Technologies are evolving rapidly in the manufacturing equipment today. Operations teams have to deal with diverse technologies, and are confronted with a wide range of standards, data formats and communication protocols. As such, it is far from easy to have a real-time view of the status of a production line, which leads to decisions being made on intuition, rather than on facts. This leads to a reactive approach to managing and configuring the production environment, increasing the risks of downtime and lost productivity.
Productivity is often measured in OEE (Overall Equipment Efficiency), a KPI percentage calculation that uses equipment availability, productivity, and quality metrics to calculate a number that summarizes how well a piece of equipment or production line is operating. OEE quantifies how well a manufacturing unit performs relative to its designed capacity, during the periods when it is scheduled to run.
IoT (Internet of Things) can play an important role in optimizing the OEE of a production line or even of a complete production plant. For plant managers, process engineers and maintenance personnel, the key to improving the OEE is being able to easily combine and correlate data from different sources, such as industrial control systems (SCADA), sensors, applications, infrastructure and IT systems, and deliver valuable new insights into asset health.
Detailed understanding of equipment performance through analytics and machine learning can help identify and remedy problems in three ways.
- Providing advanced warning of equipment degradation or failure to avoid unplanned downtime;
- Carefully monitoring production line quality – is equipment properly calibrated? are component metrics beginning to divert from prescribed dimensions? are process parameters (speed, time, temperature, …) within target ranges? – to accurately determine and remedy root causes of quality problem to improve yield. Studies have shown that companies can make major improvements in OEE by focusing mainly on small adjustments happening on the production line(s);
- Analyzing historic process and performance data to optimize maintenance planning, schedules, and resources, leading to lower maintenance costs, reduced material and supplies, and greater equipment availability.
In manufacturing, IoT is gaining a lot of momentum, and will be here to stay. Production machine sensors combined with artificial intelligence and machine learning are leading a new wave of productivity improvement. The Inimco solutions connect industrial assets and processes with customers and services, delivering value from data and supporting a business model shift. Do not hesitate to contact us if you want to discuss how we can help you improve your OEE !