Key characteristics and challenges of last-mile operations

A recent publication by Agatz et al. provides an overview of key characteristics and challenges of last-mile operations (LMO):

  1. Final Stage of Supply Networks
    • LMO processes occur at the last stage of supply chains, directly fulfilling individual agent demand.
    • Operations and resources (people, equipment, inventory) are widely dispersed.
    • Diverse geographical areas create specific challenges:
      • Urban areas: Narrow roads, traffic unpredictability, parking restrictions.
      • Rural areas: Low population density, long travel distances.
    • Errors in execution become immediately apparent to the agent, leading to perceived service failures.
    • Increasing consumer expectations and reduced local inventory drive the need for rapid response, including same-day and on-demand services.
  2. High Agent-Induced Variability
    • LMO must accommodate the unique demands of geographically dispersed agents.
    • Agents define key service requirements such as delivery location, time, and product assortment.
    • As co-creators of the service, agents introduce operational uncertainty (e.g., failed deliveries, returns, and the use of personal resources).
    • Effective management requires balancing efficiency and service quality while actively engaging with agents.
  3. Labor-Intensive Operations Enhanced by Technology
    • Labor shortages create constraints, particularly for drivers and workers handling heavy items.
    • Low scalability due to high labor dependency.
    • Behavioral factors affecting workforce performance (drivers, pickers, crowdsourced labor, and agents) add complexity.
    • Growing emphasis on automation and human-machine collaboration (e.g., soft automation for handling heavy goods).
  4. Operations in Public Spaces
    • LMOs rely on public infrastructure and involve multiple stakeholders (local authorities, urban planners, and environmental agencies).
    • Subject to external factors beyond the provider’s control (traffic, weather, public events).
    • High visibility increases reputational risks (e.g., vehicle accidents, labor rights concerns).
    • Legal and safety restrictions limit the adoption of new technologies (e.g., autonomous vehicles, drones).
    • Requires an understanding of local public-space dynamics, especially in developing regions.
  5. Negative Externalities
    • Energy-intensive transportation contributes to emissions (CO₂, NOx, particulate matter), noise pollution, congestion, and accidents.
    • Urban logistics hubs (e.g., dark stores) create land-use conflicts and safety concerns.
    • Strict agent requirements reduce flexibility in planning, limiting opportunities to optimize travel schedules.
    • Individual deliveries often require excessive packaging, increasing waste.
  6. Economic and Operational Complexity
    • High marginal costs due to labor-intensive processes.
    • Demand variability and low operational flexibility lead to labor, fleet, and infrastructure inefficiencies.
    • Dependence on multiple independent resources (subcontractors, crowdsourced labor, third-party platforms) raises challenges:
      • Competition over data ownership among retailers and service providers.
      • Aligning incentives and contracts across networked operations is crucial for efficiency.

Expanding LMO Research:

  • Agent-Focused LMO: Emerging services, such as home-based vehicle repairs and elderly care, require more research. Decentralized manufacturing (e.g., on-demand 3D printing) also blends goods- and agent-focused LMO.
  • B2B LMO: Manufacturers increasingly integrate services into their offerings, requiring rapid technician and spare-part deployment across dispersed industrial locations.
  • Global Research Scope: Studies should explore LMO beyond North America, considering regulatory shifts (e.g., EU sustainability laws) and infrastructure challenges in emerging markets.
  • Systems-Thinking Approach: Instead of optimizing isolated components, research should analyze interdependencies within the broader LMO ecosystem (e.g., unintended emissions from consumer pickup points).
  • Empirical Research Needs: Operations Research models must complement real-world studies to capture LMO complexities and inform policy and practice.

Source: Agatz, N., Fransoo, J. C., Rabinovich, E., & Sousa, R. Innovations, Technologies, and the Economics of Last-Mile Operations: A Call for Research in Operations Management. Journal of Operations Management. https://doi.org/10.1002/joom.1355

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