Digital Twins: A New Paradigm for Operations

The Digital Twin concept, particularly in industries and operations like manufacturing, was introduced by Dr. Michael Grieves at the University of Michigan in 2002. Since then, it has evolved significantly, expanding from individual systems or equipment to processes and now enterprise-wide digital twins. Despite the various definitions available across industries, ranging from digital twins for building design and building management systems (BMS) to medical applications and power grids, a fundamental question remains: “What is a Digital Twin? and what value does it deliver to businesses?”

This article explores the fundamental concept of digital twins, their applications across multiple sectors, and the value they bring to businesses.

Fundamental of the Digital Twin Concept

The industry categorizes three digital twins: the Digital Twin for equipment or system, the Digital Twin of a process, and the Enterprise Digital Twin.

• Digital Twin for an Equipment or a System: Imagine a digital twin for an MRI machine or an air handling unit (AHU) in a building. In this context, the digital twin serves as a virtual model of the equipment or system, primarily used for predictive maintenance and performance optimisation. For instance, it can monitor the operational efficiency of the MRI machine to foresee potential failures and schedule maintenance proactively.
• Digital Twin of a Process: Think about the queuing process at a retail store’s checkout counter or the security screening at an airport. In these scenarios, a digital twin acts as a digital model of the process aimed at optimising and improving efficiency. For example, it can analyse queue times and staff allocation to minimize wait times and boost customer satisfaction.
• Enterprise Digital Twin: This concept continuously evolves, aiming to create a comprehensive digital model of an entire organization. An Enterprise Digital Twin integrates multiple digital twins of equipment, systems, and processes throughout the enterprise, enabling holistic optimisation of business operations.

 Characterizing Digital Twins: Key Attributes

A digital twin is distinguished by four fundamental attributes that define its comprehensive capabilities and applications:

• Digital Model of an Equipment, System, or Process: At its core, a digital twin comprises a precise digital model that represents the physical characteristics and behaviour of a specific equipment, system, or process. This model serves as a virtual counterpart, mirroring real-world assets and processes.
•  Application of the Digital Model for Advanced Analytics: The digital model can be leveraged to build advanced analytics to gain insights from the associated data. This includes trend analysis, predictions or forecasting, pattern recognition, and anomaly detection. By applying these analytical techniques, organizations can uncover valuable insights, anticipate future conditions, and make data-driven decisions to enhance operational efficiency and reliability.

 The diagram below illustrates these four characteristics:

• Application of the Digital Model for Simulations: Another critical attribute of a digital twin is its capability to conduct simulations. These simulations enable the exploration of various "what-if" scenarios, allowing stakeholders to evaluate the potential outcomes of different actions and decisions. By simulating diverse conditions and operational strategies, organizations can optimise processes, mitigate risks, and improve overall performance.
•  Visualization of the Digital Twin: Effective visualization is essential for comprehending and interacting with the digital twin. This involves building a birds-eye view that renders the digital model in 2D or 3D visualizations, providing intuitive and detailed views of the equipment, system, or process. Such visualizations enhance understanding, improve user experience, support decision-making, and facilitate stakeholder communication by offering a clear and interactive representation of the digital twin.

 Technology driving the digital twin

Digital twins have become increasingly sophisticated by integrating state-of-the-art technologies such as artificial intelligence, machine learning, IoT, and big data. This advancement leads to deeper insights and enhanced accuracy in predictions and simulations. As industries adopt and refine digital twin technology, we can expect continuous improvements and greater efficiency in the future.

• IoT – Internet of Things: These are essential components that gather data from various physical systems. Sensors and devices within the IoT ecosystem continuously monitor and collect data, providing real-time insights into the functioning of different systems.
• API and Open Standards: These provide the necessary standards and tools to extract, share, and harmonize data from multiple sources. APIs (Application Programming Interfaces) enable seamless data exchange between systems, while open standards ensure compatibility and interoperability across diverse platforms.
• Cloud Computing: By utilising cloud infrastructure, data can be processed, organized, and made readily available for consumption by upper layers, such as analytics and application services.

 The diagram below illustrates these five technologies:

• AI – Artificial Intelligence: AI processes the collected data to provide holistic situational awareness and predictive insights. By leveraging models and algorithms, AI can analyse patterns, make predictions, and deliver actionable intelligence to enhance decision-making.
• VR/AR – Virtual Reality / Augmented Reality: These technologies render spatial models and provide visualization of the digital twin. VR and AR create immersive and interactive experiences, allowing users to visualize and interact with digital twins in a more intuitive and engaging manner.

Application of Digital Twin across multiple industries or sectors

Digital twins are the core of Industry 4.0, transforming how we operate and deliver value in our industries/sectors. Some of the examples of Digital Twins applications across several industries are as follows:

• Manufacturing: Digital twin is used for predictive maintenance -sensors on the machinery collect real-time data, which is fed into the digital twin. It allows for predicting equipment failures before they happen, reducing downtime and maintenance costs.
• Aerospace: Digital twin is used for the design and testing phase. A digital twin of an aircraft engine can simulate various conditions and stress tests. It helps engineers optimise the design for performance and durability without needing physical prototypes.
•  Airports: Digital twin is used for passenger flow management. Digital twins help optimise passenger flow, reduce waiting times, and enhance traveller experience by tracking real-time passenger movements using non-intrusive technology.
• Healthcare: Digital twin is used for performance optimisation in MR machines. By analysing the data, the digital twin can suggest adjustments to optimise the MRI machine's performance. This includes fine-tuning operational parameters to improve image quality and reduce scan times, enhancing the machine's overall efficiency.
• Energy: A digital twin of the power grid can help balance supply and demand. It can simulate various scenarios, such as peak load conditions or integration of renewable energy sources, to ensure a stable and reliable electricity supply.
• Smart Cities: Digital twin is used for traffic management. Real-time data from traffic cameras, sensors, and GPS devices can be fed into the digital twin to optimise traffic flow. It can suggest adjustments to traffic signals, reroute vehicles to avoid congestion, and improve public transportation schedules, reducing travel times and emissions.

Digital Twin for Airport Operations

When we step into a bustling airport, we are immersed in a world of constant activity—passengers hurrying to their gates, ground staff coordinating logistics, baggage being handled meticulously, and planes preparing for landing and take-off. Do you know there are at least 30 stakeholders involved in airport operations, over 20 passenger touchpoints, and numerous interconnected systems at airports? Have you ever wondered how this complex system operates and what can be done to make it even more seamless?

Envision a groundbreaking solution that not only reveals the number of passengers at security gates but also predicts wait times and offers detailed insights into aircraft turnaround and baggage processing. It's like having a real-time, all-seeing, all-knowing companion for seamless airport operations! This isn't just a vision; it's the reality enabled by Digital Twin technology.

Stay tuned for our next article to explore the world of airport operations powered by Digital Twins.