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The Industrial Metaverse

What is the Industrial Metaverse?

There is a Metaverse that is already taking shape right now, it isn’t one that will entertain you, it’s not about fun or the frivolity of your very own personalised simian NFT. It is the hyper-pragmatic overlap of industrial reality — manufacturing equipment, buildings, transport networks even cities — with an always on digital mirror. But more than just reflecting the world, this digital version offers intelligence, decision-support, collaboration and analysis. It will allow us to find, analyse and solve massively complex real-world problems before they arise.

Why now?

Our current industrial operating systems are no longer fit for purpose. With so many moving parts and the ever more complex challenges of handling and exploiting massive amounts of data to generate successful and efficient outcomes we have to look beyond today’s capabilities and change the way we do things. We have to harness the latest innovative technologies.

1

Sustainability

As much as large scale operations have improved our way of living from the steam engine to the advent of robotics, industry has also been responsible for up to 30% of the world’s greenhouse gas emissions. There is therefore a huge responsibility on the part of many, if not all, organisations to redress the balance and come up with better ways to operate whilst making as little impact on the environment as possible.

2

Soaring costs

The Covid-19 pandemic brought many challenges including an immediate downturn in demand for many products. It also highlighted a stronger requirement for ensuring the safety of employees. Many, if not all sectors in the industrial space have seen a record increase in overheads resulting from the energy price rises. With high energy costs becoming the new normal, corporate decision-makers must find innovative ways to mitigate the knock-on effects through insightful decision-making.

3

                   

Efficiency

Today’s operational systems have been handed down from the Web2 period. Upgrades are required across the board to bring improved efficiency and optimal performance and as yet untapped benefits. We have the combination of technologies to usher in the nascent era of the Industrial Metaverse. Right now we are in a stage of combinatorial innovation where the current technology available from Web2 can be federated with the advanced tech of Web3 to bring about impactful new solutions.

       

Requirements of the Industrial Metaverse:




Scalability

Platforms will need to be able to scale in order to manage the massive amounts of data being collated from multiple sensors. High fidelity 3D simulations that are reflecting real time updates will require an intense level of scalable computing power to demonstrate accurate responsiveness.



               

Interoperability

The initial requirement for interoperability within the Industrial Metaverse will be the need for different platforms and systems to be able to share relevant data with one another. Especially those within the same ecosystem in an organisation. A larger scale example would be connecting airports sharing flight arrival and departure information.



               

Security

The safety and security of data being transferred across platforms is critical as different enterprises collaborate within the Industrial Metaverse. Security protocols will need to be established in order to protect enterprise IPs. Information being shared publicly will have to be interest managed so that external users only see that which is relevant.

   

Challenges


Conceptual:

Other than increasing efficiencies and reducing operational costs, there isn’t, as yet, an economic angle to the Industrial Metaverse such as digital native products or models that can be sold off the shelf, a plug and ‘operate’ service for the Industrial Metaverse. There is still some reticence about the “Metaverse” as people come to a better understanding of how its applications can be translated into industrial settings.

       

Technical:

Although exciting, the innovations and use cases currently employing Metaverse technologies are all operating in isolation with very little interoperability even within each organisation’s own digital ecosystems. Assets and data can’t be transferred across from one Metaverse to another. Scalability will need to be embedded into the architecture of systems to ensure they are able to manage a continuous influx of data.

       

Current state of technological advancements

Fast-tracking product design to reduce go-to-market time

In BMW’s Munich plant, engineers are using AR headsets to collaborate on building out the engine of a new prototype vehicle. The parts of the engine are rendered in detailed 3D graphics and streamed from a cloud data platform directly to the engineers’ headsets. By working with AR to carry out this validation, the entire design concept process can be significantly shortened and time to market is sped up.


Digitising engineering and development systems for optimal performance

Boeing is using a digital twin asset development model to design their aeroplanes. The company reported a 40% improvement in the first-time quality of the parts and systems through the use of the model. Here, the digital twin is not only being used to design the jets but also being used within a simulation to test and assess the durability of the plane over the lifecycle of the airframe.


Using digital native technology to improve operations over entire lifecycle

Siemens’ entire Digital Native Factory in Nanjing was planned out using digital technology. A digital twin was simulated to optimise the construction, it enabled the advanced detection of potential problems thus saving time and money on costly fixes. In figures, the manufacturing capacity of the plant increased by 200% whilst productivity went up by 20%.


Data-driven autonomous operations to extract significant insights

Nokia partnered with Aerofarms, an indoor vertical farming company. Through the combination of machine learning, machine vision and AI-based autonomous drone-control they were able to monitor and track the growth of millions of plants. Growers were able to gain real time knowledge of the entire production including data-driven predictions about the yield.

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