Understanding the key characteristics & outcomes of a digital twin

In our previous blog in this series, we examined a real-life example of where a digital twin helped drive outcomes for an organisation and the overarching importance of digital twins amidst the ever-changing technological landscape. Today, we’ll explore the characteristics comprising digital twins, including their advantages, challenges and what organisations can expect from them. 

What are the key characteristics of a digital twin? 

A digital twin, in its most basic form, is a virtual representation of a physical entity or group of entities, such as the machines and their systems on a manufacturing shop floor. However, in the context of organisations, digital twins go beyond simply replicating physical assets. They represent the entire organisational structure, including processes, workflows, systems and even human behaviours. Some of the key characteristics of a digital twin include: 

Real-time data integration (H3) 

• Dynamic and continuous synchronisation: A digital twin constantly updates its virtual model based on data from its physical counterpart or the processes it represents. This real-time integration allows the twin to accurately reflect the current state of the system, asset or organisation it models.   
• Data sources: It incorporates data from various sources, including IoT sensors, enterprise systems, operational data stores and external data feeds, ensuring a comprehensive and up-to-date virtual representation.   

High fidelity and accuracy

• Detailed and precise representation: A digital twin provides a high-fidelity model that captures the complexities and nuances of its subject. This includes both physical characteristics (e.g. dimensions and materials) and operational parameters (e.g. performance metrics and environmental conditions).   
• Scalability: The accuracy of a digital twin can scale from a single asset (e.g. a machine) to complex systems (e.g. an entire manufacturing plant or organisational process, including its external factors).   

Two-way interaction 

• Bidirectional communication: A digital twin supports two-way communication, allowing not only the updating of the virtual model based on physical world changes, but also enabling the virtual model to influence its real-world counterpart. For instance, adjustments made in the virtual model can be implemented in the real-world system.   
• Predictive and prescriptive capabilities: Beyond mere replication, a digital twin can predict future states and prescribe actions based on simulations, scenario analysis or machine learning algorithms.   

Comprehensive lifecycle representation

• Lifecycle coverage: A digital twin spans the entire lifecycle of the system, organisation or asset it represents, from design and development through to operation, maintenance and even decommissioning. This ensures that insights can be derived at any stage, supporting continuous improvement and adaptation.   
• Change management: It adapts to changes in the physical environment, evolving over time as the real-world counterpart undergoes modifications, whether in design, operation or environment.   

Simulation and scenario analysis 

• What-if scenarios: A digital twin enables the simulation of various scenarios and potential changes before they are implemented in the physical world. This includes testing new designs, operational strategies or responses to hypothetical events, all within a risk-free virtual environment.   
• Optimisation: By analysing different scenarios, the digital twin helps in optimising performance, reducing costs, improving efficiency and enhancing risk mitigation.   

Advanced analytics and machine learning  

• Data-driven insights: A digital twin leverages advanced analytics, including predictive modelling, machine learning and AI to extract meaningful insights from the vast amounts of data it processes. This allows organisations to predict outcomes, prevent failures and optimise operations.     
• Learning capability: The digital twin can “learn” from the data it receives, continuously improving its accuracy and predictive capabilities over time.   

It’s important to note, however, a digital twin can still function effectively and add value without ML and AI, instead relying on real-time data integration, simulation and rule-based systems, until enough data is generated to create ML models.   

Contextual awareness 

• Environment and ecosystem awareness: A digital twin understands the context in which the physical asset, organisation or process operates, including its environment, external influences and interdependencies with other systems, enhancing the relevance and precision of the insights generated.   

Interoperability and integration 

• Seamless integration: Digital twins are designed to integrate seamlessly with other digital systems, tools and platforms within an organisation. This interoperability ensures that the digital twin can act as a central hub for data and insights, interacting with various enterprise systems like ERP, CRM and PLM.   
• Modularity and scalability: The architecture of a digital twin should allow it to be modular, enabling different components to be updated, replaced or scaled independently, which is critical for adapting to evolving organisational needs.   

Visualisation and user interaction 

• User-friendly interface: A digital twin often includes advanced visualisation tools such as 2D & 3D models, dashboards or even augmented reality (AR) interfaces, simplifying users’ interactions and interpretations of the virtual model. The use of these depends on the need, however.   
• Interactive decision support: Users can interact with the digital twin to perform analyses, run simulations and explore different operational strategies, all through an intuitive and accessible interface.   

Security and compliance   

• Data security: Given that a digital twin deals with real-time and potentially sensitive data, robust security measures are a fundamental characteristic. This includes data encryption, secure communication protocols and compliance with industry standards and regulations.   
• Governance and compliance: Digital twins must adhere to governance frameworks and compliance requirements, ensuring that the data and operations they manage meet regulatory and ethical standards.   

What are the advantages of digital twins for organisations? 

Proactive maintenance  

The system sent automatic notifications when machines required attention, whether due to routine maintenance, in response to a negative trend or as a response to an unexpected incident. This minimised downtime and ensured continuous production with a higher utilisation rate. 

Trend analysis 

The digital model tracked stats over time, allowing for trend analysis. This feature was invaluable in predicting when a machine might require more significant intervention or identifying when a production line was consistently underperforming.  

Quality assurance  

By integrating the testing processes into the digital twin, the system provided real-time feedback on the quality of the fire detectors being produced. Engineers could react quickly to any deviations, ensuring that only high-quality products left the facility.    

Enhanced decision-making

Digital twins provide a comprehensive view of organisational operations, enabling decision-makers to visualise the impact of changes before they are implemented. This leads to more informed and strategic decisions, reducing risks and improving outcomes.   

Operational efficiency 

By simulating processes and workflows, organisations can identify inefficiencies and bottlenecks in real-time, allowing for continuous optimisation and therefore improved productivity, reduced costs and agility to change.   

Predictive maintenance and risk management  

Digital twins can predict potential failures or risks by analysing data trends and patterns, minimising downtime, preventing costly disruptions and enhancing resilience.   

Scalability and flexibility 

Organisations can use digital twins to model and test new business strategies, products or services without disrupting existing operations, enabling businesses to innovate and adapt to changing market conditions with minimal risk.   

Employee and resource optimisation  

By simulating human behaviours and interactions within the organisation, digital twins can optimise resource allocation, improve workforce planning and enhance employee engagement.   

What challenges arise when creating digital twins? 

Complexity and customisation  

Developing a digital twin for an organisation is inherently complex due to the need to capture and integrate diverse data sources, processes and systems. Additionally, each organisation has unique requirements, complicating the creation of a one-size-fits-all solution.   

Data integration and quality  

A digital twin’s accuracy and effectiveness depends on the quality and integration of data. Inconsistent, incomplete or siloed data can compromise its ability to provide reliable insights, leading to suboptimal decision-making.   

Scalability of platforms    

Most existing platforms for creating digital twins are rigid and domain-specific, limiting their applicability across different industries or organisational needs and potentially hindering organisations from fully leveraging the potential of digital twins.   

High development costs and time

The process of designing, developing and deploying a digital twin is often time-consuming and expensive. This can be a significant barrier for organisations, particularly those with limited resources.  

How Mood can help 

For organisations lacking the confidence to build their own digital twin from scratch, Mood consultants work directly with customers to equip them with the necessary skills to progress towards an agile, data-driven future. Contact Mood today to begin your journey. 

Stay tuned for the next blog in this three-part series, where we’ll explore the unique approach to digital twins offered by Mood and how organisations that leverage Mood’s capabilities can enhance their digital twin experience.