Unleashing the Power of the Industrial Metaverse with Extended Reality and AI


Explore the opportunities that technology presents on the process execution side of the supply chain or industrial metaverse. Image: Shutterstock

BToday, it is difficult to ignore this new wave of technology and its potential impact on the future of businesses. However, until now trade articles have mainly focused on consumption or demand in sectors such as media and entertainment, retail and financial services.

Will metaverse technologies have an impact on business offerings? What will be the role of the metaverse in physical supply chains? In this article, we explore the opportunities this technology presents on the process execution side of the supply chain or industrial metaverse.

What is the Metaverse?

Simply put, the Metaverse is the next generation of the Web (Web 3.0) three-dimensional and powered by technologies such as Extended Reality, including VR (Virtual Reality) and AR (Augmented Reality), Blockchain, Internet objects (IoT), and Artificial Intelligence (AI). Like its predecessor, Web 2.0, the metaverse will be universally available, support an unlimited number of users, be real-time, enable commerce, and be interoperable where users in the form of avatars can switch between metaverses to another.Read also: The metaverse and the legal frameworks surrounding it

Impact on supply of demand

Traditionally, manufacturing and industrial companies have been slow to adopt new technologies. Interestingly, the core technologies of the Metaverse Stack, such as extended reality and AI, have been used in industrial settings for nearly a decade. Extended reality has been used to design and test physical products as diverse as running shoes and rockets. Digital twins or virtual twins of physical assets and systems have been used to configure, monitor and improve manufacturing processes. Changes to physical processes are first simulated in their digital twin to reduce real-world downtime and predict any negative impact of the change. Extended Reality has also been used to service equipment and piping in complex, hazardous, or geographically dispersed environments, reducing the need for human intervention.

Internet of Things (IoT) and blockchain are other vital technologies in the metaverse technology stack. These technologies have also been widely deployed in manufacturing and distribution. IoT sensors are used for tracking and managing equipment and materials. Blockchain is deployed in the distribution process to help maintain the integrity and transparency of supply chains by recording data such as quality, provenance, and other relevant information.

Read also: How AI will democratize the strategy of the next industrial revolution

AI is another technology crucial to realizing the promise of the metaverse. For this to happen, more emphasis needs to be placed on self-supervised learning. Traditionally, supervised learning, where machines learn from direct human supervision, was achieved by teaching systems to perform a single task by giving them volumes of human-generated examples. However, the challenge with this approach is that it is unclear whether the machine understands the data beyond the narrowly defined task. Overcoming this misunderstanding currently requires significant human intervention. For example, in the real industrial metaverse, the digital twin of an industrial air conditioner should be able to reference its performance, diagnose any faults, identify possible solutions and implement the steps for permission. Other than the last step, there should be no requirement for human intervention. For this to become a reality, self-supervised learning will be essential.

Convergence of simulation and reality

For the industrial metaverse to be scalable, not only must all of the above technologies come together, but they must also interact in a meaningful way with the real world.

To illustrate this, let’s take the case of an aircraft manufacturer. The manufacturer wanted to understand the maintenance requirements of critical parts by comparing the health of the aircraft over different time intervals using machine learning algorithms. They hit a snag in terms of the volume and variety of real data needed to create accurate data models.

The company turned to simulations and built a digital twin of the plane. They then created synthetic or machine-generated data by running simulations in the virtual world in a fraction of time and cost.

Also Read: Seven Challenges Against Securing Systemic Cyberspace in the Age of Industrial IoT

Such synthetic data generation is a game-changing application for the industrial metaverse. Layering data generated from simulation environments with real-world data generated using 5G and IoT creates a virtuous feedback loop between the virtual and physical worlds.

While technologies such as extended reality will help visual rendering, AI, driven by self-supervised learning, will bring it to life and make the industrial metaverse a valuable tool for business.

Look forward

There are already many examples of how industrial companies are using the metaverse to scale or increase the efficiency of their operations.

According to a Market Data Center report, “The global industrial manufacturing metaverse market was valued at $13.1 billion in 2021, and is estimated to reach over $341.4 billion by 2030, at a CAGR of over 44.85% over the forecast period from 2022 to 2030.”

Also read: Trust, Transparency and More: How Businesses Make the Most of the Metaverse

Innovative organizations are already getting a head start by investing in building the tech stack for the metaverse. Designing new products and processes, training employees, maintaining equipment, and managing operations with minimal human intervention are some of the primary use cases for the industrial metaverse.

Grounded in reality, the industrial metaverse will provide an immense opportunity to help companies better understand and improve the physical world in a more scalable, sustainable and secure way.

Authors:
Srinivas Pingali, Professor of Practice, Mahindra University
Srinivas Atreya, Chief Data Scientist, Roundsqr
Sumanta Singha, Assistant Professor, ISB
Kiran Pedada, Assistant Professor and F. Ross Johnson Scholar, University of Manitoba

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[This article has been reproduced with permission from ISBInsight, the research publication of the Indian School of Business, India]

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