An official website of the United States government
Digital platforms have become increasingly dominant in many industries, bringing the concerns of adverse economic and societal effects (e.g., monopolies and social inequality). Regulators are actively seeking diverse strategies to regulate these powerful platforms. However, the lack of empirical studies hinders the progress toward evidence-based policymaking. This research investigates the regulatory landscape in the context of on-demand delivery, where high commission fees charged by the platforms significantly impact small businesses. Recent regulatory scrutiny has started to cap the commission fees for independent restaurants. We empirically evaluate the effectiveness of platform fee regulation by utilizing regulations across 14 cities and states in the United States. Our analyses unveil an unintended consequence: independent restaurants, the intended beneficiaries of the regulation, experience a decline in orders and revenue, whereas chain restaurants gain an advantage. We show that the platforms’ discriminative responses to the regulation, such as prioritizing chain restaurants in customer recommendations and increasing delivery fees for consumers, may explain the negative effects on independent restaurants. These dynamics underscore the complexity of regulating powerful platforms and the urgency of devising nuanced policies that effectively support small businesses without triggering unintended detrimental effects.
more »
« less
Assessing the Effects of Limited Curbside Pickup Capacity in Meal Delivery Operations for Increased Safety during a Pandemic
Meal delivery has become increasingly popular in past years and of great importance in past months during the COVID-19 pandemic. Sustaining such services depends on maintaining provider profitability and reduced cost to consumers while continuing to support autonomy and independence for customers, restaurants, and delivery drivers (here crowdsourced drivers). This paper investigates the possible enactment of curbside regulations in the U.S. that limit the number of drivers simultaneously waiting at restaurants to pick up meals for delivery on both public safety and delivery efficiency. Curbside regulations would aim to increase safety by enabling social distancing between delivery personnel at pickup locations and have a secondary benefit of improving local traffic flows, which are sometimes impeded in busier, urban locations. Curbside space limits are studied in relation to their impacts on consumer-related performance measures: freshness of the food on delivery and click-to-door time. This investigation is enabled through a proposed hybrid discrete-event and time-advanced simulation platform that replicates meal delivery service calls and pickup and delivery operations across a region built on data from a leading meal delivery company. Embedded within the simulation is an integer program that optimally assigns orders to drivers in a dynamically changing environment. Order assignments are constrained by imposed curbside capacity limits at the restaurants, and potential efficiencies and curbside violation reductions from bundling orders are assessed. Results of analyses from numerical experiments provide insights to state and local communities in designing curbside restrictions that reduce curbside crowding yet enable delivery companies to retain their profitability.
more »
« less
- Award ID(s):
- 1823474
- PAR ID:
- 10213302
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2675
- Issue:
- 5
- ISSN:
- 0361-1981
- Format(s):
- Medium: X Size: p. 436-452
- Size(s):
- p. 436-452
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Restaurants are increasingly relying on on-demand delivery platforms (e.g., DoorDash, Grubhub, and Uber Eats) to reach customers and fulfill takeout orders. Although on-demand delivery is a valuable option for consumers, whether restaurants benefit from or are being hurt by partnering with these platforms remains unclear. This paper investigates whether and to what extent the platform delivery channel substitutes restaurants’ own takeout/dine-in channels and the net impact on restaurant revenue. Empirical analyses show that restaurants overall benefit from on-demand delivery platforms—these platforms increase restaurants’ total takeout sales while creating positive spillovers to customer dine-in visits. However, the platform effects are substantially heterogeneous, depending on the type of restaurants (independent versus chain) and the type of customer channels (takeout versus dine-in). The overall positive effect on fast-food chains is four times as large as that on independent restaurants. For takeout, delivery platforms substitute independent restaurants’ but complement chain restaurants’ own takeout sales. For dine-in, delivery platforms increase both independent and chain restaurants’ dine-in visits by a similar magnitude. Therefore, the value of delivery platforms to independent restaurants mostly comes from the increase in dine-in visits, whereas the value to chain restaurants primarily comes from the gain in takeout sales. Further, the platform delivery channel facilitates price competition and reduces the opportunity for independent restaurants to differentiate with premium services and dine-in experience, which may explain why independent restaurants do not benefit as much from on-demand delivery platforms. This paper was accepted by D. J. Wu, information systems. Funding: Z. Li is grateful to the National Science Foundation Division of Social and Economic Sciences for support provided through the CAREER award [Grant 2243736]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01010 .more » « less
-
Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.more » « less
-
Abstract Local delivery networks expect drivers to make deliveries to and/or pickups from customers using the shortest routes in order to minimize costs, delivery time, and environmental impact. However, in real‐world applications, it is often the case that not all customers are known when planning the initial delivery route. Instead, additional customers become known while the driver is making deliveries or pickups. Before serving the new demand requests, the vehicle will return to the depot for restocking. In other words, there exists a precedence relation in the delivery route to visit the depot before delivering new orders. The uncertainty in new customer locations can lead to expensive rerouting of the tour, as drivers revisit previous neighborhoods to serve the new customers. We address this issue by constructing the delivery route with the knowledge that additional customers will appear, using historical demand patterns to guide our predictions for the uncertainty. We model this network delivery problem as a precedence‐constrained asymmetric traveling salesman problem using mixed‐integer optimization. Experimental results show that the proposed robust optimization approach provides an effective delivery route under the uncertainty of customer demands.more » « less
-
Crowdsourced delivery platforms face the unique challenge of meeting dynamic customer demand using couriers not employed by the platform. As a result, the delivery capacity of the platform is uncertain. To reduce the uncertainty, the platform can offer a reward to couriers that agree to be available to make deliveries for a specified period of time, that is, to become scheduled couriers. We consider a scheduling problem that arises in such an environment, that is, in which a mix of scheduled and ad hoc couriers serves dynamically arriving pickup and delivery orders. The platform seeks a set of shifts for scheduled couriers so as to minimize total courier payments and penalty costs for expired orders. We present a prescriptive machine learning method that combines simulation optimization for off-line training and a neural network for online solution prescription. In computational experiments using real-world data provided by a crowdsourced delivery platform, our prescriptive machine learning method achieves solution quality that is within 0.2%-1.9% of a bespoke sample average approximation method while being several orders of magnitude faster in terms of online solution generation.more » « less
