The first month with the predictive monitoring system was more intense than I expected, but the results are already noticeable. Before, downtime due to assembly line failures cost us at least two hours every week. With the CPS sensor network, we receive early vibration alerts on the main belt motors. In the first week, we detected abnormal wear on a bearing and were able to replace it during scheduled maintenance without stopping production. The team's learning curve was fast: the control panel shows exactly which sensor triggered the alert and the reading history. It's not a system that configures itself; it requires adjusting the thresholds during the first few days. But once calibrated, the accuracy is high. What I value most is that the digital twin allowed us to simulate the impact of a failure before it occurred. That changed the way we plan shift changes.
Our reason for being is to design and implement Cyber-Physical Systems that transform production automation in Argentine factories. We do not seek only to connect sensors, but to build a data architecture that allows engineers to make real-time decisions, reduce unplanned downtime, and improve the traceability of each produced unit.
The principles guiding each project are three: deep integration with existing SCADA systems, edge processing to minimize latency, and digital twins that anticipate failures before they occur. The expected effect for our clients is a measurable reduction of at least 30% in downtime and a 25% improvement in early detection of quality deviations.
We work with plants in Córdoba, Buenos Aires, and Rosario, adapting each solution to the local productive reality. We do not sell generic hardware; we design CPS sensor networks that integrate with existing equipment and deliver actionable data to the control center. The mission is clear: make Argentine industrial production more predictable, efficient, and competitive through cyber-physical technology.