Companies have high expectations for Artificial Intelligence (AI) and Machine Learning (ML), but still a few have so far managed to successfully implement them into industrial and business processes. A leap forward might come with the Digital Twin: this emerging technology integrates the Internet of Things, AI, ML and advanced data analytics to generate dynamic simulation models of physical objects.
Digital Twin can typically be used for asset monitoring, supporting diagnostics and prognostics to optimize maintenance procedures, as well as equipment performance and utilization. Combined with existing or new Enterprise Asset Management (EAM) solutions, Digital Twins allow companies not only to improve operational efficiency but also to better identify vulnerabilities and risks, making evidence-based, forward-looking decisions about assets and plants.
Beyond operations, the business benefit is clear: by leveraging the information generated by Digital Twin, the organisation can adjust its investment plans, allocating money where it is really needed, and where it could add value to the company and its customers.
To start implementing a Digital Twin model in an industrial facility, it is usually necessary to create a high-resolution data view of all the equipment to be monitored and analysed. Model components should include single devices or assets, systems of assets, complex systems and all related processes. This step might be neither quick, not easy, but it is ground work to ensure a reliable monitoring and enable condition-based, predictive maintenance.
After the pilot phase, the company might want to extend the use of Digital Twin and achieve the business goals we’ve mentioned. How to do it? It would need to implement of a comprehensive model of all operations, to be used to guide investments in new and existing equipment, technology, and instrumentation – the idea is to understand where the budget should be directed to get the highest possible ROI.
This is a major difference between Digital Twin and AI. While AI algorithms require a given data structure and a context to perform, Digital Twin can manage to identify a problem within a process or a business without a specific background knowledge.
In large manufacturing companies, we use to see senior people and their teams spending time to collect and check data, analyse them and unlock their potential value for decision processes. With the Digital Twin, this might be over as it allows companies to move one step forward AI, taking EAM to the next level.