MIT Research: why do drivers not follow planned routes in last-mile delivery

Last-mile delivery optimization is complex. Companies are implementing TMS-tools to include customer-specific or other constraints such as time windows and congestion patterns in their last-mile delivery plans. But, what if drivers do not follow the planning information provided by the routing tools in TMS systems.

MIT Megacity Logistics reviewed data on deliveries over a one-year period for a large soft drinks company in Mexico and the US and found that three out of four deliveries did not follow the planned sequence. The research project studied the extent to which delivery crews deviate from the planned stop sequence of their routes. Additionally, they analyzed what drives these deviations and economic implications.

The research suggests that drivers tend to repeat routes that are familiar to them, and rely heavily on their past experience for taking decisions, using their knowledge on factors such as road conditions and tolls. The use of real-time information applications such as Google Maps or Waze is another factor that influences behavior. However, the stated preferences do not always match revealed preferences.

Understand driver behavior

MIT used the company’s recorded data of the deliveries to understand driver behavior. The data MIT used includes information of the planned route sequence and the actual sequence performed by the driver, such as total time and total distance. Also included is specific information on the customers visited, such as location, the drop size and the planned versus the actual sequence. This data provides valuable information that had not been used previously to find insights regarding drivers’ behavior and the economic impact it can have.

The research provides some insights into driver behavior. For example, drivers are more likely to deviate and increase route distance when more customers are visited. This observation implies that efforts to improve delivery plans should be focused on these routes. Additionally, customers’ geographical locations are highly useful in predicting deviations and improving planning reliability.

Source: MIT

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