The new KPIs of efficiency
Companies regularly measure downtime, but what other indices should industrial internet-enabled factories look out for? Dr. Dirk Lange, Technical Director at ARTIS GmbH, talks about the new key performance indicators of manufacturing.
In his opinion, the new KPIs (key performance indicators) of efficiency will include numbers denoting overall equipment effectiveness as well as energy consumption. Lange finds data analysis to be a crucial part in determining these numbers.
“One KPI that I think will continue to be relevant in the future is Overall Equipment Effectiveness, or OEE. This refers to the quality and speed of production. If you have several machines running in a line, performing the same task, the smart factory allows you to compare between their individual performance rates,” says Lange. “Sensors enable the measurement and comparison of productivity between machines, and the resulting data helps to figure out the reason for differences in performance,” he continues.
In addition, the smart factory makes it easier to measure energy consumption and detect opportunities for improvement. According to Lange, energy efficiency is becoming increasingly important to manufacturers. Data from smart machines can help reduce consumption by pointing out which part of the process is eating up energy, for instance.
The right quantity matters as well
In Lange’s view, companies in industries such as steel, pulp and paper, waste-to-energy, ports and shipping, and automotive should focus on measurements that are important to the manufacturing process and that help to detect issues in the quality of work. “Again, the overall equipment effectiveness is very important, but that is only one aspect. Another critical point is that the right quantity is being measured,” Lange says.
“For example, consider the machinery of motor blocks or those in the automotive industry. If you can detect early that something is wrong with the machine—for instance, the surface quality is not like it should be—you can react and stop the machine, which is the easiest solution, or you can adapt the machine to do something else. However, early detection requires having the right sensors measuring the right quantity,” Lange explains.
“Data mining and data analysis will be crucial in the future.”
“It makes no sense to merely integrate sensors in the machine for the sake of doing so. While we have sensors, we must go intelligent and see what kind will really help us. We must use sensors to detect specific conditions such as, for example, damage in spindles and drives, unbalancing, increasing cycle times, decreasing tool life, and worsening quality,” he says.
Measuring predictive maintenance
Lange has been working in his area of expertise for 20 years. According to him, the discussion around the need for predictive maintenance has been going on for about the same time. He shares what has surprised him about the application of measurement to this technique.
“There is not yet a real, reliable solution for predictive maintenance in the machine tools industry,” he reveals. “In high-speed machining, for example, damage is difficult to predict. Because of the high speed of cutting and the variety of tools being used, damage can occur very quickly. But if you look in contrast to aggregates with stable conditions, there are solutions available.”
The role of data analysis tomorrow
Having access to big data also yields new challenges, Lange notes. “To give you an example, in our projects in the automotive industry, process data and condition monitoring data are collected from over a hundred machines. The growing number of sensors results in a vast amount of data, which needs to be stored somewhere. This is a big challenge.”
“Therefore, data mining and data analysis will be crucial in the future. Raw data will have to be collected and compressed into an accessible, intelligible form. To this end, we will have algorithms and strategies which give the user an easy-to-use tool and will, on the other hand, act as the base for a higher grade of automation. This can lead to intelligent factories with increased performance and economy,” Lange concludes.
As Technical Director of ARTIS GmbH, the German tool monitoring company, Dr. Dirk Lange is responsible for product development and application technique.