Digital Twins Of Industrial Assets Will Revolutionize Risk Management
The first time anyone hears the phrase “digital twins” they wonder whether this is a new sci-fi film featuring Michael Fassbender as a cyborg called ‘David’. In fact, digital twins are an evolution of product lifecycle management (PLM) software which, through a logical model of a product such as a motorbike, enables product designers to manipulate and visualize how the parts fit together. We’ve all seen the 3D video clips of a car or motorbike being deconstructed and reassembled in a digital mock-up. PLM software does much more than flashy 3D images of the motorbike. Firstly, the underlying model contains relationships about interdependencies between parts. Secondly, the software can simulate the performance of the motorbike, for instance, for aerodynamics. Thirdly, the software allows designers to change design parameters such as materials and shapes.
Making the leap from PLM for products to digital twins for entire factories, vendors like SAP and Siemens have realized that they can bring together real-time data from industrial operations – for instance a digital flow meter or the location of a worker via a tracking wearable – with modelling software to create a ‘digital twin’ simulation of an industrial asset. This digital model of, for instance an automotive manufacturing plant, can be used to monitor production in real-time and can also be used to predict performance based on the real-time data that has been collected. The objective is to enhance every aspect of a plant’s performance: productivity, quality, efficiency, safety. This has led to the launch of digital factory concepts. Amongst the systems integrators, Accenture has been most heavily involved in designing the information architecture for digital factories.
The concept of the digital twin of a plant has immediate value for risk owners at industrial assets. Data can be collected in real-time from safety critical systems, from workers undertaking safety observations with mobile apps and from maintenance scheduling systems. The digital twin software can then combine these data streams to produce a risk model which captures the interdependencies of the plant’s operations in real time. A dynamic risk model – based on real-time activities – enables plant operators to monitor risk controls, predict hazards and make decisions that ensure the health of barriers. This level of prediction and control has not been possible prior to the development of digital twins which pull real-time information from sensor networks and other plant operations systems.
Unlike Aliens, the use of digital twins to improve plant performance and risk management is not science fiction. Equinor (previously Statoil) has developed a digital twin for its Johan Sverdrup offshore oilfield. SK Energy has implemented the Siemens XHQ software to create a Visualized Operations Intelligence System. Petra Data Science has built a digital twin called MAXTA for mining value chain optimization. Like any new technology, the digital twin concept needs 20 years to fully mature. But there is true potential to revolutionize industrial risk management starting today. To learn more about investment plans for operational risk management register for the forthcoming Verdantix webinar “Operational Risk Management: Budgets, Preferences And Priorities For 2019” taking place on September 20th.