How manufacturers can improve OEE using IoT
Today, the Manufacturing Industry faces stiff global competition due to new companies and new brands entering the market each day. Factors such as quality, efficiency, and deliverability are now the benchmarks for a company’s success. Optimizing Overall Equipment Efficiency (OEE) to maintain manufacturing productivity standards is crucial as it directly impacts business performance. Let’s explore what OEE is and how IoT can provide visibility to manufacturing companies to understand production losses and improve overall OEE.
What is OEE?
Overall Equipment Effectiveness or OEE is a standard to measure the manufacturing productivity across industries. Manufacturers are continually striving to achieve 100% OEE, but many reasons can affect productivity.
You can improve profitability by optimizing your processes in various ways. Still, it can be challenging to understand the overall effectiveness of a complex operation that includes multiple pieces of equipment and where each machine’s effectiveness is co-dependent.
OEE is one metric that will help you to meet this challenge. OEE helps manufacturers access the process’s reliability and health and check if it works at the required accuracy level. Manufacturers can identify gaps in machine utilization and resource utilization and compare them with the quality and availability of the finished product.
OEE is calculated by multiplying the three main factors:
- Availability
- Productivity
- Quality
To reduce OEE losses, proper availability of machines, and maintenance and handling of equipment are vital. To find the root cause and rectify errors, manufacturers can effectively use a powerful tool like IOT to accurately and consistently monitor existing bottlenecks. IoT provides data that helps manufacturers to find the inconsistencies in their process.
Six Big Losses of OEE (Overall Equipment Effectiveness)
Seiichi Nakajima is the father of OEE and TPM (Total Productive Maintenance), and he first used OEE to measure and track production performance. He created a 6-point framework called “Six Big Losses” to capture the inefficiencies. The six big losses are connected to the three main factors of OEE, which are described above.
Here are the Six Big Losses of OEE
Availability Losses
Unplanned Downtime
Unplanned Downtime is a significant amount of time when the equipment scheduled for production stops working because of equipment failure. It can also happen because of material shortages, part breakdown, or unplanned maintenance. Unplanned downtime or equipment failure is the leading cause of expenses for manufacturers.
Predictive maintenance using IoT can help manufacturers to tackle unplanned Downtime. IoT can help to gather data on temperature, machine vibrations, and current. Manufacturers can analyze the data collected through IoT devices to narrow down and pinpoint the symptoms preceding any old failures. This data helps manufacturers to predict when machine breakdowns can happen in the future and service the equipment before a failure. Predictive maintenance using IoT helps to reduce not only production downtime but also the meantime to repair.
Planned Downtime
Planned Downtime is a significant amount of time when the equipment scheduled for production is not operating due to part changes, tool adjustment, or planned maintenance and inspection. Even though planned Downtime is unavoidable, IoT can help manufacturers reduce the impact.
Unlike scheduled maintenance based on a periodic schedule, Predictive maintenance is performed based on the equipment’s condition and helps reduce availability losses and Planned Downtime.
The IoT data provides insights into Planned Downtime events and gives manufacturers the visibility to see inefficiencies in processes during part changes and tool changes. This visibility helps manufacturers take corrective action to reduce these inefficiencies.
Performance Losses
Reduced Speed
Reduced speed is when the equipment runs slower than the ideal cycle-time, directly affecting the total production output.
Worn out or dirty equipment, poor environmental conditions such as high levels of humidity or dust, insufficient lubrication, substandard materials, and operator inexperience can be some of the reasons for reduced speed.
IoT sensors can provide manufacturers with vibration data enabling operators to know when the machines are not running at reduced speeds. In addition to the vibration data, environmental sensors can provide information on the environmental factors that affect the speed and help manufacturers learn the underlying reasons and to take remedial action.
Minor Stops
Minor stops, idling, shortstops are when equipment stops operating for a short time, like a minute or two. Usually, this happens due to material jams, misfeeds, misaligned parts, quick cleaning, or incorrect settings. Since these stops are for a short duration, manufacturers usually ignore them and are blind to their impact. In the long run, these minor stops can have a snowball effect and affect OEE.
IoT sensors can provide manufacturers insights into the frequency and reasons for minor stops. The data gives manufacturers clarity on the exact place these short stops occur in the production process, helping them understand chronic problems and resolving them.
Quality Losses
Process Defects
Process Defects occur when products are not manufactured as per the established quality standards even during the stable or steady-state production resulting in either rework or scrapped products. Process defects happen due to operator or machine handling errors, incorrect equipment settings, or the inconsistency in raw material quality due to the factory’s environmental conditions.
The data gathered through IoT sensors help manufacturers monitor the equipment, determine reasons for process defects and detect environmental anomalies affecting raw material quality.
Start-up Losses
Start-up losses occur when defective parts are produced from the start-up until a steady production state is reached. The faulty parts include parts that need rework and parts that need to be scrapped. Start-up losses occur in machines that need warm upcycles or incorrect settings when a new part is run.
The data harnessed from IoT devices can provide manufacturers an indication as to which start-up conditions or changeover cycle creates more defects. Manufacturers can leverage this data to make decisions to tackle these issues.
Final Thoughts
IoT enables manufacturers to identify and rectify factors that negatively impact OEE. Monitoring equipment performance, reducing downtimes, and process defects using analytics produced by connected devices help manufacturers make informed decisions and proactive steps to limit production losses.
Manufacturers can obtain a wealth of production data and operational specifications by implementing IoT solutions and sensors connected to their existing Controller or PLC systems. Using a machine monitoring system like IoT will provide manufacturers with valuable data for future analysis.
