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Twenty years ago, digital twins were nothing more than an idea, conceptualized with the hope of driving efficiency in product lifecycles. Since then, they have become a reality, moving up the business agenda and being more commonplace in many organizations worldwide. Digital Twins now enable organizations to have a full digital replica view of their systems and processes to see what they’re trying to achieve and how they can get there sooner and more efficiently.
Digital twins allow businesses to trial new processes, run hypothetical scenarios, and identify inefficiencies across their entire value chain while also ensuring that the physical system is free from disruption. They offer the perfect solution for smoothing out any crinkles - or ‘frictions’ - across an enterprise's business operations. Additionally, they provide complete real-time visibility for departments to monitor, ultimately enabling an organization to look for strategic issues, because that’s the quickest way to see results.
Building a clear roadmap for the future
Digital twins are becoming more prominent than ever, with organizations set to increase deployment by 36% over the next five years. Today, they are being used beyond hardware, and are supporting key business functions such as finance, accounting, human resources, and supply chain management teams. It’s critical that organizations start thinking about how the capabilities of digital twins can deliver greater visibility of operations, more advanced monitoring, and simulations of events, all of which can improve data-based decision making to significantly impact future success.
But getting to this point means first developing a strong, well-defined, future-proof vision that is backed by managerial commitment. This is essential to building a clear roadmap that ensures the necessary resources, manpower and funding are allocated appropriately. So, starting small can be a good idea; defining a limited number of use cases that have the highest potential value and see them through to completion. After all - the test model also needs to be tested.
Scaling the network effectively
Like any AI-based system, digital twins need to have a clean structured data source to mine, model, simulate, predict and monitor effectively. Data quality is critical. It’s not uncommon to see larger organizations dealing with a mass of data sources that need to be integrated throughout their ecosystem. Scaling digital twins to navigate this complexity makes for an interesting challenge.
To overcome this and to ensure that the relevant data is accessible and accurate, it’s essential there is buy-in from external partners and that they are fully embedded into the process from the start. Organizations should also look to become masters of data-sharing and be part of a wider data ecosystem across their supplier and partner network. The quality of data is incredibly important in successful implementation - inconsistent or incomplete data would increase the likelihood of low-quality outputs. However, with the right strategy behind the use of digital twins, i-t’s possible to qualify the right data, understand how it flows across systems and processes, and see how it can be turned into actionable insights.
Collaboration can play an essential role in the design and development of digital twins. Partners or consortiums can help a business tap into best practices and standard requirements, aiding with the development of resources and guidelines that can further facilitate adoption.
Securing the foundation
While digital twins are more of a hypothetical model, by their very nature they contain highly sensitive information unique to a particular system. So, it should come as no surprise that security is paramount. The entire data lifecycle – from collection, enrichment, maintenance, use, archiving, and disposal – needs protection from cyberattack and violations of privacy rights. Any hack or breach of an unsecured digital twin can allow perpetrators to immediately infiltrate the system and gain access to the internal data of the entire business.
Our research shows that 69% of businesses are planning to make major changes to their end-to-end cybersecurity to accommodate digital twins. That said, enhancing an organization’s cybersecurity resilience requires a specific skill set. Whether that comes from new talent, upskilling existing employees, or outsourcing to specialist partners, it is imperative that the communication between the twin and its physical counterpart is protected.
Reconciling profitability and sustainability
Digital twins play a critical role in reconciling profitability and sustainability; they allow for experimentation and the opportunity to assess the impact of each decision without real-world risks. Because you have repeatedly practiced scenarios in a virtual environment, the likelihood of failure is considerably reduced. This gives organizations the confidence to adapt, implement change and move quickly because they have anticipated the results of their actions. Our research shows that 57% of organizations agree that one of the key drivers behind their digital twin investment is improving sustainability efforts. Of those who have implemented digital twins, 16% of the subsequent progress made in the area of sustainability was considered a result of using digital twins. Sustainability has a powerful impact on a business’ growth as it can improve customer loyalty, mitigate regulatory risks, and ultimately help navigate their journey towards net zero. Digital twins can positively impact profit and turnover, prepare any business to minimize their environmental impact from production through to delivery of products/services. This can further give them an edge over others.
Take for example, smart cities. A digital twin of the city continuously gathers data from the built environment using technologies like sensors, drones, or mobile devices, which includes monitoring the carbon emissions produced throughout the area. By understanding the carbon intensity of various elements within the city, sustainability can become a core component in the adaptation of existing structures and the creation of new infrastructure. In a project with Siemens and a German city of around 200,000 residents, digital twin modelling of the city’s infrastructure and energy demand found that a 70% reduction in emissions by 2035 would be completely feasible. The technology can play a vital role in understanding crucial facets of smart cities, such as cost-minimized energy systems, utilities and electric vehicle charging stations, as demonstrated by Siemen’s work in Germany. As the realization of sustainability goals increasingly centers around ‘how’ rather than ‘if’, digital twins will play a key role in the decarbonization strategy of both the public and private sector.
Digital twins allow for organizations and businesses to expand their capabilities, providing a full-scale simulation for organizations to follow. However, that simulation needs to be developed and built in the right way to ensure it is accurate and can adapt to external change.
Digital twins will afford an organization greater agility as circumstances evolve. They can act as a critical component for any business looking to optimize workflows, exceed customer expectations, deliver sustainable value and reduce overall costs. With the ability to empower organizations to pre-empt challenges that lie ahead, digital twins could be the answer to some of the most pressing concerns in business today. A brilliant technology that was simply an idea twenty years ago, is now here, ready to unlock new levels of progress and success for organizations around the world.