July 2019

| 22 | July 2019 www.smartmachinesandfactories.com | STRATEGIES | understand way, can help operators identify and respond to anomalies in the process. Data analysis helps operators Displaying process operation data in this way can already deliver 20%-30% efficiency increases. However, as the amount of data increases, humans are less able to interpret it or perceive patterns. By incorporating large data analysis software, computers offer a more accurate and tireless tool to support humans. These tools can identify irregularities in performance data and flag potential issues to the operator. With more data and more advanced or ‘smarter’ analysis, the insights and results become more comprehensive and accurate. For example, instead of just identifying an issue, the system can locate exactly where the problem is in the line and what needs to be done to fix it. The operator’s job is made easier and line efficiency is further optimised. As the amount of data increases, data management also becomes important. Collected data is often taken offline for advanced processing and pattern recognition. Then, the resulting patterns are transferred back to the factory to be implemented in real-time by the machine. Using data to increase automation We can take this automation a step further. As previously mentioned, Smart systems could identify an issue or potential issue, flag it, and then automatically adapt parts of the production line to compensate for any shortfall whilst the problem was being fixed – all within safe operating parameters. Once again, this results in even better production efficiency. Let us consider this at the level of an individual machine. Smart machines – equipped with data analysis capabilities – can optimise their behaviour for any given situation because they ‘know’ how they are supposed to work normally. They monitor their own performance, ensuring it matches expected behaviour. If a defect or divergence from a standard pattern occurs, the machine reports the issue to the entire system and if possible, compensates for the issue by amending its operation. From a system viewpoint, any alterations must be balanced throughout the line to ensure consistent operation between machines. Real smart factory automation Complexity of data is one thing that makes moving to a smart factory a major challenge. We are implementing these smarter systems into our own processes, allowing us to investigate requirements and develop best practices – and there is plenty to learn. When we started looking at our own processes about two years ago, our very first data scientist spent 80% of his time just cleaning up the data. Companies who have taken this