Datafluct began offering DATAFLUCT Route Optimization, a delivery route prediction digital transformation service, on October 9.
The service collects data on delivery performance via GPS, and analyzes delivery conditions such as the available number of trucks, number of drivers, cargo volume, number of delivery destinations, and the desired delivery times together with external real time data like weather and traffic information to predict the optimal route and timing.
The company leveraged its knowledge and technology cultivated in the field of data science to develop the service in order to solve the emerging shortage of truck drivers in Japan’s logistics sector, and to aid in both maximizing corporate profits and improving working conditions for drivers by predicting optimal routes.
It utilizes a data analysis base known as a “data lake” which aggregates a wide variety of structured and unstructured data, enabling consolidated management. A proprietary algorithm combines data on delivery conditions gathered via GPS such as the number of available trucks, total number of drivers, cargo volume, and delivery destinations with external real time data including past traffic information (congestion, accidents, etc.) and weather information.
In addition, the accuracy of the predictions improves through machine learning the more of this data is accumulated. They aim to make the service available to businesses within 9 months from the start of the project by rapidly designing the data lake, analyzing the data, and implementing the application.