How AI and automation transform bulk goods logistics

One of the most practical and impactful applications of AI in the bulk solids industry is automated vehicle identification. By leveraging Automatic Number Plate Recognition (ANPR) at plant entrances, bulk material facilities and production sites can significantly accelerate the vehicle check-in process – for example, for silo trucks. If deliveries are planned in advance via yard management systems (e.g. the VAS solutions from Axians IAS), check-in can be fully automated and accelerated.

Successful deployment hinges on the seamless integration of ANPR into the yard management system and the selection of a high-performance camera system with exceptional AI-supported recognition capabilities. But the potential of camera-based AI goes well beyond vehicle detection. Webcams and cameras can also be used to monitor loading zones – automatically identifying soiled truck surfaces and preventing improper loading. In rail transport, AI systems can inspect the condition of train wagons before and during loading. For palletized or packaged goods, AI image analysis can verify proper load securing and document the process for compliance and safety purposes..

AI-driven camera and image processing technologies are also transforming material management in open bulk storage areas – such as heap storage for sand & gravel or calcium carbonate. These solutions can measure and monitor material volumes, enabling precise tracking of material quantities. While professional, fixed-camera setups offer the highest level of accuracy, even smartphone cameras can deliver surprisingly reliable results. The collected data can be transmitted in real time to connected yard management systems, ensuring accurate material planning and optimized resource use.

Efficient transport management plays a key role in reducing empty runs, shortening transit times, cutting CO₂ emissions, and ensuring on-time deliveries. AI-driven transport optimization systems analyze a wide range of data sources – including historical trip data, current traffic conditions, weather forecasts, scheduled deliveries, and real-time production capacities. This enables dynamic, real-time route planning that adapts instantly to changes. For instance, if a traffic jam occurs or production is delayed, routes can be adjusted and alternative loading windows proposed – keeping logistics operations flexible and resilient.

AI algorithms determine the most efficient transportation routes based on cost minimization, emission reduction, and delivery punctuality. The result: significantly lower fuel consumption and emissions, coupled with improved customer satisfaction through reliable, on-time deliveries.

A leading example of such a solution is INFORM SYNCROTESS. This AI-based system calculates optimization parameters such as delivery punctuality, fleet requirements, and route distances – offering multiple optimized scenarios. Delivery times are precisely calculated, and routes are continuously adjusted in real time across all scheduled trips. The outcome is reliable, cost-effective transport execution.

To enable this level of efficiency, a robust and centralized data infrastructure is essential. Cloud-based platforms such as VAS Cloud Logistics from Axians IAS consolidate order entry, pre-planning, material flow management, and delivery feedback. This seamless integration allows the AI system to make real-time decisions, reducing logistics costs by up to 30% while boosting customer satisfaction.

AI also significantly improves on-site efficiency – particularly during truck loading and unloading. By analyzing data such as historical arrival times, live traffic data, and current loading bay utilization, AI systems can accurately estimate arrival times and dynamically assign optimal loading slots.

These intelligent systems adjust in real time to current plant conditions, allowing for faster, more efficient vehicle processing. This reduces bottlenecks, minimizes delays, and improves overall truck throughput.

Solutions like VAS Yard Management from Axians IAS provide real-time KPIs to identify inefficiencies and optimize operations. These include material flow analysis, loading bay occupancy monitoring, and plant traffic management. Based on this data, actionable strategies can be implemented to improve efficiency.

Companies using AI-optimized KPI tracking have already reduced door-to-door delivery times by up to 20%, significantly enhancing overall process performance.

Intelligent AI-based delivery planning also enables optimized time slot management for trucks already on-site or approaching the plant. By evaluating traffic data, the availability of loading bays, and current fill levels, AI systems can automatically assign the most suitable time slots and loading stations. To prevent congestion on the premises, trucks scheduled for later loading can be directed to designated waiting areas. This improves flow and minimizes delays.

These smart systems reduce vehicle idle times and enhance the utilization of available resources. They also help avoid contractual penalties due to excessive waiting times. AI can detect process bottlenecks early, enabling proactive adjustments. Historical data analysis allows AI to identify root causes of delays and provide automated optimization recommendations that experts can implement with precision.

Of course, these solutions are only fully effective when they are seamlessly integrated into a yard management solution – such as the Truck Queuing System (TQS) within the VAS Yard Management solution from Axians IAS. This integration ensures end-to-end process visibility and optimization for efficient, reliable vehicle flow management.

Accurate demand planning is essential to avoid supply bottlenecks while minimizing unnecessary storage costs. Traditional forecasting methods, often based on static calculations or historical experience, tend to lack precision –especially in dynamic market environments.

AI-powered demand forecasting offers a smarter alternative. By analyzing a wide range of influencing factors – such as seasonal trends, historical order volumes, and real-time market developments – AI enables far more accurate predictions of material requirements. This paves the way for just-in-time procurement strategies that reduce excess inventory while safeguarding supply reliability.

In addition to forecasting, AI can automate ordering processes and optimize warehouse utilization. As a result, production and delivery workflows are better synchronized, making the entire supply chain more agile, cost-effective, and resilient.

Despite these clear advantages, many companies are still missing out on the benefits of AI in this area. According to a 2024 Bitkom study, only a small fraction of businesses have adopted AI-driven solutions for demand and material management – highlighting the vast untapped potential for optimization and competitive advantage.

Amid growing challenges and rising demands, the bulk solids industry is increasingly turning to technological innovations that redefine standards in process optimization and resource management. A prime example is the process control of landfills for mineral waste. In many European countries, recycling is not always a feasible option, and available landfill space is rapidly diminishing.

This is where artificial intelligence (AI) steps in to make landfill project planning more efficient and precise. AI systems analyze data to identify the most suitable landfill locations based on material types, volumes, and geographical distribution. Key factors such as material properties, transport costs, and contractual requirements are incorporated into the analysis, enabling customized and strategic disposal solutions.

The advantages are clear: enhanced decision-making flexibility, lower transportation costs, and faster disposal processes. Efficiency is taken to the next level when AI technologies are integrated with yard management systems and transport optimization tools—paving the way for cutting-edge, resource-efficient waste management.