The Opportunity
Manual Process
Quality checks relied on human intervention, increasing the risk of error during production
High Material Waste
Delays in decision-making caused unnecessary losses of copper and insulation material
Variable standards
The start-up phase of extrusion differed by recipe, making it harder to ensure consistent product quality
The Solution
We deployed a machine learning quality decision model based on historical data that runs on factory floor machines. In real time, it determines when the cable is approaching target properties.
A key aspect of this solution was inference time. Each second without a decision meant costly material waste, so predictions needed to be immediate. Our model delivers real-time predictions, using only the minimum material required.
We deployed our machine learning model within the factory floor machines, ensuring seamless integration with existing systems. This enabled the client to leverage the benefits of AI on legacy machines without incurring costly infrastructure upgrades.
The Impact
The results were significant. Each machine now saves over €300 per day, representing million-euro annual savings across the client’s operations. More than 1,000 extrusion recipes are modeled, and 12m of copper and insulation material are saved on every run.
By automating quality verification, operators are freed from constant monitoring and can focus on higher-value tasks. The client was thrilled with both ROI and operational efficiency, and based on this success, we are now expanding the solution to improve their cable design process. This is fully aligned with one of DareData’s main goals: delivering constant value via innovation while building long-term strategic partnerships.
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