“Coordinating Supply and Demand on an On-demand Service Platform with Impatient Customers” by Professor Christopher S. Tang
STRATEGY AND IB SEMINAR
Professor Christopher S. Tang
University Distinguished Professor
UCLA Anderson School of Management
Consider a situation when an on-demand service platform uses earnings-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its wait-time and price sensitive customers with heterogeneous valuation of the service. As such, both the supply and demand are “endogenously” dependent on the price the platform charges its
customers and the wage the platform pays its independent providers. In this paper, we present a queueing model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to model this on-demand service platform. To coordinate endogenous demand with endogenous supply, we use the steady state performance in equilibrium to characterize the optimal price and wage rates that maximize the profit of the platform (as well as the total welfare). We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price). We then extend our model to allow the platform to adopt a dynamic payout ratio. Due to the fact that exact analysis based on an M/M/k system is
intractable, we develop an approximation scheme to generate some analytical results, and show that our approximation scheme performs well. Based on our analysis, we find that it is optimal for the platform to charge a higher price, pay a higher wage, and offer a higher payout ratio when the potential customer demand increases. Furthermore, when customers become more time-sensitive, the platform should also pay a higher wage and offer a higher payout ratio, but the price rate is not necessarily monotone. We used a set of actual data from a large on-demand ride-sharing platform in numerical experiments to illustrate some of our main insights.