Truck scale weighing has traditionally included several manual steps related to recording data, inspecting loads, and ensuring quality. Going through these steps takes time and ties up personnel resources. The digitalization of weighing processes improves data management and automates different stages, which reduces manual work. When artificial intelligence is integrated into weighing, it enables even broader automation and more efficient operations. In this way, truck scale weighing becomes an intelligent part of a larger operational whole.
mScales’ AI features have not been developed as a separate technical experiment, but as a solution to real customer challenges. AI is utilized in different situations where weighing involves a significant amount of manual inspection work.
“Customers have very different needs. One may need to read a container number, another to identify whether a load is covered, and a third to ensure that the material in the load is what it is supposed to be,” says mScales Solution Architect Arttu Pakarinen.
What these situations have in common is that the information to be checked is often visual. Traditionally, this has required human effort, time, and attention to detail. This is exactly where AI brings the most significant benefit. It enables automated recognition, pre-screening, and decision support without rebuilding the entire process.
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One of the key applications of AI is load identification and inspection. The driver drives onto the truck scale and starts the weighing with mScales, after which cameras connected to the scales capture images. The images are automatically stored with the correct weighing record, and AI analyzes them by comparing observations with predefined data and requirements.
AI can, for example, check whether the load is covered or not, whether the material in the load matches the product defined in the order, or whether the load contains something that does not belong there. Only cases where uncertainties or deviations are detected are directed to further review.
Automated functions can be built around the process. Typically, a detected deviation triggers a notification to the operator, who then reviews the specific load and takes any necessary follow-up actions, such as recording a complaint.
In one customer organization, the use of AI has reduced manual inspection work by up to 90 percent. This is because the majority of loads proceed directly, and employees only need to review exception cases. This results in direct time savings and more efficient use of resources.
The time saved in load inspection is just one example. AI also brings other benefits, such as:
Implementing mScales’ AI features does not require a heavy project or long-term training. A foundational model has been built in the background, containing a vast amount of information. It can recognize a wide range of elements and continuously improves.
“Customers have reported that the recognition works well. Surprisingly well. The system has identified, for example, tree stumps, wood, and plastic in loads,” says Pakarinen.
To ensure that AI integrates smoothly into the customer’s processes, its functionality is fine-tuned for each customer. For example, if the goal is to identify products from a product catalog, a product name alone is not always sufficient. A description of what the product should look like is also needed. The use of natural language makes this easy. Instead of training AI from scratch to recognize each material separately, it is often enough to provide a clear description of what the load should be like and what should not be present.
If cameras are already in use at the scale, implementing AI features does not require significant additional investments. mScales can be integrated with any truck scale regardless of the manufacturer. In organizations where mScales is already in use, AI features can be enabled easily by activating them in the system.
Download the guide to discover how AI can be applied across different areas of industrial weighing:
The use of AI in industrial weighing is still at an early stage, but the direction of development is clear. Automatic recognition is increasing, and a growing share of routine inspection work can be transferred to AI.
In the coming years, the role of AI is also likely to grow in filtering and processing information. The use of natural language opens new possibilities, as users can describe in words what kind of information they need. From a weighing perspective, this may mean, for example, that a user requests a summary of a specific customer’s loads from last month, weighings that included deviations, or the total quantity of a certain material over a specific period, and the system compiles the data directly from weighing records.
However, full automation is not in sight. In industrial weighing, AI is not a replacement for humans, but a tool. It streamlines processes and reduces manual work, but responsibility for the accuracy of measurements remains with humans.
“Manual work will not disappear completely. Weighing processes can be highly automated, but system supervision, handling of exceptions, and ensuring the reliability of results will still require a human role,” Pakarinen concludes.
The article is based on an interview with mScales Solution Architect Arttu Pakarinen. He has over seven years of experience in developing weighing solutions and adapting them to customer business needs, as well as strong expertise in AI-based systems.
Read more:
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Weighbridge Software Brings Benefits To The Traditional Way Of Weighing
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