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A Digital Twin is a means to an end: to valorise data and make efficient data-driven decisions. It helps companies to gain better insights and supports organisations in the evolution towards a data-driven enterprise.

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The origin of the Digital Twin concept dates back to the period of NASA space travel. In April 1970, an oxygen tank from the Apollo 13 flight exploded, leaving NASA in need of simulating possible solutions. It was 15 huge simulators on Earth, which NASA engineers got to work with, that eventually offered a solution. Thanks to the technological progress in the amountof data, computational power and accuracy with which data can be processed, the concept of a Digital Twin is beginning to take root in various sectors. In recent years, technological progress in those areas have been tremendous, allowing data to be valorised in a Digital Twin to support business decisions. As a result, the Digital Twin concept is able to accelerate the business transformation towards a data driven organization. The benefits it offers organisations range from increased efficiency and lowering the environmental impact, to greater reliability and cost savings.

There are different definitions of a Digital Twin. The Digital Twin Consortium has determined a consolidated definition based on input from the various members from multiple industries:

• ‍A digital twin is a virtual representation of entities and processes from the physical world, synchronised with a certain frequency and reliability.
• ‍Digital Twin systems transform businesses by accelerating a holistic view, optimal decision-making and effective actions.
• Digital twins use real-time and historical data to represent the past and present, and simulate the future using various potential scenarios.
• Digital twins are motivated by results, aligned with use cases, enabled by integration, built on data, guided by domain knowledge and implemented in IT/OT systems.

And as Capgemini also points out, a Digital Twin is more than a simulation, a model or a graphical interface. There are a number of key characteristics of a Digital Twin:

• The existence of a physical product, system or processon on which the digital counterpart is based
• Connectivity and a flow of information between physical and digital entities
• The ability of the virtual entity to store and track data through a network or system
• Periodic or near-real-time synchronisation of the states of the physical and virtual twins
• The ability of the virtual twin to simulate and influence bi-directionally the physical entity, its characteristics and its performance levels.
• The ability of the virtual twin to predict the characteristics of its physical counterpart and simulate characteristics to make it more efficient
• The ability of the virtual twin to monitor, maintain, optimise and bi-directionally influence the operations of the physical twin.

This definition and characteristic indicate that a Digital Twin helps companies make decisions based on all available data. Therefore, the concept should be seen as a methodology that has a fundamental impact on how companies make decisions and supports them in the evolution towards a data-driven company. Focusing only on the technological part is insufficient, as a qualitative decision consists of a number of parameters: data x analytics x IT x people x processes. The decision value chain for qualitative decisions consists of a number of technical parameters(data, analytics and IT) and a number of business parameters (people & processes). 

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Only when all these different parameters are addressed and implemented can a Digital Twin act as a catalyst for profitability and sustainability. The system acts as a platform to simulate different scenarios. This helps to assess the impact of decision without real risks, thus positively impacting business results. This ensures effective, efficient decision-making and the system helps to create a shared understanding among various stakeholders thereby maximising profitability, maintaining reliability and improving sustainability.

With digital twins, organisations can improve a number of KPIs such as cost, operational efficiency, lead times and sustainability. A study by Capgemini shows that organisations realised an average 13% cost reduction across Digital Twins use cases and a 15% increase in operational efficiency. 

The benefits of a Digital Twin for organisations extend beyond process improvement, and can result in a number of valuable cases:

• A continuous data flow that perfectly synchronises the Digital Twin with business processes
• More sophisticated monitoring that improves follow-up and isolates key data to support root case analysis
• A digital counterpart where, through forecasting, the organisation can simulate impact analysis (e.g. Predictive maintenance)
• An active feedback loop between strategy and execution that provides new evidence for performance management
• Predictions of events affecting the business that can lead to SLA optimisations
• Creation of an AI playground by collecting training data used to feed a number of AI algorithms.

As the idea of Digital Twins becomes established, it becomes clear that their use can extend to almost anything, from health solution for people to efficiency solutions for production lines. While Digital Twins were originally developed mainly for hardware and products, we can now just as easily develop Digital Twins for maintenance processes, manufacturing processes, buildings, department stores, human resources and supply chain management. This opens the door to Digital Twins of entire organisations and promises exponential benefits such as greater transparency of business operations, more advanced monitoring and forecasting using integrated AI simulations. The possibilities are truly endless - and we are currently at the beginning of a promising transformation.

The methodology of D-Cide aims to support companies in this transformation towards a data driven organization by providing support in defining both the business and technical parameters of a quality data-driven decision(data x analytics x IT x people x processes).

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