Exploring the opportunities technology presents on the fulfilment side of supply-chain processes or the Industrial Metaverse. Image: ShutterstockB
y now, it is hard to be unaware of this new technology wave and its potential impact on the future of business. However, the focus of business articles so far has mainly been on the consumption or demand side of industries such as media and entertainment, retail, and financial services.
Will metaverse technologies have an impact on the supply-side of business? What will the role of the metaverse be in physical supply chains? In this article, we explore the opportunities that this technology presents on the fulfilment side of supply-chain processes or the Industrial Metaverse.
What is the metaverse?
Simply put, the metaverse is the next generation of the web (web 3.0) that is three-dimensional and powered by technologies such as extended reality, including VR (Virtual Reality) and AR (Augmented Reality), Blockchain, Internet of Things (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, allow for commerce, and be interoperable where users in the form of avatars can move from one metaverse to another.Also read: The metaverse and legal frameworks around it
Impact on supply-side of demand
Traditionally manufacturing and industrial companies have been slow at adopting new technologies. Interestingly the core technologies of the metaverse stack, such as extended reality and AI, have been in use in industrial environments 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 set up, monitor, and improve manufacturing processes. Modifications to physical processes are first simulated in their digital twin to reduce downtime in the real world and predict any adverse impact of the change. Extended reality has also been used to maintain equipment and piping in complex, hazardous or geographically dispersed environments, reducing the need for human intervention.
The Internet of Things (IoT) and blockchain are other vital technologies in the metaverse technology stack. These technologies have also been extensively deployed in manufacturing and distribution. IoT sensors are being used for equipment and material tracking and management. Blockchain is being 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. Also read: How AI will democratise strategy for the next industrial revolution
AI is another crucial technology to make the promise of metaverse come alive. For this to happen, there needs to be a greater focus 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 if the machine understands data beyond the narrowly defined task. Overcoming this lack of understanding currently requires significant human intervention. For example, in the true industrial metaverse, the digital twin of an industrial air conditioning should be able to baseline its performance, diagnose any defects, identify paths for correction, and implement the steps on the authorisation. 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 do all the above technologies need to come together, but they also need to interact meaningfully with the real world.
To illustrate this, we use the case of an aircraft manufacturer. The manufacturer wanted to understand the maintenance requirements for critical parts by comparing the health of the aircraft across different time intervals using machine learning algorithms. They hit a roadblock in terms of the volume and variety of real-world data needed to build accurate data models.
The company turned to simulations and built a digital twin of the aircraft. They then created synthetic or machine-generated data by running simulations in the virtual world at a fraction of time and cost. Also read: Seven challenges against securing the systemic cyberspace in the industrial IoT age
Such synthetic data generation is a game-changing application for the industrial metaverse. Overlaying 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 in a visual rendering, AI, driven by self-supervised learning, will bring it to life and make the industrial metaverse a valuable tool for companies.
There are already many examples of how industrial companies are using the metaverse to scale or increase the efficiencies of their operations.
According to a report by Market Data Centre, "The global Metaverse in Industrial Manufacturing Market was valued at $13.1 Billion in 2021, and it is estimated to be valued over $341.4 Billion by 2030, at a CAGR of over 44.85 percent during the forecast period from 2022 to 2030."Also read: Trust, transparency, and more: How businesses make the most of the metaverse
Innovative organisations are already getting a head start by investing in building the technology stack for the metaverse. Designing new products and processes, training employees, maintaining equipment and managing operations with minimum human intervention are some of the initial use cases for the industrial metaverse.
As it is rooted in reality, the industrial metaverse will offer immense opportunity to help companies better understand and improve the physical world in a more scalable, sustainable and safer 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 Fellow, 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]