Innovations, Technologies, and the Economics of Last-Mile Operations: A Call for Research in Operations Management

Abstract

Last mile operations (LMO), the processes involved in the critical last stage of delivering goods and services, have widespread relevance across major sectors of the economy, including retail, food services, healthcare, humanitarian services, energy distribution, telecommunications, public services, and others. These operations account for a significant portion of the costs, jobs, and economic output in these sectors. Global economic output involving last mile deliveries alone, for instance, is valued at $165 billion per year and is growing at about 10% per year (InsightAce Analytic 2024).

Recent decades have witnessed an acceleration in the rate of evolution of LMO (Agatz et al. 2024; Boutilier and Chan 2022; Boyer and Hult 2005; Dreischerf and Buijs 2022; He and Goh 2022; Lyu and Teo 2022). Technology-driven innovations have catalyzed profound changes in the planning, design, and execution of LMO, with significant implications for the economics of these operations. Extending the last mile to the final user has increased convenience, accessibility, and reliability. Zipline, for example, has introduced drones to safely deliver lifesaving products in remote communities (Ackerman and Koziol 2019). An increasing number of pharmacies in Europe and Africa have been equipped with smart lockers to allow 24/7 access to critical medicines (Gobir et al. 2024). Some innovations leveraging platforms based on smartphone apps have given small corner stores in neighborhoods in cities across Latin America the means to sell and deliver daily groceries and other household staples to local residents (Escamilla et al. 2021). Other innovations, leveraging artificial intelligence, have found applications in vehicle routing tools and warehouse and fulfillment automation (such as Ocado's system (Mason 2019)), track-and-trace systems that provide real-time communications and visibility into delivery processes (such as Instacart and Uber Eats), anticipatory shipping algorithms to move inventories to specific areas ahead of realized demand (Chen and Graves 2021), and integration tools with third-party services (successfully deployed by ClickPost and ShipEngine).

However, considerable challenges remain. For example, because of short time frames and high delivery volumes to many dispersed locations, LMO have little room for human error. Yet, since many firms tend to tap into low-skilled, temporary, or crowdsourced labor to provide these services, there is high variability in performance and worker availability. LMO are also expensive, due in part to rising labor costs, delivery failures, more demanding customers, and vehicle and parking restrictions. Although academic research in LMO has a long tradition in Operations Research (see e.g., Agatz et al. (2011), Otto et al. (2018), Boysen et al. (2019) and Reed et al. (2022)), LMO have barely been considered as an operations problem that requires process understanding and management within a sociotechnical system. The need for this is apparent, as increasing evidence points to managerial, economic, and sociotechnical challenges as major determinants of LMO success. Delivery workers have been noted to largely ignore the recommendations by routing algorithms in urban settings (Liu et al. (2023)); working conditions are an increasing societal and corporate concern; and customer experiences are less than satisfactory in many cases. Further, LMO are associated with negative externalities such as emissions, traffic congestion, and the abuse of public parking space. Operational costs are also very high—often up to a point where LMO are loss-making, such as in grocery home delivery. And, while there have been extensive technological innovations, many seem to fail in scaling at large, which could potentially be due to a poor understanding of the LMO from a process perspective.

We need new research to better understand these challenges, as well as to propose new operational practices and business models based on the application of recent innovations. Such research requires a broadening of the phenomenological and theoretical scope of LMO research beyond traditional work in Operations Research. Theories on innovation applied to Operations Management can offer a valuable foundation to study research questions surrounding the scalability of technologies to support new business models in the last mile (Arthur 1994). Similarly, theoretical models examining technology, productivity, and employment can provide a foundation to understand how innovations can change the nature of work in last-mile settings (Autor et al. 2003; Autor 2015). Additional opportunities also exist to use transaction and information cost theories to understand how technological innovations may change organizational boundaries and the nature of organizations in the last mile (Afuah 2003).

This confluence of innovations in the field, the multidimensional phenomena that determine performance, and the perspectives from theories from the operations management field provide an opportunity to shape a research program in LMO that will benefit from the Operations Management academic community. This was one of the main goals of our call for papers for the special issue on “Innovations, Technologies, and the Economics of Last-Mile Operations.”

Another objective of this special issue was to formalize a research agenda and offer future directions for research to advance our understanding of LMO. To that end, in Section 2, we delve deeper into these operations, their functionalities, distinctive features, and challenges in the context of Operations Management. Then, in Section 3, we expand on research opportunities to tackle the most pressing challenges in LMO and identify knowledge gaps in Operations Management to be addressed in this endeavor. We close in Section 4 with conclusions, recommendations, and potential initiatives to build on the momentum created so far and further advance LMO as a knowledge area within Operations Management. In doing so, we introduce the several papers in the special issue as exemplars of research that can be done in the LMO domain.

LMO are made of processes triggered by an agent (e.g., consumer, user, patient, worker, organization) that enable the provision of a service to this agent at the agent's selected location and time (or time period). LMO involve interactions with the agent—who participates in the process and co-creates value—and, by definition, comprise different service processes (Sampson and Froehle 2006). These processes are triggered by an agent's request for service and include the preparation and movement of goods and/or tangible resources (people, equipment) required for providing the service to the agent's selected location at the agent's selected time. A key trait of LMO is the fact that agents select the location and time of the provision of the service and that the provision of the service requires at least in part co-location with the agent.

We submit that LMO can be classified into two main categories that differ significantly in the nature and extent of the associated customer co-creation activities (Sampson and Froehle 2006): goods-focused and agent-focused. Goods-focused LMO entails the provision of agent access to goods at a selected location and time, involving the preparation and movement of goods (e.g., groceries, meals) and resources (e.g., delivery vans, delivery people) to that location. A typical example would be e-commerce deliveries to consumer homes. Agent inputs are limited, primarily including information about the required goods (product selection and quantities) and delivery (time and location), as well as engaging in minor interactions with the provider during goods reception. The core value added is the movement of the goods to the agent's selected location and time. Goods-focused LMO correspond to “delivery services” and have received most research attention. Agent-focused LMO entail the provision of more general services to an agent at a selected location and time, involving the preparation and movement of service provision resources (e.g., people, equipment, inventory) to that location. A typical example would be performing repairs of equipment owned by the agent at its selected location, involving the movement of technicians, tools, and inventory (spare parts) to the agent's location. Another example would be an emergency ambulance service, which involves the movement of equipment (vehicle, medical instruments), medical staff, and inventory (medical supplies) to the agent's location. Agent inputs are substantial, including information about the required service, service delivery time and location, and agent's resources, as well as engaging in relevant service co-creation activities at the agent's location. The core value added is the transformation of the agent's inputs (e.g., agent-owned equipment, the agent self). Typically, the level of customization and agent co-creation increases from goods-focused to service-focused LMOs, while the transaction volumes decrease.

LMO processes are characterized by a set of distinctive features that raise unique challenges for the management of operations. Based on our conceptualization of LMO and extant literature, we summarize LMO's distinctive features and associated challenges in Table 1. The remainder of the editorial will discuss LMO against this framework and address in more detail several of the distinctive features and challenges. The distinctiveness of LMO processes, their pervasiveness and widespread economic relevance, and the managerial challenges that remain unaddressed jointly motivate the development of a specific research program for LMO within the field of Operations Management.

The features and challenges presented in Section 2 provide a framework for the development of new LMO research that can broaden the scope of LMO subject knowledge, as well as strengthen the theoretical foundations supporting LMO research. This framework also serves as a reference for new research to inform about new technologies and business models in LMO and their implementation and execution. The remainder of this section expands on these research directions.

Despite its growing presence and importance in many businesses and across business models, LMO has received relatively little attention in the operations management literature. In fact, research on LMO has been conducted almost exclusively in the operations research literature. We argue that LMO is a service operation that takes place in close coordination with the agent receiving the service. As such, work on LMO can build conceptually and theoretically on the extensive OM literature on service operations.

The rapid expansion of LMO is a direct consequence of the growth in online commerce, which has been enabled by the rapid adoption of new technologies and the associated rapid development of new business models over the past two decades. While many companies have been focused on top-line revenue growth, many of the new players have been less concerned about the quality, cost, efficiency, and environmental and social impact of their LMO. As businesses mature, the attention for better understanding and subsequent improvement of the LMO becomes more imminent.

Both the practical challenges and the limited empirical work on LMO require extensive additional and novel research that advances our understanding and provides new insights. New LMO technologies and business models emerge on a regular basis, with many of them failing. More in-depth theoretical understanding should help companies design better operations that can deliver the envisioned service at a lower cost and reduce the service's negative externalities. Rigorous empirical work can help in evaluating the true prospects of the many novel technologies that still emerge.

The four papers published in this special issue were selected from a pool of 30 submissions and provide interesting and novel perspectives on LMO. They contribute to the Operations Management literature on LMO across the retail, logistics, and food service industries. The paper by Chen, Su, Linderman, and Li (“Last-Minute Coordination: Adapting to Demand to Support Last-Mile Operations”) focuses on durable goods' retailing. In this context, they examine unstructured mechanisms to adjust and direct inventories across fulfillment centers to meet unforeseen last-mile delivery requirements caused by uncertainty in consumer demand. Their results, based on a collaboration with a logistics service provider managing a network of fulfillment centers, underscore the complexity involved in these mechanisms and the need for centralized coordination to ensure greater efficiency across these facilities. In their paper (“Targeting Online Sales through Last-Mile Delivery Platform Integration”), Park, Pan, and Dresner address the LMO feature “challenging economics and operational complexity” and the related challenge of reliance on independent resources from third-party platforms. Specifically, they study the integration of a major grocery retailer's own online delivery channel (Target) with a third-party last-mile delivery platform (Shipt). Their finding that the integration augments sales for both the delivery platform and the retailer's online sales channel has important implications for understanding the economics of these LMO networks. The paper by Zheng, Mao, Ming, Rong, and Tang (“Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform”) focuses on the meal delivery market and identifies underlying factors contributing to improve delivery performance and customer retention. The paper combines empirical and analytical methodologies based on a study with a last-mile delivery platform to identify these factors and establish guidelines to minimize delivery time, minimize delivery lateness, and maximize customer future orders. Finally, the paper by Li, Wang, Liu, Zhang, and Tang (“Real-Time Orders, Restaurant Density, and Delivery Reliability: An Empirical Analysis of On-demand Meal Delivery”) focuses on restaurants to evaluate how real-time demand and spatial density affect delivery reliability. The study draws from data collected in partnership with a last-mile delivery platform to reveal an inverted U relationship between real-time demand and delivery reliability and a positive relationship between density and delivery reliability.

When analyzing these papers through the lens of our framework presented in Table 1, we find a clear emphasis on features 1, 2, and 6. They predominantly address the economic and operational challenges of meeting dispersed customer demand with timely service delivery. While these contributions help to advance our understanding of economic and operational aspects in LMO, substantial opportunities remain for exploring the framework's other dimensions. With LMO generally being labor-intensive and taking place in the public space, OM research on LMO would benefit from a broad perspective including human behavior: as a service delivery worker, as a client, and as a stakeholder making use of the same limited public space. In doing so, LMO research has the potential to expand the field far beyond the extensive work that has been done in transportation and operations research, drawing on and contributing to different theoretical perspectives within the OM field, as well as exploring relevant interfaces with fields as diverse as customer behavior, labor economics, business information systems, and urban planning.