AI Solutions
for the logistics and transportation industry
Against a backdrop of expanding e-commerce demand and labor shortages, efficiency and risk management have become urgent issues in the logistics and transportation industry. With the need for flexibility to respond quickly to a variety of risks, such as delivery delays, inventory shortages and excesses, and supply chain disruptions, We use advanced analysis based on on-site data to achieve both cost reductions and stable operations, helping to realize sustainable logistics.
How We Solve
Use Cases
Warehouse Operators
Demand Forecasting to Prevent Stockouts and Overstocks
Automated Inbound and Outbound Operations
Environmental Monitoring (Temperature / Humidity)
Logistics & Transport Companies
Route Optimization for Fuel Efficiency and Delay Prevention
Driver Data Analysis for Safe Driving Support
Vehicle Sensor-Based Failure Prediction
EC Delivery Services
Demand Forecasting Linked to Purchase Data
Automated Parcel Sorting and Tracking
Chatbot
How We Solve
Solutions
From video and speech to language and sensor data, our expert team builds AI solutions that tackle real-world challenges using deep learning.
Logistics risk prediction
Delivery route optimization
How We Solve
FAQ
How long does it take to complete a PoC?
Although it depends on the task and the state of data preparation, in most cases results can be confirmed within 2 to 3 months. For small-scale testing, it is possible to complete it in about 1 month.
How should I estimate costs (initial costs and operating costs)?
It will vary depending on the type of AI used, the amount of data, and the scale of operation. If you contact us, we will provide you with a rough estimate including initial and operating costs.
Is it possible to identify and prevent risks of delays?
Yes, it is possible. By analyzing a combination of factors such as weather, traffic information, and cargo volume, we can predict potential delays in advance. This allows us to respond flexibly, such as by notifying customers in advance and selecting alternative routes.
Can AI be applied to load forecasting and personnel allocation?
Yes, it is possible. By learning from past shipping data, seasonal factors, campaign information, etc., it is possible to create optimal staffing and vehicle operation plans based on demand forecasts. This will prevent staff shortages during busy periods and over-staffing during slow periods.