Last-mile operations (LMO), the processes involved in the critical last stage of delivering goods and services, are widespread across major economic sectors, including retail, food services, healthcare, humanitarian services, energy distribution, telecommunications, public services, and others. These operations account for many of these sectors’ costs, jobs, and economic output.
Global economic output involving last-mile deliveries alone, for instance, is valued at $165 billion annually and is growing at about 10% annually. According to Agatz et al. (2025), the evolution of last-mile operations (LMO) has accelerated over the past decades due to technological innovations, which have had significant implications for their planning, design, and execution.
Convenience, accessibility, and reliability
These innovations have led to increased convenience, accessibility, and reliability. Examples include drones that safely deliver life-saving products to remote communities, smart lockers at pharmacies that provide 24/7 medication access, and platforms that enable small neighborhood stores to sell and deliver daily necessities. Artificial intelligence is used in tools for vehicle routing, warehouse automation, track-and-trace systems, anticipatory shipping algorithms, and integration tools with third-party services.
Despite technological advancements, challenges remain, such as limited room for human error due to short time frames and high delivery volumes. Many companies rely on low-skilled, temporary, or crowdsourced labor, leading to performance and workforce availability variability. LMO is costly due to rising labor expenses, delivery errors, demanding customers, and vehicle and parking restrictions. There is increasing attention to managerial, economic, and socio-technical challenges as critical factors for its success.
New research is needed in LMO
Agatz et al. state that new research is needed to understand these challenges better and propose new operational practices and business models based on recent innovations. This requires broadening the phenomenological and theoretical scope of LMO research.
Theories on innovation, technology, productivity, and employment can provide a valuable foundation for studying the scalability of technologies and the changing nature of work in last-mile settings. LMO can be classified into two main categories: goods-oriented and agent-oriented. Goods-oriented LMO involves providing access to goods at a chosen location and time. At the same time, agent-oriented LMO includes broader services where service resources are moved to the agent’s location, e.g., repair operations.
Unique challenges for operations management
LMO processes are characterized by several distinctive properties that pose unique challenges for operations management. These include their position in the final stage of supply networks, high agent-induced variability, labor intensity, occurrence in public spaces, and negative externalities. Future research should focus on expanding the scope of LMO studies, developing more substantial theories, understanding the potential and limitations of emerging technologies, investigating new business models, and improving delivery execution. More attention should be given to agent-oriented LMO, B2B LMO, and LMO in various geographical settings. Research should also benefit from a systems-thinking approach and more empirical studies.
Knowledge from service operations, people-centric operations, behavioral operations, socially responsible operations, urban planning, and urban logistics would benefit theory development in LMO. Contingency theory and configurational approaches can help generate prescriptive knowledge.
Emerging technologies, such as autonomous vehicles and smart lockers, offer potential, but further research is needed on their scalability, interaction with agents, and impact on service processes. Advanced analytics can enhance LMO, but human factors must be considered.
The LMO literature heavily focuses on routing optimization while largely neglecting routing execution. Few studies explore the causes of execution failures, their impact on future demand, and the effects of spatial interventions like parking availability.
Research opportunities include incorporating agents’ private information on routing and deliveries, leveraging consumer behavior (e.g., price sensitivity, willingness to assist in delivery) to optimize resource allocation, and using AI to detect routing failures early and predict demand for better efficiency. Additionally, the literature assumes full accuracy in the delivery address data despite errors in geocoding.
New business models
Several business models have emerged in online commerce that have impacted LMO across different industries. These include platform models, commonly observed in the delivery of products and services to consumers. The primary benefit of these models is that they reduce transaction costs between consumers and product and service vendors, offer consumers low-cost access to products and services, and induce higher purchases such that a higher gross margin per purchase or basket could offset high operational costs.
The economic viability of these models depends, in part, on reducing frictions (i.e., transaction costs and search costs) involved in matching consumer demand for items in vendors’ inventories and for services in the menu of options offered by service providers. New business models, such as platform, subscription, and cost-sharing, influence LMO. Research on their economic viability and operational implications is needed.
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