Digital Twins: Why They are Important

Nov. 23, 2025 /Mpelembe Media/ — What are Digital Twins?

A Digital Twin is a virtual replica of a physical object, system, or process that uses real-time data to accurately reflect its real-world counterpart’s behavior, performance, and conditions.

It creates a dynamic link between the physical world and the digital world, allowing for continuous monitoring, simulation, and analysis.

The Three Core Components

The Physical Asset (The Twin): This is the real-world object (e.g., a wind turbine, a factory floor, an entire city, or even a human organ). It is equipped with sensors (IoT devices) that capture data on its performance, condition, and environment.

The Digital Model (The Virtual Replica): This is the high-fidelity, virtual representation of the physical asset. It is built using the collected data and often incorporates physics-based simulation models to realistically mimic how the asset behaves under various conditions (e.g., how a bridge structure reacts to high winds).

The Communication Channel (The Feedback Loop): This is the continuous, two-way data flow.6 Real-time data from the physical asset updates the digital twin, ensuring its accuracy.7 Conversely, insights and optimal operating instructions derived from simulations on the digital twin can be sent back to control or optimize the physical asset.

Why They are Important

Digital twins are crucial because they enable organizations to:

Predictive Maintenance: Anticipate equipment failures before they occur, reducing downtime and maintenance costs.

Scenario Testing: Test “what-if” scenarios (e.g., extreme weather, production spikes, or system failure) safely in the virtual world without risking the actual physical asset.

Optimization: Continuously adjust operations to maximize efficiency, reduce energy usage, and lower costs.

The technological advancement of Digital Twins represents a journey from basic physical simulators to complex, intelligent, and interconnected autonomous systems. This evolution has been driven by the convergence of powerful computing, ubiquitous sensing, and advanced analytical software.

Stages of Technological Evolution

The concept has evolved through distinct stages, moving from simple offline models to dynamic, real-time cyber-physical systems.

1. Early Conceptualization & Physical Twins (1960s – Early 2000s)

Foundation: The earliest form of “twinning” was established by NASA during the Apollo missions in the 1960s. These were physical twins—exact, non-digital, duplicated systems built on Earth to mirror the spacecraft in space.

Milestone: During the Apollo 13 crisis, engineers used this physical replica and telemetry data to simulate solutions and guide the astronauts’ survival, proving the value of real-time mirroring.

Formal Theory: The modern, three-part concept (Physical Object, Virtual Model, and Data Link) was formally introduced by Dr. Michael Grieves in a 2002 presentation at the University of Michigan, though the term “Digital Twin” wasn’t widely adopted until later.

2. Synchronization & Real-Time Monitoring (2010s: The IoT Breakthrough)

This period marked the true commercialization of the Digital Twin.

IoT Integration: The proliferation of affordable Internet of Things (IoT) sensors was the single biggest enabler. These sensors could be embedded into nearly any physical asset, allowing for the continuous capture of real-time data on temperature, pressure, vibration, and performance.

Cloud Computing: The rise of Cloud Computing provided the necessary storage and scalable processing power to handle the vast volume of data streaming from millions of connected sensors.

Functionality: Digital Twins moved beyond just static 3D models to become dynamic monitoring tools. The twin synchronized (updated) with the physical asset in quasi-real-time.

Applications: Primarily used for predictive maintenance in manufacturing and industrial assets (e.g., wind turbines, factory machinery), allowing operators to anticipate failure and reduce downtime.

3. Intelligence, Federation, and Simulation (Mid-2010s – Present)

The current stage integrates advanced capabilities to create sophisticated, interconnected systems.

AI and Machine Learning (ML): Integrating ML algorithms allows the Digital Twin to become predictive and prescriptive.12 The twin can analyze historical data, detect patterns, predict future outcomes (e.g., how a component will degrade), and suggest optimal operational changes.

Federated Digital Twins: The technology expanded from modeling a single component (Component Twin) or a single machine (Asset Twin) to modeling entire systems (System Twin) and even networks of systems (Federated Digital Twins).

Example: Modeling an entire factory floor or a Smart City (e.g., Singapore’s digital twin) requires federating thousands of individual twins into one complex, interconnected virtual environment.

Advanced Simulation: Increased computational power allows for more detailed “what-if” simulations across these complex federated twins, enabling risk-free testing of operational changes or responses to major disruptive events.

Key Enabling Technologies

The advancement of Digital Twins is fundamentally a story of technological convergence:

Technology | Contribution to Digital Twin Advancement
IoT & Sensors | Provides the input data—the real-time stream that keeps the digital twin synchronized with its physical counterpart.
Cloud Computing | Provides the computational backbone and scalable storage required to process large datasets and run complex simulations.
Artificial Intelligence (AI/ML) | Provides the intelligence—the ability to learn from data, make accurate predictions, and recommend or execute optimizing actions.
Visualization & AR/VR | Provides the output interface, allowing humans to interact with and visualize complex data in immersive 3D environments (e.g., viewing a physical machine’s internal performance in AR).

The Future: Autonomous Digital Twins

The final stage of development is the Autonomous Digital Twin. In this phase, the system goes beyond simply recommending an action and moves toward self-governance.

Goal: The Digital Twin, coupled with an AI Agent, will autonomously recognize a problem, design a solution, and automatically send control signals back to the physical asset to execute the optimization or repair, with minimal human intervention.

Implication: This paves the way for the Autonomous Economy, where entire supply chains, energy grids, and manufacturing processes become self-optimizing and self-managing.

How Pulse Africa Utilized Digital Twins

Pulse Africa, a leading innovative media company in Sub-Saharan Africa, has adopted the concept of “Digital Twins” but applied it to a human-centric, creative-media context, rather than physical infrastructure like a bridge or a factory.

Pulse Africa’s use of digital twins is focused on extending their media reach and enhancing newsroom capacity, specifically through the creation of AI-generated journalist avatars.

Pulse Africa’s Application: AI-Powered Journalist Avatars

Pulse Africa utilized digital twin technology to create virtual replicas of their actual human journalists.

The “Twin” in this context is the Human Journalist. The physical asset being replicated is the voice, mannerisms, tone, and on-screen presence of a specific journalist.

The Digital Model is the AI-Powered Avatar.16 This is an AI model that can mimic the journalist’s style, voice, and on-screen look. This is a form of generative AI being used to create a digital counterpart of a person’s professional persona.

The Purpose and Implications

The primary goals and implications of this innovative application were:

Content Scalability and Speed: The AI twins can be used to generate content, conduct basic interviews, and translate content across languages at speed, expanding the newsroom’s capacity far beyond what human journalists could achieve alone. This is particularly valuable in a region with diverse languages and rapidly growing digital content demand.

Optimizing Editorial Workflows: The AI twins can handle routine or repetitive tasks (like reading a script or providing a quick news update), freeing up the human journalist to focus on complex, investigative journalism, and creative reporting.

Maintaining Trust and Ownership (The Ethical Component): Pulse Africa highlighted a crucial ethical safeguard: the journalists retain full ownership and control of their digital twin.19 If a journalist leaves the company, their digital twin cannot be used by the organization. This policy is vital for ensuring ethical AI use and maintaining the integrity and professional identity of the human staff.

In short, while most industries use digital twins to optimize things (like machines or grids), Pulse Africa used the concept to create a digital extension of their key people, leveraging AI to scale creativity and news delivery while prioritizing human control and ownership.