On December 10, the DX Research Institute, which provides DX consulting within the Daisei Group, announced that it has officially released "ATMTC (Automatic Transport Management & Tracking Cloud)," an AI-powered automated dispatch, operation management, and data analysis system that it had been testing and implementing within the group, to external companies starting in December.
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ATMTC is an AI-powered automated dispatch and operation management system that is easy for both administrators and drivers to use, optimizing on-site dispatching, operation management, and delivery reporting tasks. It is already in production with 1000 units across the group companies, and its multifaceted benefits are being demonstrated through continuous improvements in the field at logistics companies.
Examples of the effects seen by companies that have implemented the system include: "a reduction of approximately 80% in dispatch time (simple automated dispatch operation)," "a reduction of approximately 10% in driver working hours (visualization of working hours and automated dispatch of the shortest route)," "a reduction of approximately 90% in misdeliveries (prevention of misdeliveries through GPS integration in the driver app)," and "a customer satisfaction rate of over 95% (real-time information on dispatch status and immediate response to customer inquiries)."
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"ATMTC" is a cloud-based logistics management system that integrates dispatching, operation management, and analysis. Through AI optimization and integration of on-site data, it automates and visualizes decision-making in logistics operations.
In terms of functionality, the AI-powered automatic dispatch system automatically calculates the optimal dispatch schedule by considering factors such as the number of vehicles, load capacity, and time specifications. Real-time operation management visualizes the current location and delivery status of all vehicles. The driver-only app allows for the completion of daily reports and arrival reports within the app. GPS integration prevents misdeliveries. For document and report output, it can output operation-related reports, working hours tables, and delivery performance tables.
Going forward, we will provide support for implementation to external logistics companies, and will sequentially expand the integration with other systems through API linkage, as well as the analysis and KPI visualization functions for dispatch and operation performance data. In addition, we aim to further improve the accuracy of automated dispatching and optimize operations through continuous tuning and version upgrades of the AI model.
