A convergence of market drivers is reshaping factory automation. The increasing demand for customized products is just one example of how these trends are transforming production lines worldwide. In the pursuit of perfect production at assembly lines, overall efficiency effectiveness (OEE) has emerged as the yardstick that tells manufacturers whether they are producing quality products or components, as fast as possible, and with fewer interruptions. OEE focuses on three elements—availability, performance, and quality—to help factory managers keep their finger on the pulse of production.
How OEE Works
OEE measures the effectiveness of manufacturing by means of a numerical value. A formula calculates the availability rate, performance rate, and quality of output of any given machine or tool to show how many usable components it has manufactured in a set time frame. OEE is calculated as follows: availability rate x performance rate x quality rate = OEE
Each factor is first calculated separately. The availability rate indicates the percentage of time a machine is operating fully without any downtime. The formula is as follows: running time ÷ planned production time
The performance rate refers to the actual output compared to a standard output. The formula is as follows: ideal cycle time x (total count ÷ running time)
Both the availability and performance rates refer to the status of the machine.
The third component of the formula, the quality rate, reflects directly on the produced units. It measures the ratio of products passing quality control processes against the actual output. It is calculated as follows: good count ÷ total count
If a machine under-performs according to its OEE benchmark, then OEE can help line managers and operators identify the problem areas in their factory. Normally, one of the three aforementioned factors needs to be addressed to ensure that a piece of equipment remains effective in its value stream.
OEE is related to the three major levels in a factory: the device level, the control level, and the information level. This means all the production data needs to be collected from machines or sensors and sent via a network to a manufacturing execution system (MES). In the past, the OEE information was limited to shop floors only. With the advent of Industry 4.0, the collected data can now be uploaded to the cloud for big data analytics.
The importance of OEE in Industry 4.0
OEE and Industry 4.0 go hand in hand. In fact, OEE is the outflow from the manufacturing industry’s endeavors to address the challenges of Industry 4.0. With the rise of Industry 4.0, manufacturers soon realized that a high degree of flexibility will be required from their assembly lines as market forces are now dictated by an era of mass customization. Customers want products tailored for their specific needs and personal tastes, and the Henry Ford-style business model of mass production, which minimizes variation, no longer meets their demands. Thus, the trend is shifting towards mixed-model production lines that put out a larger variety of products, but fewer quantities of a specific product. Toyota originally developed the concept of mixed-model production in the 1960s to undo the hold-ups created by line changeovers. The biggest challenge is not to compromise efficiency during changeovers in order to achieve a high degree of variation. This is where OEE helps manufacturers make mass customization work for them. As this kind of manufacturing is more complex and has more challenges with regard to workflow and material flow, OEE benchmarks help mixed-model production lines incur minimal or no losses in time, quality, cost, or quantity.
Integrated solutions for a bevy of benefits
Because OEE conveys a lot of information within one number, it is influential. At all times, OEE must be a yardstick for improvement and not a rod for handing out punishment. The best approach is to try understanding what needs to be changed in the manufacturing process to get better results on shop floors. A three-pronged approach that examines the availability, performance, and quality rates is suggested. Once the problem areas have been identified, new technologies can be sought to address the shortcomings in a manufacturing process.