In this project we want to: Take the supply chain to the next level of sustainability using artificial intelligence. The goal of this project is to develop a software to decarbonize the transport and logistics industry using artificial intelligence. All required tests and inspections of trucks/containers are performed without human intervention and include: License plate code + Container weight + Container volume + ContainerID + Loading type with unique innovation namely loading container safety detection + Container damage detection based on AI and all-in-one API through our future software. Project overview The development of intelligent truck and container detection software aims to increase efficiency and accuracy in AI-based logistics centers. This software uses advanced image processing and machine learning technologies to optimize traffic flow and warehouse management. Objective Automation: Reduce manual processes through automated detection and monitoring of trucks and containers. Real-time analytics: Providing real-time data to improve decision-making and resource allocation.

Error reduction: Minimizing human errors and associated costs through precise identification and tracking.

Technological basis

AI-supported image processing: Use of deep learning algorithms to recognize trucks and containers from video and image data.

IoT integration: Connection to existing systems and sensors in the logistics center for data aggregation.

Cloud-based infrastructure: Ensuring scalability and flexibility to process large amounts of data.

Advantages

Increased efficiency: Optimization of loading and unloading processes as well as storage space utilization.

Cost reduction: Reduction in operating costs through lower resource requirements.

Competitive advantage: Improvement of service quality and response times compared to competitors.

Market analysis

With the growth of e-commerce and increasing logistics requirements, the need for innovative solutions is increasing. The software addresses specific challenges in the industry and
positions itself as a key technology for modern logistics centers.4
Implementation strategy
Phase 1: Development of a prototype and implementation of initial tests in a controlled
environment.
Phase 2: Scaling of the solution and integration into existing logistics processes.
Phase 3: Market launch and continuous improvement based on user feedback.

The Logistics Industry is facing a $bn problem Delays and disruptions of the supply chain Over 40% of truck inspection appointments experience a delay of over 2hours in the US 12% of trucks experience detention rates of $66/hour The annual cost of these delays is estimated at 3bn just for the US Logistics industry

AI can preventive maintenance can lead to a 40% decrease in unplanned down time Precise and proactive AI Inspection can lead to a 20-30% decrease in truck restoration costs which can be translated to annual savings of over €300/truck

Logistics-Artificial Intelligence