Seco Machine Monitoring for Maximizing Manufacturing Efficiency

Seco’s Machine Monitoring is designed to offer a complete machine connectivity ecosystem that gives users the data-driven knowledge they need to increase machine uptime and optimize overall equipment effectiveness by leveraging their shop’s big data.

The system enables users to measure and quantify the causes of unproductive time so they can manage production efficiency as well as increase both output volume and product quality, the company says. Done in real time and remotely, this technology solution samples production data on a continuous basis to build a full picture of what happens on a manufacturer’s shop floor and beyond. These insights are said to build an in-depth profile of daily activity and trigger alerts for on-site response or remote notification of production-critical metrics.

The system is said to eliminate bottlenecks and boost overall equipment effectiveness (OEE). Detailed insights into machining operations on all attended and unattended shifts are said to make it easy for manufacturers to track OEE and make continuous improvements to operations for better planning, scheduling and communication. Overall, such data-driven insights can reveal the real root causes of production problems that hinder workflow optimization.

The technology is also said to provide cost-effective, facility-wide data collection in order to accelerate responses to individual applications, machines, cells and overall production workflow. This machine connectivity is said to enhance communication from the shop floor to upper management based on data-driven smart manufacturing.

The color-coded metrics on real-time dashboards give manufacturers the insights they need to improve efficiency, the company says. Employees are also able to easily add their insights and observations, categorize machine downtime and help contextualize problems. These service solutions result from partnerships with an exclusive network of software providers and technology partners such as MachineMetrics.