Cite this Article

Ali Ramezankhani, Azra Jeelani, 2025. "AI-Powered Digital Twins for Real-Time Sustainability Tracking in Manufacturing", International Journal of Research in Artificial Intelligence and Data Science(IJRAIDS)1(1): 45-56.

The International Journal of Research in Artificial Intelligence and Data Science (IJRAIDS)
© 2025 by IJRAIDS
Volume 1 Issue 1
Year of Publication : 2025
Authors : Author Name
Doi : XXXX XXXX XXXX

Keywords

AI-powered digital twins, sustainability tracking, real-time monitoring, manufacturing industry, industrial IoT, predictive analytics, smart manufacturing, energy efficiency, carbon footprint reduction, cyber-physical systems.

Abstract

As the world moves swiftly towards more environmentally friendly industrial practices, the manufacturing sector, which has traditionally been a major source of carbon emissions and resource use, is under a lot of pressure to adapt. The solution is that combining artificial intelligence (AI) with digital twin technology offers a new technique to keep track of sustainability in real time that might change the game. Digital twins are replicas of things, processes, or even full systems that exist in the actual world. They help manufacturers keep an eye on, copy, and make operations better with incredible accuracy. Adding AI features like machine learning, predictive analytics, and real-time anomaly detection to these digital twins turns them from passive models into smart agents that can not only copy reality but also predict and make it better. This article talks about how AI-powered digital twins could help make manufacturing more environmentally friendly by enabling people keep an eye on things like energy use, emissions, waste, water use, and carbon footprint all the time. Digital twins let you make changes ahead of time and see how things will turn out by connecting the real world with the virtual world. This helps companies make sure their work is in line with global aspirations for sustainability.These systems' best feature is that they can turn data from a variety of sources, such as IoT sensors, production logs, supply chain nodes, and corporate systems, into usable, actionable information. Digital twins employ AI algorithms to learn from this information so they can see problems, try out changes, and make the best choices. For example, a smart factory might use an AI-powered digital twin to keep track of how much power each machine on the floor is using. The twin can tell if the rapid increase in use is due to mechanical wear, bad scheduling, or outmoded processes, and then recommend fixes or even implement them automatically. These skills not only have less of an impact on the environment, but they also make things run more smoothly, make products better, and save money.Companies can also use this technology to run "what-if" scenarios without pausing real production. Before they employ new materials, change the way they create goods, or change the way they receive supplies, they may utilise their digital twin system to assess how each choice would effect the environment.

Businesses who have to cope with difficult environmental standards and greater demands from stakeholders for openness and responsibility need to be able to see things ahead of time like this.Digital twins powered by AI have a lot of potential for tracking sustainability, but there are still some issues with using them. Some technical issues include combining data, making ensuring that all of the old systems are following the same standards, and the need for a lot of processing capacity for real-time analytics. You also need to be very careful about moral issues like data privacy, model explainability, and the risk of greenwashing, which is when systems are falsely advertised as being more environmentally friendly than they really are. But it's apparent that this technology gives organisations a strategic edge: those who use it well will not only reach their environmental goals, but they will also defend their business from the future in a market where sustainability and digitisation are swiftly becoming one and the same.This research looks at the structure, features, probable usage, and future prospects of AI-driven digital twins, especially in manufacturing settings that prioritise sustainability. It shows how this new mix of AI and virtual modelling could lead to better, greener, and more adaptable production systems by using case studies from the commercial sector and technical frameworks. This manner, it adds to the growing body of knowledge that is shaping the Fourth Industrial Revolution, when enterprises must be ecologically responsible to stay competitive.

Introduction

This is a highly significant time for the industrial business. It is locked between the inevitable march of the digital revolution and the urgent demand for sustainability around the planet. Manufacturers need to consider about not only what they make, but also how they make it as people become more worried about the environment due to climate change, resource depletion, and government rules. In a society that needs to be able to respond quickly and be open, old means of keeping track of environmental effect, which often employ static reporting, delayed feedback, and walled data systems, are becoming less and less useful. In this high-stakes circumstance, AI-powered digital twins give us a whole new approach to keep track of and improve sustainability performance at all levels of industrial processes. They are smart, work in real time, and can change. A digital twin is more than simply a digital replica. It's a model of something genuine, like a machine, a manufacturing line, or even an entire factory. It learns and grows over time by gathering data from sensors, IoT devices, and business systems. Adding AI to digital twins makes them able to help make decisions, forecast what will happen in the future, detect problems, and even come up with long-term solutions on their own.

This study says that AI-powered digital twins are not just a better technology; they are also a significant tool for making production more sustainable in real time. Digital twins can employ AI techniques like machine learning, deep learning, and reinforcement learning to analyse intricate environmental data in real time. They can discover spikes in energy use, figure out when maintenance will be needed to avoid waste, model emissions from the supply chain, and modify the flow of resources in real time to reduce the damage to the environment. These talents aren't simply things that could happen in the future; they're growing more and more likely due of better edge computing, 5G connections, and cloud-based platforms. Even while AI-powered digital twins are growing better, they are still not used sufficiently to help with sustainability goals. This is especially true for small and medium-sized enterprises who don't know how to use these systems or don't have the right tools. This paper seeks to address that gap by looking at the main parts, benefits and downsides, and moral difficulties of employing AI-driven digital twin technology to keep track of sustainability in manufacturing settings.

There are many conceivable ramifications of this technology. As the world moves towards carbon neutrality, circular economy models, and ESG (Environmental, Social, and Governance) reporting, manufacturers need tools that are both accurate and adaptable. Digital twins powered by AI can do this by giving you extensive, real-time information about everything from how much energy and water you use to how you manage your waste. More importantly, they let companies shift from reactive compliance to proactive sustainability leadership, making sustainability a business benefit instead of just a checkbox. But making this modification isn't simple. There are huge concerns that need to be fixed, such making sure data can work together, keeping AI safe, making sure AI can be explained, and the risk of digital greenwashing. The upcoming chapters of this article will go into more depth regarding these things. It describes a plan for how digital twins with AI could become a fundamental aspect of sustainable industrial innovation in the Fourth Industrial Revolution.