Logistics optimization of last mile deliveries with Artificial Intelligence
Date
Brief relate
The last-mile delivery and collection services sector is experiencing permanent double-digit growth due to the push of the expansion of electronic commerce, which is generating enormous pressure on logistics companies. In addition, customers increasingly have higher expectations regarding the speed and convenience of deliveries, and are less tolerant of delays and errors in orders.
The general objective of the project is to develop a system for predicting incidents, analysis and proactive automated actions through the application of Machine Learning techniques that allow the reduction of failed deliveries. This will have as a direct consequence the reduction of kilometers traveled by transporters and therefore the reduction in CO2 emissions, the reduction in urban congestion and an improvement in the quality of service to customers, thus contributing to a model of more competitive and sustainable business.
In the project, an incident prediction tool is designed and developed with Machine Learning techniques, implementing an automated system that minimizes last mile incidents. Finally, it is validated with pilot tests of the new operation in messenger environments.
One of the keys to the last-mile delivery and pickup services business is failed deliveries. Currently, a significant number of incidents occur that translate into a significant impact on the quality of service and the profitability of specialized logistics companies that work with tight margins. These incidents are services not delivered on the agreed date, rejected for not being in accordance with the conditions of the service or anomalies prior to the distribution of the order. Depending on multiple factors, or types of services, the percentage of incidents in the sector can average between 10% and 20% of total services, according to different studies.
Transport agencies can take certain measures to try to reduce these failures as there are behavioral patterns in the services, which using artificial intelligence techniques allow them to identify those deliveries in which incidents will occur. The early detection of possible incidents will allow last-mile delivery agencies to carry out early proactive actions that will result in improved reputation with customers, reduced costs and an increase in the sustainability of urban transport.
