Processes refer to the series of actions, operations, or workflows that are carried out to achieve a specific outcome. These can include manufacturing workflows, supply chain operations, maintenance routines, or any other systematic activities. A Process Digital Twin is a virtual representation of these processes, capturing their dynamics in real-time and enabling simulation, monitoring, and optimization. It goes beyond individual assets to focus on how components, systems, and workflows interact within a larger operational framework.

Key concepts

Processes are integral to the operation of physical assets, as they define how these assets function within larger systems. Digital twins of processes provide functional solutions by enabling real-time monitoring, predictive maintenance, simulation capabilities, end-to-end integration, improved decision-making, collaboration across teams, sustainability improvements, and risk mitigation. By focusing on workflows and interactions rather than isolated components, process digital twins ensure that physical assets operate efficiently within their broader operational context. This makes them indispensable for achieving operational excellence in industries such as manufacturing, logistics, energy production, and infrastructure management.

Digital twins of processes enable organizations to optimize the performance of physical assets by focusing on the workflows and interactions that govern their operation.

Mechanisms

Real-Time Monitoring and Optimization

Process digital twins provide a live view of how physical assets operate within a larger system:

For example, in a factory, they can monitor how machines interact across production lines.

In supply chains, they track logistics operations like shipping routes and inventory movements.

This allows organizations to identify inefficiencies or bottlenecks in real-time and make adjustments to improve performance.

Simulation and Scenario Testing

Process digital twins enable organizations to simulate various scenarios to predict outcomes before implementing changes physically:

For instance, manufacturers can test different production schedules or resource allocations to determine the most efficient workflow.

In infrastructure projects, simulations can evaluate how construction processes impact timelines and costs.

This reduces risks and ensures that changes are optimized for efficiency and effectiveness.

Predictive Maintenance Across Workflows

By analysing data from interconnected processes, digital twins help predict potential failures or disruptions:

For example, if a machine in a production line shows signs of wear, the process twin can anticipate how its failure might affect downstream operations.

Maintenance schedules can then be adjusted proactively to minimize downtime across the entire system.

This ensures that physical assets remain functional within their operational context.

End-to-End Process Integration

Process digital twins provide a holistic view of how different assets and systems interact:

In manufacturing, they integrate data from machines, workers, and supply chains to ensure seamless operations.

In energy production, they model how power plants interact with grid systems and consumer demand.

This integration helps optimize workflows across multiple touchpoints.

Enhanced Decision-Making

Process digital twins provide actionable insights by combining real-time data with predictive analytics:

For example, they can recommend changes to improve production speed without compromising quality.

Supply chain managers can use them to adjust routes dynamically based on traffic or weather conditions.

These insights enable faster and more informed decision-making.

Improved Collaboration Across Teams

Process digital twins create a shared virtual environment where teams can collaborate:

Engineers, operators, and planners can work together using the same data-driven model of processes.

For instance, they can jointly analyse how changes in one part of a workflow affect other parts.

This fosters alignment and reduces miscommunication.

Sustainability and Waste Reduction

By optimizing processes, digital twins help reduce waste and energy consumption:

For example, they can identify inefficiencies in material usage during manufacturing.

They also enable better planning for recycling or reuse at the end of an asset's lifecycle.

This supports sustainability goals while improving cost efficiency.

Risk Mitigation

Process digital twins simulate potential disruptions to identify vulnerabilities:

For instance, they can model how supply chain delays impact production timelines.

They also help organizations prepare for emergencies like equipment failures or natural disasters by testing contingency plans virtually.

This proactive approach minimizes risks to physical assets.

References

[1] https://www.ibm.com/think/topics/what-is-a-digital-twin

[2] https://www.eurostep.com/digital-twin-key-concepts-and-benefits-for-asset-management/

[3] https://info.microsoft.com/rs/157-GQE-382/images/Digital Twin Vision.pdf

[4] https://www.centricsoftware.com/blog/what-is-a-digital-twin/

[5] https://aws.amazon.com/what-is/digital-twin/

[6] https://en.wikipedia.org/wiki/Digital_twin

[7] https://www.sw.siemens.com/en-US/technology/digital-twin/

[8] https://www.amrc.co.uk/files/document/406/1605271035_1604658922_AMRC_Digital_Twin_AW.pdf

[9] https://www.sogelink.com/en/innovation-2/digital-twin-explained/

[10] https://awortmann.github.io/research/digital_twin_definitions/ [

11] https://www.aveva.com/en/solutions/digital-transformation/digital-twin/

[12] https://www.twi-global.com/technical-knowledge/faqs/what-is-digital-twin