Digital transformation is quickly reshaping our world as we know it. The fourth industrial revolution is well underway, which has had an impact on the manufacturing, industrial and engineering industries. This digital shift – coupled with the Internet of Things (IoT) and Cloud technologies – are generating a vast amount of data every day. According to the independent research firm Gartner, 20 billion connected “things” will be connected to the Internet by 2020—each generating more and more data.
As industrial organizations look to digitalization to transform operational effectiveness, improve safety and increase production, interest in digital twin technologies is rising.
Here, I’ll address some of the most common questions manufacturers ask about digital twin and share best practices to ensure an effective digital twin strategy.
What Is a Digital Twin?
A digital twin is a digital, 360° representation of a physical asset such as a compressor, motor or an entire plant. The digital twin represents not just the structure, but also the behavior of the physical asset in real life. This digital likeness can be manipulated to simulate operations under different conditions to provide visibility and predictability into asset behavior. In turn, this empowers personnel to anticipate problems before they happen.
For manufacturers, digital twin applications include designing for quality and innovation (Quality by Design) and managing and decommissioning assets. The digital twin captures real-time data from the physical object to simulate the asset’s behavior. This is particularly helpful where on-site inspection of the physical asset is inconvenient, costly or hazardous. It allows a shift from preventive maintenance, based on historic data, to predictive maintenance, based on real-time data.
Similarly, a digital version of the asset can be very useful when managing modifications, particularly if the asset will be partially or completely unmanned, or if it is highly complex.
In the food and beverage industry, it is quite common for one maintenance crew to monitor several facilities. The ability to visualize and understand an asset at a remote facility in digital format before visiting it can be very helpful.
It’s All about the Data
A digital twin is only as good as the data on which it is built and the real-time data that it collects. Thus, an effective digital twin strategy requires both a comprehensive data set for each asset including master engineering data, plant design, construction and commissioning history, and maintenance history. And, a strategy must be in place to maintain that data to ensure it remains current and accurate.
Whenever possible, get the data directly from the company or consulting firm that designed or built the plant, as it should have rich, digital models of the asset in digital format. However, it’s not uncommon to find inadequate or incomplete digital specifications during the handover process, since many firms still rely on physical files and documentation. Laser scanning technology is another source for data when the original engineering specifications are unavailable. A series of scans can be completed to convert physical assets into digital ones, helping to move forward your digital twin strategy.
Digital Lifecycle Continuity
While it’s critical to start operations with a rich digital model of the asset, it’s equally important to maintain the asset over time, updating the digital twin as the physical asset changes. In the absence of this data, the twin will gradually diverge and become out-of-date.
This challenge of digital continuity occurs throughout the asset life cycle from handover through the operational span of the project. This means the twin must be fed a constant flow of real-time information throughout the project. An effective digital twin will synthesize those information streams, embedding technology seamlessly into core business processes, and ensuring data is accessible and integrated with computer-based analysis.
How to Get Started
There are several considerations when pursuing a digital twin strategy.
Homegrown or commercial tools: Some enterprises are using homegrown solutions to manage some parts of their data and processes. This can become less sustainable as more advanced technology, like augmented and virtual reality, are more seamlessly plugged into commercial platforms. Companies that specialize in software can also manage maintenance, scalability and system compatibility.
Security and the cloud: Cloud adoption is steadily increasing, especially for large data storage needs where multiple partners require access. New security protocols are now helping to overcome this perceived threat.
Organizational and cultural change: The rise of the digital twin demands that companies put in place a robust digitalization strategy. This is as much a cultural and human process challenge as it is a technological one. It requires buy-in from leadership and a change in mindset across the organization to develop and embed data-centric ways of working.
A constantly-evolving digital twin enables industrial organizations to manage the continual change of complex, physical assets across every phase of the asset lifecycle. Manufacturers that prioritize a reliable digital twin strategy can achieve a holistic, data-centric organization and improve safety, reliability and profitability.
Rick Standish is Solution Strategy Vice President at Aveva, where he helps guide industrial and engineering companies with their digital transformation strategies.
