Abstract
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. Our service provider chooses capacity to maximize its profit (revenue from served customers minus capacity costs) over a horizon. Because demand varies over the horizon, the provider benefits from flexibility to adjust its capacity from period to period. However, the firm controls its capacity only indirectly through compensation. The agents have the flexibility to choose when they will or will not work and they optimize their schedules based on the compensation offered and their individual availability. To guarantee adequate capacity, the firm must offer sufficiently high compensation. An augmented newsvendor formula captures the tradeoffs for the firm and the agents. If the firm could keep the flexibility but summon as many agents as it wants (i.e., have direct control) for the same wages it would not only generate higher profit, as is expected, but would also provide better service levels to its customers. If the agents require a “minimum wage” to remain in the agent pool they will have to relinquish some of their flexibility. To pay a minimum wage the firm must restrict the number of agents that can work in some time intervals. The costs to the firm are countered by the self-scheduling firm’s flexibility to match supply to varying demand. If the pool of agents is sufficiently large relative to peak demand, the firm earns more than it would if it had control of agents’ schedules but had to maintain a fixed staffing level over the horizon.
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Notes
- 1.
Describing the people serving customers for these firms requires some finesse. Generally, those answering calls or driving customers are not employees. Rather, they are independent contractors whose continued relationship with the service provider is dependent on achieving a minimal level of performance (e.g., an Uber driver rating) over time. We will generally refer to those serving customers as agents.
- 2.
http://driver.grubhub.com/. Accessed May 24, 2016.
- 3.
http://www.liveops.com/company/careers-jobs Accessed May 24, 2016.
- 4.
- 5.
- 6.
See workathomemoms.about.com/od/callcenterdataentry/a/arise.htm accessed on May 24, 2016.
- 7.
See www.glassdoor.com/Reviews/Employee-Review-LiveOps-RVW2743190.htm accessed on May 24, 2016.
- 8.
Fixing the staffing level for the entire horizon is admittedly extreme. However, it implies that the result do not depend on the exact sequence of high and low demand periods.
- 9.
This conclusion depends on \(\varepsilon \left ( p\right ) \) being strictly increasing. If \(D\left ( p\right ) =p^{-\tilde {\varepsilon }},\) the elasticity of demand is constant at \(\tilde {\varepsilon }\), making \(\hat {p}\) proportional to η and the optimal service level independent of η.
- 10.
For example, note the following complaint about UK on-demand delivery service Deliveroo: “I was working in a bar when a friend of mine started working for Deliveroo. I was sick of working until 2am and I really like cycling so I decided to join too. I wanted as many hours as possible, but cycle couriers mainly do a three-hour shift at lunchtime and three hours in the evening, because those are the busiest times for Deliveroo. … Its not flexible either. We used to have a system where you could swap shifts with people but they said it was too chaotic. Now you do the same shifts every week.” (https://www.theguardian.com/money/2016/jun/15/he-truth-about-working-for-deliveroo-uber-and-the-on-demand-economy?CMP=share_btn_tw)
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Gurvich, I., Lariviere, M., Moreno, A. (2019). Operations in the On-Demand Economy: Staffing Services with Self-Scheduling Capacity. In: Hu, M. (eds) Sharing Economy. Springer Series in Supply Chain Management, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-01863-4_12
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