A smart digital platform that uses the internet of things (IoT), manufacturing execution system (MES), artificial intelligence, digital twins, big data and user interface applications to provide end-to-end solutions and modular applications.
Bilkent Cyberpark, H Blok, 1.Kat No: 13-14, TR-06800 Bilkent, Ankara Türkiye
TEKNOPAR Merkez Yerleşkesi Uzay ve Havacılık İhtisas O.S.B, G3 Caddesi No:9 06980 Kahramankazan / Ankara
tia@teknopar.com.tr
+90 312 395 99 07
+90 549 548 38 47
With the continuous advancement of technology, the efficiency of machines and equipment used in production systems is also steadily increasing. However, ensuring the sustainability of this efficiency requires accurate monitoring and systematic improvement. At this point, the question “What is machine efficiency and how is it measured?” becomes crucial.
Machine efficiency (or machine performance) refers to the ratio between a machine’s theoretical capacity and its actual output. The most widely used method for measuring machine efficiency is Overall Equipment Effectiveness (OEE). OEE is calculated based on three main parameters: availability, performance, and quality.
Availability: The ratio of the machine’s actual operating time to the planned production time. Example: If a machine is scheduled to run for 10 hours but actually operates for 9 hours, its availability is 90%.
Performance: The ratio of the actual average operating speed to the theoretical (ideal) speed. Example: If the theoretical speed is 100 units and the machine operates at 95 units on average, its performance is 95%.
Quality: The proportion of defect-free products to the total number of produced items. Example: If 98 out of 100 units are defect-free, the quality rate is 98%.
The OEE score is calculated by multiplying these three parameters. In the above example, with 90% availability, 95% performance, and 98% quality, the OEE score is 83.79%. The critical point here is the multiplicative effect of the parameters: even a small decrease in one parameter can significantly reduce the overall OEE score. For this reason, OEE provides clear insights into which areas—maintenance, quality, or process efficiency—require improvement.
Accurate collection of machine data is essential not only for OEE calculations but also for performance, downtime, failure, and quality analyses. Data collection methods can be classified into five main categories:
Sensors and IoT Devices: Parameters such as vibration, temperature, pressure, and current are measured by sensors. Optical sensors are used to track product counts and defect ratios. This method allows both machine conditions and production outcomes to be monitored reliably.
PLC Integration: Data is directly extracted from the systems controlling the machine, providing insights into performance and quality.
SCADA Systems: Used on a larger scale for production lines; enables centralized monitoring of multiple machines.
MES (Manufacturing Execution System): Consolidates all production data flows into an online environment, enabling comprehensive management of machine data.
Manual Data Collection: Based on operator logs and observations. Due to low efficiency and high risk of error, this method is largely abandoned in modern facilities.
The main data collected through these methods include: production quantity, production time, downtime and reasons, number of defective products, energy consumption, failure records, and maintenance logs. Selecting the right method is crucial for ensuring data reliability.
Real-time monitoring of machine data enables early detection of potential problems and timely intervention, thus preventing possible failures in advance. Key advantages include:
Reduced Downtime: Early identification of failure symptoms prevents prolonged stoppages.
Quality Assurance: Real-time data enables early detection of factors that may negatively impact product quality.
Operator Alerts: Notifications generated by the system allow operators to take immediate action.
Remote Monitoring: Machines across different production sites can be monitored from a single center.
These functionalities make real-time monitoring systems indispensable for modern manufacturing facilities. The most common technologies used for this purpose are IoT platforms, SCADA panels, and cloud-based monitoring solutions.
Process optimization in machines and production systems can be achieved through various approaches. However, some fundamental practices can be applied across all industries:
Regular Maintenance: Scheduled preventive maintenance reduces unplanned downtime and extends equipment lifespan.
Operator Training: Well-trained operators reduce error rates and enhance both performance and quality.
Data-Driven Decisions: OEE scores and other machine data should be analyzed to identify and resolve bottlenecks.
Automated Reporting: Regular reports ensure reliable performance monitoring and trend analysis.
Software Updates: Keeping machine management software up to date improves control and efficiency.
Spare Parts Management: Having spare parts for critical machines readily available minimizes downtime caused by failures.
Artificial Intelligence Applications: AI-powered predictive maintenance methods enable early detection of potential failures, ensuring continuous improvement.
