Order Ahead for Pickup: Promise or Peril?

45 Pages Posted: 11 Nov 2020

See all articles by Yunan Liu

Yunan Liu

North Carolina State University - Department of Industrial Engineering

Luyi Yang

University of California, Berkeley - Haas School of Business

Date Written: August 13, 2020

Abstract

Recent years have seen growing adoption of order-ahead among quick-service restaurants. Ordering ahead enables customers to place orders on demand remotely and then travel to the service facility for pickup. It is widely believed that order-ahead reduces delay and therefore attracts more orders than if customers must order on-site. We build queuing-game theoretic models to study the implications of order-ahead for delay announcement and system throughput. We show that if the market size is small, a throughput-oriented service provider should give no real-time delay information to remote customers; if the market size is intermediate, the service provider should still withhold delay information from remote customers but reveal it to in-store customers; if the market size is large, the service provider should share delay information with remote customers. Contrary to conventional wisdom, the prevailing order-ahead model used in practice may yield a lower throughput than the order-onsite model. We propose two approaches to mitigate this throughput deficiency. The first approach rejects new orders at the outset if there are already too many outstanding ones; the second approach allows customers to cancel their orders in the process if they so choose. While both approaches restore the throughput superiority of order-ahead over order-onsite, neither always dominates the prevailing order-ahead model that does not support rejection or cancellation.

Keywords: on-demand service, digital innovation, omni-channel retail, information provision

Suggested Citation

Liu, Yunan and Yang, Luyi, Order Ahead for Pickup: Promise or Peril? (August 13, 2020). Available at SSRN: https://ssrn.com/abstract=3673617 or http://dx.doi.org/10.2139/ssrn.3673617

Yunan Liu

North Carolina State University - Department of Industrial Engineering ( email )

Raleigh, NC 26695-7906
United States

Luyi Yang (Contact Author)

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
64
Abstract Views
261
rank
386,221
PlumX Metrics