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Blog Series Part 6 – Digital Manufacturing Technologies in Review

Posted on March 25, 2020 by

David Staunton, Global Services Director & Ryan McInerney, Technical Consultant

Digital Manufacturing Technologies in Review, part of our: ‘Life Sciences 4.0, Revolutionising Life Sciences Manufacturing Through Connected Systems & Data’ blog series

Digital Manufacturing Technologies – Data Lakes

Big data is stored in a data lake. It is vital that businesses upload the data into the data lake correctly through optimised ingestion practices, otherwise it becomes a data swamp. Ingestion tools are already readily available, allowing for more time to be spent on analysis. As discussed previously, contextualising data is important – a time stamp makes data ingestion easier and importantly, when records are created at the same time as a batch, the correct ingestion allows time-series and context data to be linked. These ingestion tools can then make accurate suggestions and semi-automate the linking of data.

Digital Manufacturing Technologies – Machine Learning & Digital Twin

Machine learning and artificial intelligence will lead to significant change in manufacturing practices. With ‘deep learning’, computers will train themselves by running scenarios and learning from the outcomes, using the massive amounts data available in data lakes with the aim of creating a digital twin. Using simulations to model a process and running scenarios in this virtual system rather than running experiments on real equipment, is already used in pharmaceutical process development.

The concept of the digital twin goes beyond traditional modelling simulation to create a generic digital representation of an asset. The twin captures multiple characteristics of the asset from sensor data, and the data can be used for deviation or anomaly detection, prediction, and simulation. The digital twin models can also learn continuously as they adapt to new information. Building on the DMAIC approach, digital twins will reduce variables by modelling processes, removing a variable and supporting tests for improvement.

Digital Manufacturing Technologies – Edge Devices / Edge Computing

Edge devices collect data and run local analytics on a piece of equipment or process. They store local data for a limited time and only send information to the data lake if there is an event. This keeps the data lake clean of unnecessary information. The resulting data can be used to create insights into performance.

Digital Manufacturing Technologies – Asset Performance Management & Utilisation

Asset performance, throughout its life cycle, is key to every organisation. When assets can talk to each other and communicate data, engineers can get a better understanding of causes and effects of faults and their impact on performance, on a much more detailed scale. Asset performance management tools that can access, interpret and visualise the relevant data in a data lake will offer better predictive asset analytics, risk-based maintenance and condition-based monitoring. Data can also be used to more effectively monitor utilisation and inform decisions around purchasing extra machines, by confirming whether current assets are being fully utilised.

Digital Manufacturing Technologies – Industrial Apps

Contextualised data in a lake will open the door for the development of clever industrial apps that monitor asset utilisation and performance. These apps will be significantly cheaper than current industrial-scale software, and offer real-time, remote, visual monitoring of assets.

In the final blog of the series, we look at how Life Sciences 4.0 aids the quest for manufacturing excellence.

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