With more and more advancements in technology, implementing an achievable process optimization plan is no longer farfetched. However, you first need to find the right technologies and approaches that work best for your particular manufacturing operation.
The key to optimizing a manufacturing process is to embrace some of the advanced industry 4.0 technologies available today. By understanding which technology is best for your manufacturing business, you will be one step closer to optimizing your process. Let’s dive a bit deeper into what this means, and into the four main technology pillars to process optimization in manufacturing.
The implementation of automation and use of data in manufacturing is what’s called “Industry 4.0″, with use cases such as predictive maintenance and predictive quality. Industry 4.0 includes the following technologies critical to process optimization:
As mentioned above, by implementing process-based artificial intelligence, process engineers can identify inefficiencies, such as the formation of undesired side products, process instabilities, impurities and more. This can be done with Automated Root Cause Analysis.
Before understanding how this will help you achieve process optimization, let’s take a look at the difference between traditional root cause analysis, and automated root cause analysis.
Firstly, traditional root cause analysis takes time – often measured in days – and expert resources from multiple teams. With massive amounts of data captured from thousands of tags every minute, it’s almost impossible to find correlations between the operational variables that lead to a process inefficiency. The longer the analysis takes – the longer the process inefficiency happens in the production line.
For this reason, production teams need a faster and more accurate way of finding early events that lead to production failures.
Automated root cause analysis enriches historical and real-time asset data, and applies machine learning algorithms to automatically trace the causal chain of events leading to production failures.
By doing so, investigation teams get fast and accurate insight into early symptoms of process inefficiencies, making it easy for them to pinpoint and mitigate the root causes.
Having the ability to identify why process inefficiencies in your production line happen, is priceless. But if you take this one step forward, you can also anticipate exactly when they will happen.
By applying industrial predictive analytics, you have the ability to translate data into predictive insights.
Machine learning algorithms can then be implemented to identify relevant events and predict their outcomes.
For example, predicting when undesired side products will form, or when a specific process instability will happen. By doing this, process teams are able to increase yield and prevent imminent quality failures.
Once we’ve understood why process inefficiencies happen and can predict them before they happen, it is fundamental to understand how to optimize the manufacturing process with these insights at hand.
Predictive simulation determines how specific inefficiencies can be avoided by simulating how processes will behave in different scenarios, and how to avoid the anticipated process inefficiency.
By using predictive simulation, process teams can:
To summarize, the coming of age of industrial artificial intelligence, and machine learning specifically, has introduced an opportunity to harness production-line data to surface actionable insights and drive continuous improvement in manufacturing processes. And digital twin visualization makes it now possible for process engineering teams to use these insights independently of data scientists and take action in a timely manner.
Ready to get started with process optimization, driven by data and machine learning? Contact us!
Augury is building a world where people can always rely on the machines that matter. Augury supports its partners by enabling Digital Transformation through superior insights into the health and performance of the machines they use to make products, deliver services and improve lives.