Imagine that you are the head of a car manufacturing plant, and would like to better understand how you can change your production line in order to get more efficiency and more flexibility to introduce personalized features to your customer. In order to do that, you create digital twins of your production line, and simulate these changes through a collaborative software, involving other teams as well, and without having to change anything on the ground. Your costs of testing these new formats plummet dramatically, as you manage to create many more different scenarios than if you were to do it in the real world. 

Or for instance imagine that you invested into new machinery that is very complex to use: though, the manufacturer of this machinery is going to deliver it in 6 months, since it is so complex to use, you’d like to have your teams ready and able to use it on Day 1. How do make them ready for that? You use VR training to simulate the use of this machinery, you have them prepared beforehand and ready to use the machinery, even though they have never touched it before the day it arrived. 

"Andrea, how is all of this even possible? How can you solve in minutes problems that the industry today takes weeks if not months to solve?". This sounds more like a fictional script from the Netflix "Black Mirror" series, right? 

But make no mistake: it is much more real than Black Mirror, and it represents some of the real-world applications of Web3 technologies to the manufacturing sector.

Let's go step by step.

First of all, what is Web3? Well, Web3 is considered by many the 3rd iteration of the internet, towards which we are approaching thanks to its underlying new technologies: blockchain, Metaverse, DAOs, digital twins, crypto, dApps (decentralized Apps), NFTs, all powered by A.I. and ML (Machine Learning), and so on: basically, a new generation of Internet services that are built on top of decentralized technologies.

How did we get here, though? Let's take a look at the evolution of the Web: Web 1.0 came with the birth of the Internet and fundamentally digitized information, submitting knowledge to the power of algorithms (this phase came to be dominated by Google) and making it read-only for the most part. Web 2.0 came with social media, running mostly on Smartphones, and digitized people and subjected human behavior and relationships to the power of algorithms (this phase was dominated by Facebook), and made the internet not only a place to consume content, but also to create it. 

What about Web3? This third phase will fundamentally digitize the rest of the world and render it in 3D. In Web3, all objects and places will be replicable and readable by machines and subject to the power of algorithms. And who will the metaverse be dominated by? Most likely by anyone and no one at the same time – exactly because it is a decentralized web, as well as it will be a place for people to consume content, produce it but most importantly: own it. It has certain characteristics, namely that it is decentralized (as we mentioned), immersive (namely it is 3D and not only 2D as the internet is today), and persistent (namely, things happen even while we are not online). 

Recent statistics show the opportunity for companies to dive deep into the Web3, as the expectation for the market is to grow steadily:  The global Web 3.0 market size reached USD 3.2 Billion in 2021 and is expected to register a CAGR of 43.7% up until reaching USD 81.5 Billion in 2030, according to a latest analysis by Emergen Research. 

As per some of its underlying technologies, such as the metaverse, the opportunity is very big as well: for instance, a new report by research firm Gartner predicts that by 2026, 25% of people will spend at least one hour per day in the metaverse for work, shopping, education, social and/or entertainment. It’s also expected that 30% of the organizations in the world will have products and services ready for the metaverse by 2026.

When it comes to blockchain, although the financial sector accounts for more than 30% of the complete market value of the technology (a market value that is poised to reach $ 67.4 billions by 2026, according to Markets and Markets), the value of the ecosystem has also begun to spread to other technologies, such as manufacturing (17.6%), distribution and services, (14.6%) and the public sector (4.2%). 

The truth is that, although in manufacturing we might not be there yet when it comes to Web3 maturity, we see a strong acceleration of Digital Transformation in the sector. As a keynote speaker and researcher that works with most manufacturing companies globally (including Schneider Electric, BASF, Bayer,  and many others), I am fully aware of the impact that Digitalization is having on the manufacturing industry, especially after Covid-19: The digital transformation in manufacturing market was valued at USD 263.93 billion in 2020 and is expected to reach USD 767.82 billion by 2026 and work at a CAGR of 19.48% over the forecast period 2021-2026, especially through the acceleration of Industry 4.0, which can be defined as a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, machine learning, and real-time data

But if we can agree that Digital transformation is underway at the moment (and accelerated by Covid-19), we still have to admit that - besides some timid but much-needed experiments and pilot projects - the manufacturing industry is not very clear yet about the potential impacts and opportunities of Web3 on its business, from digital twins of equipments to Edge computing for inventory management, from VR for education and training to blockchain for supply chain management, eventually helping to do what the industry aims for since its inception: better manufacture products with efficiency and quality. 

This is why I have spent the last several weeks talking to experts from the biggest manufacturing companies across the globe, and have put together this episode that describes what are the main impacts of Web3 technologies on the manufacturing industry.

1.Digital Twins of equipments for Industry 4.0

In 2019, Kevin Kelly, the founder of Wired magazine, wrote an amazing cover story for the magazine called “Welcome to the Mirrorworld'', where he describes how Augmented Reality will unleash the next big tech platforms. He wrote: “We are building a 1-to-1 world map of almost unimaginable reach. When completed, our physical reality will merge with the digital universe.” In other words, get ready to meet your digital twin and the digital twin of your home, your country, your office, and even of the world.

“Digital twin?”, you might be asking yourself, especially after having read about this concept previously in the article.

Well, let me then introduce you to one the first building blocks behind the metaverse, that is, the concept of “digital twins”. A digital twin is, according to IBM's definition, a virtual representation of an object or system, or even person as we saw, that spans its lifecycle, is updated from real-time data, and uses simulation, Machine Learning and reasoning to help decision-making. Imagine a large manufacturing company having digital twins of its equipment: through them, an engineer from his home will be able to solve problems in a factory on another continent through the Metaverse. The same technologies will enable office meetings that are much more productive than using today's two-dimensional video conferencing tools. Customer-facing applications can include creating Digital Twins in retail, offering customer service experiences that would not be possible in the physical world, and even engineering companies such as Ericsson are using digital twins to simulate the impact of trees falling on their 5G antennas. Amazing, right?

Besides, it is a huge market: The global digital twin industry was valued at $6.5 billion in 2021, and is projected to reach $125.7 billion by 2030, growing at a CAGR of 39.48% from 2022 to 2030.

When we get to manufacturing and look at the potential implications for the industry, we can use Digital twins in countless applications, but especially in simulations of new lines and equipment. See, one of the biggest advantages of using digital twins is that manufacturers can leverage this technology without having to replace their existing solutions — and drive faster time to value at a lower cost. A manufacturing organization’s underlying MOM architecture will still act as the foundation for its operations.

By implementing a digital twin in parallel with the existing MOM architecture, manufacturers can extract more value from years of technology investments and avoid the need to “rip and replace.” It allows manufacturers to capture data from all existing systems and contextualize it quickly and effectively. Benefits can be realized within three to six months, depending on use-case complexity.

The main benefit of digital twins in manufacturing is their ability to automatically provide comprehensive information about equipment or product performance without any involvement from employees. What is more, putting today’s computing capabilities to use, factories can quickly analyze the data provided by the physical twin using advanced ML algorithms and turn it into actionable insights. Prior to the inception of digital twins, such a level of control in manufacturing was unattainable. With thousands of sensors installed across the plant and constantly streaming data to a digital twin application, manufacturers can accumulate important insights about the system’s performance and make evidence-based adjustments to the plant’s workflow. To top it off, modern sensors can gather data about a wide variety of characteristics ranging from asset thickness and temperature to general environmental conditions at the plant.

With the help of sensors, digital twins can monitor assets outside the manufacturing facility. For example, a car plant can assess how various car components wear and tear or perform under extreme conditions. These insights can be used to enhance future product design. A digital twin also enables manufacturers to experiment with unconventional design decisions without any economic risks and drive innovation. 

Siemens Digital Industries has grown revenues from about $15 billion a year to $20 billion within only two years of transitioning its digital twin portfolio to an industrial metaverse cloud called Siemens Xcelerator.   Digital Industries is the pearl of the Siemens portfolio. Its factory automation tech, a programmable logic controller (PLC), is installed in 30% of all manufacturing equipment and its design and product lifecycle management tools are a staple in most industrial companies. 

2. Edge computing for local data processing 

Let’s start off by remembering what Edge computing is: it is an emerging computing paradigm which refers to a range of networks and devices at or near the user. Edge is about processing data closer to where it’s being generated, enabling processing at greater speeds and volumes, leading to greater action-led results in real time.

Edge computing (as opposed to cloud computing) allows manufacturers to implement automation across factory floor and supply chain processes through advanced robotics and machine-to-machine communication closer to the source, rather than sending data to a server for analysis and response. For example, scanning sheet metal to detect fatigue, monitoring flow through pipes, or keeping track of automated machine cycles, to improve low latency, resulting in faster analysis and correction.

Gathering, analyzing, and acting on data on the factory floor in real-time offers profound benefits. Reducing downtime, accurately predicting maintenance, and improving overall product quality results in higher yield, reduced waste, increased throughput, and lower overall costs.

The future of manufacturing is one in which decisions are made autonomously right on the factory floor, based on real-time conditions. Edge computing helps to integrate all aspects of the manufacturing process, including design, supply chain, and operations. This allows companies to react to changes faster with more flexibility and less waste. Edge computing coupled with open hybrid cloud infrastructure can provide real time transparency, accelerate software-driven production, maximize scaling, and leverage big data for analytics across the IT infrastructure.

Data supports the vision that Edge computing is accelerating. According to the survey  "Edge Computing in Manufacturing," based on an IDC survey of 128 decision-makers at manufacturers in partnership with Lumen and Microsoft, 27% of respondents said they have edge computing in production; and  56% will kick off pilots in the next 2 years.  A whopping 90% of industrial enterprises will use edge computing technology by 2022, according to Frost & Sullivan, while a recent IDC report (registration required) found that 40% of all organizations will invest in edge computing over the next year. 

3. Blockchain to streamline operations

Before getting to its application to the manufacturing industry, let's first understand better what the Blockchain technology is: it is basically a distributed database that is shared among the nodes of a computer network, which stores information electronically in digital format. A blockchain collects information together in groups, known as blocks, that hold sets of information and that have certain storage capacities and, when filled, are closed and linked to the previously filled block, forming a chain of data known as the blockchain. All new information that follows that freshly added block is compiled into a newly formed block that will then also be added to the chain once filled, and when it is filled, it is set in stone and becomes a part of this timeline. Each block in the chain is given an exact time stamp when it is added to the chain. See? The blockchain is a distributed ledger technology (DLT), where that database is spread out among several network nodes at various locations, which makes it decentralized. 

And when it comes to its potential impacts in manufacturing, we can list several ones such as Supply-chain monitoring for greater transparency, Materials provenance and counterfeit detection, machine-led maintenance, Engineering design for long-duration, high-complexity products, Identity management, Asset tracking, IP Protection, and more. 

Let’s look first at Counterfeiting and supply chain fraud. Those are issues that have plagued the industry for years. With this problem continuing to cause issues, manufacturers are turning their attention to blockchain solutions to create a secure and immutable database of assets or products. Blockchain technology enables the creation of a shared digital ledger, which tackles the problems relating to the sharing, and the speed of sharing, information across the fragmented supplier ecosystem. Suppliers have access to a secure and permissioned database where no-one entity owns the data. This mitigates the risk of certain suppliers altering data fraudulently. Blockchain for manufacturing also removes the reliance on paper-based audit trails and the miscommunication that arises from this. Digital twins of physical assets can be created allowing physical goods to be represented on a shared digital ledger, allowing them to be tracked across the supply chain in real-time. This also enables those involved in the manufacturing industry to verify where their materials have come from while preventing counterfeit materials from entering the ecosystem. As blockchain in manufacturing is able to track and prove the origin of materials, an immutable audit trail is achieved while reducing the risk of fraud.

Now, on to maintenance: The manufacturing industry has come a significant way from outdated paper-based checks to track and fix issues. Nowadays, manufacturers are using IoT-enabled devices that give the industry a better understanding of what’s happening across the factory floor. With the aid of blockchain, manufacturers can go one step further by creating a secure network for IoT-enabled machinery. By doing so, it solves the issue of unreliable and bottlenecked IoT networks; networks that leave manufacturers vulnerable to attacks and downtime, which is costly for them. Data shows that manufacturers can lose as much as 20% of productivity, which can include up to 800 hours of downtime, resulting in millions of dollars of lost revenue. With the aid of blockchain technology in manufacturing, manufacturers receive more accurate and reliable data from their IoT-enabled machinery, allowing them to put together preventative measures. Through the use of smart contracts, these checks can be undertaken according to pre-set conditions, while ensuring that real-time diagnostic data is reliable and verifiable through immutable maintenance ledgers. Being able to address issues quickly before any unplanned downtime is beneficial to manufacturers as well.

As per Patents, Organisations across manufacturing industries face a reliance to protect IP. In tandem with cost, IP protection is an important consideration in decisions about whether to make parts in-house or buy them from a supplier. One possibility is for a company to utilize blockchain technology to help prove that it owns IP in the event of a patent dispute. For example, Bernstein Technologies has developed a web service that enables its users to register IP in a blockchain. The service creates a certificate that proves the existence, integrity and ownership of the IP. 

4. Metaverse and VR for education and training 

"Meta-what?": This was probably your reaction to Mark Zuckerberg's recent announcement of Facebook's rebranding to Meta. At least, it was mine. But interestingly enough, now we all talk about the Metaverse thanks to that announcement and although it is not a new idea, we only recently became able to better understand its implications for healthcare companies, especially for the way they conduct surgeries and interact with patients.

But let's first understand what is the Metaverse: the term was born from the junction of the Greek prefix "meta" (meaning beyond) and "universe", and fundamentally is a virtual and collective shared space, created by the convergence of virtually enhanced physical reality (represented by the "Digital Twins", of which we will talk about), and the virtual space that already permeates the physical world (in particular Augmented Reality, also called AR). Confused?

Think of it this way: today we are basically online when we access the Internet, but with new devices, greater connectivity such as 5G and cutting-edge technologies, we will be online all the time in decentralized, immersive and persistent worlds.

One of the great opportunities, besides Digital Twins that we explored previously, that the Metaverse is providing to the manufacturing industry is to educate and train professionals in operations. 

The whole point of the industrial metaverse is to utilize technology to provide a safe working environment for workers, as safety is a prime concern when dealing with people in a physical factory environment.

Thereby, by working in a real-life replica of a factory plant, a trainee or an inexperienced worker can learn how to use or maintain equipment while staying in a risk-free environment. Hence, considering this, many industrial enterprises have chosen to go with a metaverse and VR-first approach rather than a real-world first approach. This modus operandi allows them to carry out:

• Virtual tours and classroom training for multi-users;
• On-the-job guidance for heavy equipment;
• Self-guided onboarding of the workers;
• Hands-on training in a remote, risk-free environment.

The truth is that Today’s Industry 4.0 and advanced manufacturing operations require knowledge about smart technologies and automation. Using artificial intelligence (AI) and augmented or virtual reality (AR/VR) applications as a training strategy can support novice trainees in learning critical manufacturing skills, said Yunbo Zhang, an assistant professor in RIT’s Kate Gleason College of Engineering and part of the research team developing the technology platform. Developing smart technology solutions can also be a means to retain the knowledge of master machinists and manufacturing engineers.

“There are challenges in the workforce in the U.S.; manufacturing industries require a qualified workforce, meaning they can work the machines and conduct the manufacturing process in factories. We believe the training would be an important way to improve the situation,” said Zhang, who teaches in RIT’s Industrial and Systems Engineering Department and has a background in advanced manufacturing and human-centered design.

Trainees would wear virtual reality goggles and through an immersive experience will learn to use the manufacturing systems in a factory setting. Instructions and training tasks would allow trainees to explore that setting safely and efficiently.

Also, production teams are strongly benefited: With rapid advancements in digital twin technology, the industrial metaverse has a lot to offer to production teams. A digital twin is nothing but a virtual representation of physical objects, locations, and processes.

The industrial metaverse enables the creation of digital twins of the industrial environment to monitor complex systems in real time and interact with them using AI, IoT, and VR. This allows the assembly team to design, develop, and assemble digital twins of physical products and processes in the virtual assembly lines, including:

• Gathering information on the state and behavior of monitored machines;
• Transporting heavy equipment or systems like conveyor belts;
• Capturing data from the processes in real-time using sensors and actuators;
• Connecting different processes, information streams, and stakeholders.

An example? Staff at Volkswagen often have to travel long distances for training, which slows down the learning process. Therefore, the organization uses virtual reality to train employees anywhere, anytime.

In virtual reality simulations, employees perform tasks such as assembling a door or a brake. In addition to assembly training, simulations have also been developed for customer service and orientation of new employees.

During the training, someone watches and gives advice and points for improvement. This increases effectiveness and provides room for evaluation and the implementation of improvements.

In total, Volkswagen has developed more than 30 different assignments and trained more than ten thousand employees. The scalability of virtual reality training ensures an efficient process and cheaper training.

5. A.I./ML for automation and prediction

I would like you to imagine the following scenario: you are the pilot of an airplane, and one day, while in the middle of the flight, one of your engines breaks down. Terrible, right? It happened suddenly, and seemingly nothing would have been able to preview that. 

But the truth is that, yes, it might have likely been possible to preview it if the airplane was filled with sensors that would capture data in real time, and through A.I. In other words, it would be able to anticipate an engine stopping through correlations and simulations based on the Big Data it collects (pretty much like a Tesla is able to do, differently from most cars).

See, this power of Big Data being processed by Artificial Intelligence, that by its definition is when computer systems are able to perform tasks and to solve problems that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages, among others. This power helps us to predict more and react blindly less. And consider that we already live in a world with lots and lots of data, where more than 90% of the data generated since the beginning of humanity was generated in the last decade, and where today we got to the point of 97 Zettabytes of data by the end of 2022 according to Statista (which just to give you an idea, a Zettabyte is a number with 12 zeros…that's a lot of data!). 

So how can Big Data and A.I. impact the manufacturing sector?  The truth is that there are a plethora of applications, such as demand forecast accuracy, process automation and increased efficiencies.

The impact of AI in manufacturing is game-changing. French food manufacturer Danone uses machine learning to improve its demand forecast accuracy. This has led to a:

• 20% decrease in forecasting errors
• 30% decrease in lost sales
• 50% reduction in demand planners’ workload

Meanwhile, the BMW Group uses automated image recognition for quality checks, inspections, and to eliminate pseudo-defects (deviations from target despite no actual faults). As a result, they’ve achieved high levels of precision in manufacturing.

Another company that benefited from AI in manufacturing is Porsche. They use autonomous guided vehicles (AGVs) to automate significant portions of automotive manufacturing. The AGVs take vehicle body parts from one processing station to the next, eliminating the need for human intervention and making the facility resilient to disruptions like pandemics.

These are just a few examples of companies leveraging AI in manufacturing to improve overall productivity and operational efficiency.

But why is A.I. so crucial to the future of manufacturing? AI tools can process and interpret vast volumes of data from the production floor to spot patterns, analyze and predict consumer behavior, detect anomalies in production processes in real-time, and more. These tools help manufacturers gain end-to-end visibility of all manufacturing operations in facilities across all geographies. Thanks to machine learning algorithms, AI-powered systems can also learn, adapt, and improve continuously.

Such capabilities are crucial for manufacturers to thrive in the aftermath of pandemic-induced rapid digitization.

According to McKinsey, companies using AI have witnessed cost savings and revenue growth. 16% of those surveyed noticed a 10-19% decrease in costs, whereas 18% saw a 6-10% increase in overall revenue.

AI systems also enable predictive analytics, which helps tackle operational challenges and disruptions to supply chains as well as the workforce. A McKinsey report suggests that AI can improve forecasting accuracy in manufacturing by 10-20%, which translates to a 5% reduction in inventory costs and a 2-3% increase in revenues.

Let’s look at the impact of A.I. in predictive maintenance: A McKinsey report states that the greatest value from AI in manufacturing is because of predictive maintenance, which accounts for $0.5-$0.7 trillion in value worldwide. BCG calls predictive maintenance the first Industry 4.0 priority, especially for cement producers.

AI-powered systems can:

• Capture and process big data (including audio, video, and GPS) from sensors on the shop floor
• Help spot anomalies or equipment inefficiencies to prevent unplanned equipment breakdown. This could be an odd sound in a vehicle’s engine or an assembly line malfunction.
• Prevent unplanned equipment downtime to improve factory efficiency while reducing costs
• Besides, fixing malfunctions in individual components is cheaper than replacing an entire machine.

This is all so amazing, and it proves that Web 3 and A.I. can generate an incredible upside to manufacturing companies of all sizes and segments.