Operations in the On-Demand Economy: Staffing Services with Self-Scheduling Capacity
Northwestern University - Kellogg School of Management
Northwestern University - Department of Managerial Economics and Decision Sciences (MEDS)
May 4, 2015
Motivated by recent innovations in service delivery such as ride-sharing services and work-from-home call centers, we study capacity management when workers self-schedule. We assume that agents determine for themselves whether or not to work in a given period. The service provider thus seeks to maximize its profit (revenue from served customers minus capacity costs) when it controls capacity only indirectly. Agents choose when to work based on the compensation offered and their individual availability. To guarantee adequate capacity, the firm must offer sufficiently high compensation.
These novel service platforms provide a variety of benefits to the firm, the agents and the service's users. However, our analysis shows that self-scheduling can impose costs on the firm and its customers. Relative to the setting in which the firm can dictate when agents work, the firm has lower profits and the customers a higher chance of not being served. Furthermore, in the face of time varying demand, self scheduling results in lower service level in high demand periods. We show that the firm has an incentive to increase its pool of agents in order to drive down the compensation rate it must offer. If the firm must offer a minimum compensation rate, it no longer chooses an arbitrarily large pool but it does limit agent flexibility by restricting the number of agents that can work in some time intervals. Our key results are robust to the agent-compensation mechanism and to the pricing capability of the firm.
Number of Pages in PDF File: 22
Keywords: strategic servers, on-demand economy, independent capacity, distributed systems, service operations, Uber
Date posted: October 6, 2013 ; Last revised: May 5, 2015
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