The last mile is one of the most critical and costly segments in logistics. It directly shapes both operational efficiency and profitability. Yet, managing courier workflows remains complex due to varying work patterns, route deviations, traffic congestion, and other external factors. A recent study based on real-world data from a Spanish logistics company sheds light on how courier efficiency can be better understood, measured, and improved.
From Data to Insight
The research analyzed last-mile delivery and pickup operations using a simple yet effective methodology to identify performance patterns. Seasonal trends emerged clearly: January saw a notable peak in demand, while March was the quietest month. Across all periods, the most productive hours were consistently between 9 a.m. and 2 p.m., aligning with morning shifts. This time window proved crucial for assessing performance, allowing researchers to focus on travel and service times as key variables.
Efficiency Is Context-Dependent
While efficiency levels showed slight variation for most couriers individually, differences between couriers were more pronounced. However, these discrepancies could not be explained by personal capability alone. Geographic and contextual factors, such as traffic congestion, parking availability, and delivery density, played a significant role in shaping performance. For instance, a courier in a compact, high-density urban zone faced different challenges and opportunities compared to one covering a dispersed suburban area.
Key Influences on Performance
The study found that workload density broadly impacted efficiency across all profiles, while other factors had courier-specific effects. This heterogeneity underlines the need for nuanced management approaches. Efficiency in last-mile logistics is the result of a complex interplay between individual work habits and the operational environment.
Implications for Logistics Management
The findings suggest that companies can optimize task allocation by factoring in both courier efficiency metrics and the unique conditions of each service area. A balanced distribution of work tailored to the realities of different zones could boost both productivity and job satisfaction. This, in turn, may reduce disruptions caused by end-of-shift time constraints and other operational bottlenecks.
Moreover, efficiency data can help identify reliable, consistent performers whose work patterns could serve as benchmarks for future planning. These insights could support route optimization, workload balancing, and more accurate service time predictions.
Future Research Directions
Further investigation into the geographical characteristics of delivery areas and their relationship to efficiency could refine strategic decision-making. Incorporating data on foot movements, the presence of delivery assistants, and idle times would provide a fuller picture of operational performance.
In short, integrating efficiency metrics into last-mile decision-making offers a powerful way to improve service reliability, optimize resources, and enhance both courier and customer satisfaction.