【学术视点】按需送餐平台：运营层面的数据和研究机会

On-Demand Meal Delivery Platforms: Operational Level Data and Research Opportunities

Wenzheng Mao, Liu Ming, Ying Rong, Christopher S. Tang, Huan Zheng

Manufacturing & Service Operations Management, available online in Articles in Advance, https://doi.org/10.1287/msom.2022.1112

Recommend Reason

Delivery platforms, such as Meituan, Ele.me in China, Grubhub and Uber Eats in U.S., are a large and rapidly growing part of the restaurant industry. Different from a two-sided business model, meal delivery platforms coordinate various activities among three parties: consumers, restaurants, and delivery drivers. Because of weather and traffic conditions and the restaurant operations for serving online and dine-in customers, it is challenging for a platform to enable its drivers to deliver meals to customers on time. These challenges create research opportunities. To support more Operations Management (OM) researchers to conduct empirical and theoretical research in this area, this paper provides an operational level data set obtained from a meal delivery platform in China. This paper also reviews recent studies on meal delivery platforms and suggest research opportunities directly related to delivery performance.

About the author

Wenzheng Mao, Advanced Institute of Business, Tongji University

Liu Ming, School of Management and Economics, The Chinese University of Hong Kong (Shenzhen)

Ying Rong, Antai College of Economics and Management, Shanghai Jiao Tong University

Christopher S. Tang, Anderson School of Management, University of California

Huan Zheng, Antai College of Economics and Management, Shanghai Jiao Tong University

Keywords

Meal delivery platforms; platform operations; data set; operations management

Brief introduction

This paper describes the operations of most on-demand meal delivery platforms and discusses how empirical research can improve the operational performance of these platforms. To support and encourage more studies on the operations of on-demand delivery platforms, we provide a unique data set obtained from a meal delivery platform in China. This data set contains operational level data sampled from July 1 to August 31, 2015, in Hangzhou, China. The data set includes information about order placements, order deliveries, restaurants, drivers, weather and traffic conditions, and so on. We also review recent studies on meal delivery platforms and suggest research opportunities for improving delivery performance.