May 23, 2023 . 5:35pm

Evolution of Digital Twins

Evolution of Digital Twins

We are living in an era where technology is not just evolving, but accelerating at an unimaginable pace. It's like standing on the beach, marveling at the waves, only to realize that the tide is not just coming in - it's rushing forward, transforming the landscape with relentless momentum. One such wave is the concept of Digital Twins. Not too long ago, the term 'Digital Twin' was a novel idea, a cutting-edge concept that promised to revolutionize industries by providing a dynamic digital mirror of physical systems. Today, this innovation has evolved far beyond its original premise. It has matured, split, and grown into a nuanced ecosystem, now comprising three distinct evolutionary levels - each more advanced and capable than the last. In this post, we will outline three levels, starting from the reactive Digital Twin to the proactive Intelligent Digital Twin; we'll uncover the distinct capabilities, business values, and implementation considerations of each level.

Level 1: Digital Twin

The first level, often called the 'Digital Twin,' mirrors physical system and its context into a digital realm. It collects and processes data, utilizing this information for modeling. This concept sprung from the need to have a digital representation of physical systems for better management and control.

The mirroring digital twin of the physical systems is valuable in the space you known has inefficiencies and you know what inefficiencies are.

Consider the example of a globally operating manufacturer with complex machinery spread across different plants. Keeping track of each machine's performance manually can be tedious and prone to errors, leading to unexpected downtimes and financial losses. To solve this problem, the manufacturer implements a Digital Twin system. By doing so, it can monitor each machine's health in real-time, identify potential issues before they lead to failures, and schedule maintenance proactively. The result is significantly reduced downtime, cost savings on repairs, and a more streamlined operation.

Level 2: Cognitive Digital Twin

Building on the foundations of the first level, the 'Cognitive Digital Twin' emerged as a solution to the increasing complexity of decision-making in rapidly evolving digital ecosystems. It not only visualizes but mimics human decision-making rules and suggests action plans.

The cognitive digital twin is super valuable when you know what you don't know.

Imagine a logistics company struggling with fluctuating delivery demand, leading to either unmet customer expectations or excessive idle time. By adopting a Cognitive Digital Twin, the company can simulate different scenarios and their outcomes based on historical data and predict future demand more accurately. The twin can suggest optimal delivery routes, schedules, and resource allocation plans. As a result, the company experiences reduced costs, improved customer satisfaction, and enhanced overall performance.

Level 3: Intelligent Digital Twin

The third and most advanced level is the 'Intelligent Digital Twin.' It carries all attributes of its predecessors, but its defining feature is its ability to create new knowledge and decision-making algorithms through self-learning and human-AI collaboration. This level responds to the growing need for systems that can adapt and learn in real-time in our ever-changing world.

It is valuable when you operate in the space of unknown unknown and want to learn and explore a system's behavior hidden from you.

Consider a large-scale energy provider grappling with dynamic energy demand and supply conditions. Implementing an Intelligent Digital Twin can not only proactively forecast energy disruption, but also reveal hidden trends of performance issues anticipate disruptions, and either execute mitigating issues of propose them to a human. Resilience becomes a property of the system, so there is no more differentiation between disruption-free and disrupted modes of operations. The focus shifts to continuous and real-time adaptability and performance persistence, preventing disruption, and generating knowledge about how system works what was not observable before. This results in more efficient resource utilization, no service disruptions, and ultimately, happier and more loyal customers.

In conclusion, the progression from the Digital Twin to the Intelligent Digital Twin is a journey from reactive to proactive system management. 

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