor demand. Before demand realization, each individual firm can hire permanent employees, whereas the platform determines a compensation rate for potential on-demand workers. After knowing the realized demand, firms in need can request on-demand workers from the platform, and then, the platform operator allocates the available on-demand workforce among the firms. We explore how to maximize and distribute the benefits of an on-demand workforce through coordinating self-interested parties in the staffing system. Methodology/results: We combine game theory and online optimization techniques to address the challenges in incentivizing and coordinating the online workforce. We propose a novel and easily implementable fill rate-based allocation and coordination mechanism that enables the on-demand workforce to be shared optimally when individual firms and the platform operator make decisions in their own interest. We also show that the proposed mechanism can be adapted to the cases when contract terms need to be identical to all firms and when actual demand is unverifiable. Managerial implications: The proposed contract mechanism is in line with the performance-based contracting commonly used in on-demand staffing services. Our results suggest that under an appropriately designed performance-based mechanism, individual firms and the platform operator can share the maximum benefits of on-demand staffing.