• Written by Azhar Aliyar, Director

A digital twin is a virtual replica of an asset or process that is kept in step with reality, often confused with a simulation model, but distinct in its purpose and application. This confusion arises because both digital twins and simulation models are computer-based representations of physical systems, but they serve different purposes and are used in different contexts. A digital twin is a model of an asset or process that is updated in real-time to reflect the current state of the physical system, whereas a simulation model is a static representation of a system used to predict its behavior under various scenarios.

Distinguishing from simulation models

The main difference between a digital twin and a simulation model is the level of detail and the purpose of the model. A simulation model is typically used to analyze the behavior of a system under various conditions, such as different operating scenarios or failure modes, and is often used in the design and testing phases of a project. In contrast, a digital twin is used to monitor and control the operation of an asset or process in real-time, and is typically used in the operational phase of a project. For example, a digital twin of a manufacturing process might be used to monitor the production line and detect any anomalies or deviations from the expected behavior, whereas a simulation model of the same process might be used to optimize the production line design or to test different operating scenarios.

Another key difference between digital twins and simulation models is the level of integration with the physical system. A digital twin is typically integrated with the physical system through sensors and other data sources, and is updated in real-time to reflect the current state of the system. In contrast, a simulation model is typically a standalone model that is run independently of the physical system, and may not be updated in real-time.

Applications in large organisations

Digital twins have a wide range of applications in large organisations, particularly in industries such as manufacturing, energy, and transportation. For example, a digital twin of a production line might be used to monitor and control the operation of the line, and to detect any anomalies or deviations from the expected behavior. A digital twin of a building might be used to monitor and control the energy usage and environmental conditions, and to optimize the operation of the building systems. In the energy industry, digital twins of power plants or transmission systems might be used to monitor and control the operation of the systems, and to detect any anomalies or deviations from the expected behavior.

In a large organisation, digital twins can be used to improve the efficiency and effectiveness of operations, and to reduce costs and improve profitability. For example, a digital twin of a supply chain might be used to monitor and control the flow of goods and materials, and to detect any anomalies or deviations from the expected behavior. A digital twin of a logistics system might be used to monitor and control the movement of goods and materials, and to optimize the operation of the system.

What digital twins do not do

Despite the many benefits of digital twins, there are some common misconceptions about what they can do. One common misconception is that digital twins can be used to replace human operators or to automate decision-making. While digital twins can provide valuable insights and data, they are not a replacement for human judgment and decision-making. In fact, digital twins are typically used to support and augment human decision-making, rather than to replace it.

Another common misconception is that digital twins are a one-time investment, and that they can be implemented and forgotten. In reality, digital twins require ongoing maintenance and updates to ensure that they remain accurate and effective. This can include updating the model to reflect changes in the physical system, as well as integrating new data sources and sensors. For more information on how to implement and maintain a digital twin, please visit our website.

When digital twins are the wrong choice

While digital twins can be a powerful tool for improving the efficiency and effectiveness of operations, they are not always the right choice. In some cases, the cost and complexity of implementing a digital twin may outweigh the benefits, particularly for small or simple systems. In other cases, the data and sensors required to support a digital twin may not be available, or may be too expensive to implement. For example, a small manufacturing company may not have the resources or budget to implement a digital twin of their production line, and may need to consider alternative solutions.

In these cases, alternative solutions such as simulation models or data analytics may be more appropriate. For example, a simulation model of a production line might be used to analyze the behavior of the line under different operating scenarios, and to identify opportunities for improvement. A data analytics system might be used to analyze data from sensors and other sources, and to provide insights and recommendations for improvement. If you are considering implementing a digital twin, but are unsure if it is the right choice for your organisation, please contact us to discuss your options.