What is the metaverse? At ÎÚÑ»´«Ã½, we define it as a blended space of virtual and physical interactions that can be used to improve the customer experience. For major brands, this technological evolution is often interpreted as the creation of richer and deeper interactions with the people who buy their products and services, whether they’re at home or at play.

However, we believe the current hype for all things metaverse is misdirected. Certainly, immersive technologies will impact on customer-facing sectors, the true step change will take place in the industrial metaverse. Here, organizations will use virtual environments to transform operations in organizations across the public and private sectors. The impact of the industrial metaverse on work and society will be inexorable.

A recent ÎÚÑ»´«Ã½ Research Institute report explored this potential in more depth; Total Immersion; How immersive experiences and the metaverse benefit customer experience and operations, found that 77% of consumers expect immersive experiences to impact how they interact with people, brands and services, but also that organizations recognize the broad opportunities it presents to drive value across the business, specifically in their  internal operations. And my own paper On the way to the industrial Metaverse takes a much deeper dive into how metaverse technologies will help achieve operational excellence in the industrial workplace. This blog provides an introduction to the key topics.

Technology convergence: The evolution of the industrial metaverse

This potential for new business models means the metaverse is a hot topic in technology departments, marketing organizations, and executive boardrooms. Bolstered by significant investments from cash-rich Big Tech firms, business leaders are eager to explore the nascent metaverse in consumer-facing areas such as retailing and gaming.

Yet customer-facing deployments are not the only game in town. Away from the cacophonous hype of virtual try-ons and NFT tokens, there’s a set of hidden use cases that hold more promise: the industrial ones. These applications range from optimal resource utilization to reduced travel requirements and onto advanced simulation models.

While the metaverse has been popularized recently, its evolution has involved a 20-year convergence of IT, automation, and cognitive models, including the internet, natural language processing, lean manufacturing, and failure prediction in mission-critical systems. The industrial metaverse is the next stage of this ongoing convergence.

As opposed to the static spaces of the consumer metaverse, the dynamic spaces of the industrial metaverse are complex and layered. This ever-evolving reality involves interactions on a deeper, more collaborative level. We suggest the dynamic experiences of the industrial metaverse are best exemplified by the next generation of digital twin technology.

From digital twins to the “Internet of Experiences�

Digital twins are virtual representations of real-life objects, processes, systems, and their interconnections. Employees are already using twins in enterprises to perform practical tasks, such as system simulation, monitoring, and maintenance – and all without having to interact with real-world objects.

The industrial metaverse will take digital twins to a new level. This shift will become manifest via a progression from the Internet of Things, where data is exchanged between sensors in physical objects over networks, to the “Internet of Twins,� where an interconnection between digital objects and information flows creates an added dimension.

In this Internet of Twins, workplace users will carry out co-design and co-simulate processes at scale. For example, we envisage a factory in which equipment, products, and people are all connected. And the way these elements operate – and their associated behaviors – could be simulated in a dynamic, virtual experience.

Employees will encounter richer representations of the situations they encounter. Unlike in static consumer metaverses, these dynamic industrial metaverses will involve collaboration between different digital twins and their associated simulations.

By working together, employees will use this dynamic space of systems and data to create solutions to the problems they find. This dynamic metaverse of interconnected machines and humans will form the basis for a new era, which we refer to as the “Internet of Experiences.�

Opening a multitude of use cases

This Internet of Experiences creates opportunities for using metaverse capabilities across a variety of sectors, including engineering, manufacturing, and supply chains. We consider those in detail in On the way to the industrial Metaverse, but let’s outline some of those use cases now:

• Production-level simulations – Testing manufacturing scenarios across machines, plants, and supply chains, and exploring different strategies across an ecosystem.

• Collaborative R&D – Producing interactive environments for the design of products and services, so that they can be tested before mass production.

• Health and safety training – Creating virtual representations of real-life operations so that employees can collaborate in test environments before working in the field.

• Smart cities – Using data to analyze the interaction between people, vehicles, and other facilities to manage our fast-growing cities.

• Personal support services – Providing robots that watch over people in need, communicating with other assistants, and facilitating access to other necessities such as groceries.

It’s our contention that enterprise-level applications will create huge benefits for organizations and the people they serve, both internally and externally. As well as delivering value through better designed products and cost-effective services, the industrial metaverse will provide advantages in another critical area: sustainability.

The more you improve simulation capabilities, the more you improve the potential for sustainability. Take digital twins, which allow employees to simulate potential scenarios across the lifecycle of a product or service. By using data to anticipate safety issues and future behaviors, the right choices can be made the first time, and finite resources can be saved.

Conclusion: Overcoming challenges to deliver results

By blending the physical and digital in the industrial metaverse, your business will be able to change its operations and potentially society in general for the better. Sounds exciting, doesn’t it?

But before you get ready to invest your company’s cash, it’s important to issue a note of caution: delivering the Internet of Experiences will be far from straightforward. There are a number of significant technological and cultural barriers that will need to be overcome.

We discuss these challenges in the full paper, suggesting that organizations should start exploring the industrial metaverse now, working with metaverse partners to identify potential use cases that make sense for the business. The convergence of technologies – while complex – will also create incredible opportunities. Now we just need to grasp them!

Read  the full paper, On the way to the industrial Metaverse, here.

The progress in quantum computing is accelerating

The first quantum computers were introduced 25 years ago (2 and 3 qubits), the first commercially available annealing systems are now 10 years old. During the last 5 years, we have seen bigger steps forward, for example systems with more than twenty qubits. Recent developments include the Osprey chip with 433 qubits by IBM, first results of quantum error correction by Google, as well as important results in interconnecting quantum chips announced by the MIT.

From hype to realistic expectations

Where some see steady progress and concrete steps forward, others remain skeptical and point out missing results or unkept promises—the most prominent of which is found in the field of the factorization into large prime numbers: There still is a complete lack of tangible results in breaking the RSA cryptosystem.

However, development in quantum computing has already passed various important milestones. Dismissing it as mere hype that will pass eventually now becomes increasingly difficult. In all likelihood, this discussion can soon be laid to rest, or at least refocused towards very specific quantum computing frontiers.

The domain of machine learning has a natural symbiosis with quantum computing. Especially from a theoretical perspective, research in this field is considered fairly advanced. Various research directions and study routes have been taken, and a multitude of results are available. While much research is done through the simulation of quantum computers, there are also various results of experiments run on actual, non-simulated quantum devices.

As both the interest and the potential of quantum machine learning is remarkably high, ÎÚÑ»´«Ã½ and the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, have delved deeply into this topic. On request of the German Federal Office for Information Security (BSI), we went as far as analyzing the potential use for, as well as against, cyber security. One of the major results of this collaboration is the report ““, published by the BSI. Current developments indicate that there is trust into quantum machine learning as a research direction and it’s (perceived) future potential.

Laggards increasingly lack opportunities

The ever-growing availability of better and more efficient IT technologies and products is not always reasonable to implement and often difficult to mirror in an organization. Nevertheless, innovation means that a certain “technology inflation” constantly devalues existing solutions. Therefore, an important responsibility of every IT department is to keep up with this inflation by implementing upgrades and deploying new technologies.

Let us consider a company that still delays the adoption of cloud computing. While this may have been reasonable for some in the early days, the technology has matured. Over time, companies that have shied away from adoption have missed out on various cloud computing benefits while others took the chance to gain a competitive advantage. Even more, the longer the adoption was delayed or the slower it was conducted, the further the company has allowed itself to fall behind.

Time to jump on the quantum computing bandwagon?

Certainly, quantum technology is still too new, too unstable, and too limited today to adopt it in a productive environment right away. In that sense, a pressure to design and implement plans for incorporating quantum computing into the day-to-day business does not exist today.

However, is that the whole story? Let us consider two important pre-implementation aspects: The first of these is to ensure everyone’s attention for the topic: For an eventual adoption, a widespread appreciation for what might be gained is crucial to get people on board. Without it, there is a high risk of failing­—after all, every new technology comes with various challenges and affords some dedication. But developing the motivation to adopt something new and tackle the challenges takes time. So, it’s best to start early with building awareness and basic understanding of the benefits throughout all levels and (IT) departments.

The second aspect is even more difficult to achieve: experience. This translates to know-how, participation, and practice within the organization to get prepared for the adoption of technologies once they are ready for productive deployment. In the case of quantum computing, gaining experience is harder to achieve than with other recent innovations: In contrast for example to cloud computing—which constitutes a different way of doing the same thing, and thus allows companies to get used to them slowly—quantum technologies represent a fundamentally new way of computation, as well as a completely new approach of solving problems and answering questions.

The key to the coming quantum revolution is a quantum of agility

Bearing in mind the scale of both pre-implementation aspects and of the uncertainty of when exactly quantum is going to deliver advantage in the real world, organizations need to start getting ready now. On a technical level, and in the realm of security, the solution for the threat of quantum cryptanalysis is deployment of post-quantum cryptography. However, on an organizational level, the solution is crypto agility : having done the necessary homework to be able to adopt quickly to the changes, whenever they come. Applying the same concept, quantum agility represents having the means to adapt quickly to the fundamental transformations that will come with quantum computing.

Thus, building awareness and changing minds now will have a considerable pay-off in the future. But how can organizations best initiate this shift in mindset towards quantum? Building awareness is a gradual process that can be promoted by a working group even with small investments. This core group might for example look out for possible use cases specific to the respective sector. Through various paths of internal communication, they can spread the information in the proper form and depth to all functions across the organization.

To build up knowledge and experience, the focus should not be on viable products, aiming to replace existing solutions within the company. Instead, it is a way of playing around with new possibilities, of venturing down paths that might not ever yield any tangible results but aiming to discover guard rails subjective to each corporation and examine fields where quantum computing might eventually be the way to substantial competitive advantages.

Frontrunners are gaining experience in every sector

For example, some financial institutions are already exploring the use of quantum computing for portfolio optimization and risk analysis, which will enable them to make better financial predictions in the future. Within the pharma sector, similar efforts are made, gauging the potential of new ways of drug discovery.

In the space of quantum cyber security, together with the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, ÎÚÑ»´«Ã½ has built a : performing spam filtering on a quantum computer . While this might be the most overpriced—and under engineered—spam filter ever, it is a functioning proof of concept.

Justifying investment in quantum computing requires long-term thinking

The gap between companies in raising organizational awareness and gaining experience with the new technology is gradually growing. Laggards have a considerable risk of experiencing the coming quantum computing revolution as a steamroller, flattening everyone that finds themselves unprepared.

The risks and challenges associated with quantum technology certainly include the cost of adoption, the availability of expertise and knowledgeable talent, as well as the high potential of unsuccessful research approaches. However, the cost of doing nothing would be the highest. So, it’s best to start now.

We don’t know when exactly the quantum revolution will take place, but it’s obvious that IBM, Google and many more are betting on it—and in the °ä²¹±è²µ±ð³¾¾±²Ô¾±â€™s Quantum Lab, we are exploring the future as well.

Sustainable Aviation Fuel is one option, and has the benefit of working with most existing engine designs. But entirely new propulsion systems, eg hydrogen and electric, require whole new designs for the aircraft’s powertrain, from engines to fuel tanks and transport, and power transmission to propellers. And in some cases, it may require a wholesale redesign of the plane.

This will not be easy. The companies who have started on this path see many years of work before they can get green planes into regular flight. There is an engineering challenge ahead on a scale and urgency the likes of which aviation has never seen. Nonetheless, companies large and small are taking on the challenge.

Time is of the essence, not only because the clock is ticking on climate change, but also because the companies that get there first will have a significant advantage. This doesn’t just mean fielding new aircraft, but also retrofitting existing fleets for sustainability. For example, just a year’s jump on competitors could mean many orders, before others catch up.

So, how can companies accelerate this Engineering R&D process?

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The challenge ahead

Decarbonizing propulsion comes with a series of options, each with its own challenges. We will summarize the opportunities and challenges of each, before discussing solutions.

Digital enablers: getting there faster

The challenges above will clearly take energy and research. Given the safety-critical nature of aerospace, they will also take a lot of testing before passengers are allowed anywhere near them. Some of this just has to be done, but some elements can be sped up through new digital engineering approaches.

Digital design tools can help scope design and architecture, engineering, as well as the electrical, mechanical systems and physical domains, and how they should join up. Modelling – when designed by aerospace entering experts – can help optimize and define the most effective configuration for fuselages, tanks and wings, predict the best materials choices, and design the integration of electrical, electronic, and mechanical components. Even Artificial Intelligence (AI) can help propose optimal designs if provided with clear input criteria, reducing the number of false starts, and the need to produce early physical prototypes.

Simulations and physics-based systems modelling can be used for understanding important properties, like thermal management, which will be critical for the safety and efficiency of designs of battery packs, and engines using new fuel sources stored at different pressures and temperatures.

Software design will also be increasingly important for system management, as powertrains are electrified and systems need to be monitored and held at particular states throughout the flight.

Model-based system engineering (MBSE) – ‘the application of modeling to support system requirements, design, analysis, verification and validation (V&V) activities’ – allows designers to take a holistic view; analyzing the aircraft system as a whole throughout its entire lifecyle, and identify the interactions between its components. The digital approach can help to accelerate projects, hastening the Validation & Verification (V&V) certification process, for example, by allowing more of the test and evaluation work to be done digitally.

AI can also be used to translate flight data into scenarios for simulated testing from the component level up to virtual flight tests in varied and challenging conditions. This helps spot challenges early, reducing risk in costly real-life flight tests – though these, of course, must be done eventually. Once they are, detailed data collection, post-processing and visualization can help understand risks and improvements – which can be fed back into the digital model in order to improve the design.

´¡¾±°ù²ú³Ü²õ’ is a good example of ‘digital-first’ in action. It has been used to help develop the Future Combat Air System and the A321XLR, which Airbus claims burns 30% less fuel than previous generation aircraft.

No silver bullets, but many choices

The commercial aviation community agrees on a mixture of propulsion solutions, but there are no ‘silver bullets’.

In the near future, SAF-powered aircraft will likely predominate, as SAF requires no modification to airframes and is an improvement on conventional fuel from a sustainability perspective. SAF alone won’t get us to Net Zero, however.

This leaves us with hydrogen and electricity. Electricity is always likely to be limited to short distance flights due to the weight of batteries. Green hydrogen will likely be the eventual solution for commercial flights, due to its highly sustainable credentials and ability to be stored on board as a liquid fuel. Nonetheless, hydrogen will add storage and weight to current aircraft compared to kerosene, and so SAF is likely to be the best option for long-distance flights, at least in the medium term.

Neither electric nor hydrogen propulsion is anywhere near ready to meet the full needs of commercial aviation, but both are progressing rapidly. Both will require major redesigns of aircraft, followed by endless optimization and rigorous testing. Those that get through this process first will have a significant competitive advantage. Digital engineering will likely determine the winners.

Generative AI (GenAI) is beginning to transform many activities, and product support is no exception. Product support is vital for the ongoing function of all products, from Microsoft Office to niche robotics systems. Users need product support when installing systems, integrating with other software, working out how to use the product, and resolving issues when they arise. 

Such work must be handled by experts who understand the product and its operation. The cost of this support must be factored into any product cost model, so improving the support process can unlock revenue by extending the life of products while reducing the costs of supporting them. This is particularly true as products reach “end of lifeâ€�, when user numbers often shrink, and support costs relative to revenue can become problematic.

The potential of GenAI in product support

Because GenAI can process information and predict the answer to a question based on experience, it opens a world of possibilities for product support. Given sufficiently large training data of good quality, GenAI can be taught about the fundamental nature of systems and predict the most appropriate answers to questions about them. A few examples of GenAI’s potential uses in product support are developed below.

• Tech support automation: GenAI’s ability to learn answers to common technical questions about problems and provide quick and detailed responses means such a service can be available 24/7. Further, GenAI responses can be adapted to the specific user query and context. This approach is an important improvement on the typical support model, based on asking a series of fixed questions and pointing the user to an off-the-shelf ‘how-to’ article.
• Augmenting human support workers: GenAI can facilitate the work of human support workers by summarizing requests and providing these workers with the relevant information to solve these requests quickly. If support workers respond by email, GenAI can help them turn their response into text that will be easier for the user to follow, based on the GenAI model’s technical knowledge. It can also translate responses, allowing teams to offer support, even when they do not speak the user’s language.
• Onboarding new hires in the support team: A support GenAI can be used to train new support engineers on common product issues.
• Software product upgrades: Generative AI can be used by support engineers to facilitate software product upgrades, for example, translating software code into a newer language or modifying code to be more efficient as part of a green code sustainability initiative.
• Streamlining processes: GenAI tools can automatically categorize emails and support tickets and learn to prioritize in order of importance, assigning these to the relevant experts or those with the most capacity.

A well-composed suite of GenAI-powered tools can reduce time-to-solution, human error, and product support costs and so allow experts to focus on the more complex tasks that humans are best suited to.  

GenAI in product support – the art of the possible

Theoretical possibilities are all well and good, but what is happening in the real world? ÎÚÑ»´«Ã½ is fortunate to have worked with multiple clients on projects to create value by harnessing GenAI in their product support processes and systems.

In one example, a large computer hardware organization wanted a system to identify multiple ticket types, handle initial conversations with users, and respond in various languages. The GenAI system we developed provided the firm’s customers with step-by-step instructions on how to resolve their queries. These responses were based on information in product knowledge bases and user manuals. It also identified user queries that couldn’t be solved using this approach and then escalated them to human support engineers. Finally, the GenAI collated user feedback and used this to propose updates to the knowledge base. The outcome was considerably fewer tickets routed to human agents, saving time and money.

In another case, we worked with a Network Equipment Provider to develop a chat assistant to provide ‘human-like’ first-level responses and summarize tickets for efficient handover to other support staff. Again, we saw reduced operational costs and improved SLA (Service Level Agreements) adherence in their 24/7 operations.

In a final example, we built a do-it-yourself (DIY) tool and analytics generator for a leading telco. They needed to document the standard operating procedures (SOPs) of their support engineers for future training and generate role-based visualization and prediction. The customer required a centralized management dashboard that unified all IT platforms on a single pane and a GenAI-based tech stack for predictive and preventive monitoring. 

The challenges of integrating GenAI in product support

Developing, deploying, and running GenAI-powered systems is becoming ever more accessible, thanks to the increasing availability of large open-source language models. However, care needs to be taken when integrating AI into systems.

Firstly, GenAI must be carefully crafted and trained for the specific use case – using up-to-date, high-quality data. The AI will be wrong if the user manual or knowledge base is wrong. This means that people who understand the product for which the GenAI support system is being developed must be involved in designing and testing it. They must ensure it has been trained correctly. Because GenAI is probabilistic, GenAI outputs can occasionally be wrong; this is often described as a ‘hallucination’ in the GenAI community. Consequently, quality control is vital.

Secondly, there are IT practicalities to consider. The IT infrastructure must offer sufficient computational power to run a GenAI model and provide the connectivity needed for the GenAI to interact with knowledge management databases and issue management systems (including email, WhatsApp, etc.). There must also be a single source of truth so that any updates to the knowledge base – by humans or AI – feed into the GenAI’s model of the world. Organizations must be willing to share this data, regardless of its sensitivity.

Finally, GenAI project timescales need to be calibrated to the business case. Training takes time, but no business wants to wait a year for a perfect GenAI support system that will be obsolete when launched. An AI that can solve 50% of queries and refer the rest to humans but takes three months to build and deploy may offer better value than one that can solve 60% of queries but takes two years to deliver.

Ultimately, the recipe for success with support systems is the same as most data projects. Set clear goals and expectations. Work with experts who know the tech and the domain, and use frameworks that allow you to move efficiently through the development process.

ÎÚÑ»´«Ã½ has multiple software frameworks and project blueprints to accelerate the development, deployment, and operation of GenAI in product support systems. Contact our experts to learn more.

It feels like my four days at IAA Mobility 2023 passed in a flash, and yet I still had time to meet at least a dozen members from the world’s press, spend time at °ä²¹±è²µ±ð³¾¾±²Ô¾±â€™s booth (our first ever at this prestigious event), participate in a panel discussion with friends from and , and even pay a visit to Munich’s beautiful downtown, where the world’s leading brands were showcasing their latest models and innovations, and inviting the general public to be part of the event. It perfectly reflected my opinion that mobility should be open and accessible to all. 

In and among the excitement and action of my four days in Munich, three themes emerged that I believe will shape the automotive industry in the months and years to come. They are: how to deal with continued uncertainty in the supply chain, the challenge presented by new market entrants, and the importance of collaboration in shaping a future of mobility that is intelligent, sustainable, and connected.

Dealing with uncertainty as the ‘new normal’

The automotive industry has experienced more than its fair share of disruption in recent years. Every major industry had to deal with the effects of the pandemic, the blockage of the Suez Canal, and the fallout from geo-political tensions around the world. But the automotive industry was perhaps more exposed than others due to the complex nature of its supply chain and its growing reliance on semiconductors and chips – components that are key to enabling the digital, electrified, and increasingly assisted or autonomous experiences that customers today expect.

This left many automotive companies and their suppliers exposed. The globalized network that had previously enabled frictionless trade and operations around the world showed itself to be fragmented and fragile.

This vulnerability has implications for everybody – if automotive companies can’t get the parts and materials they need, then their workers can’t build cars to spec, and customers can’t get the cars they want (or they have to pay more for them). This has an impact on the bottom line for companies, it poses a threat to jobs, and it can lead to dissatisfied customers, who may choose to spend their money elsewhere.

Although the pandemic seems to be behind us, geo-political tension shows no signs of abating and – after a summer in which wildfires and floods ravaged large parts of the world, – the list of risks to automotive supply chain stability now includes climate-related incidents and shows no signs of shrinking. What’s equally concerning is that these major events are happening almost everywhere, and when they do, they can have a negative impact on many locations and on many aspects of production.

Based on my discussions with colleagues and industry experts in Munich, it’s clear that volatility cannot be dismissed as a temporary shadow on the industry – instead, we must learn to deal with volatility as part of a new normal for the industry and as a continued threat to supply chain stability.

This is reflected by the findings of our recent research paper on the automotive supply chain. Our research reveals that, although automotive companies have – to some extent – stabilized their supply chains and are now better prepared to deal with a ‘black swan’ event like the COVID-19 pandemic, this stability has mainly been achieved by increasing inventories and re-locating some operations and/or preferred points of supply to closer locations.

Although effective in the short term, these measures imply higher costs and/or investment of working capital, which means that they could compromise long-term competitiveness.

A concerning outcome of this could also be that other strategic objectives – such as sustainability – could be downgraded in favour of short-term priorities. Indeed, our report demonstrates that investment in sustainability has stalled among OEMs and suppliers, which makes for worrying reading, especially after the slower-than-expected progress we discovered in our 2022 CRI Report: From Ambition to Action¹.

The question is thus, How can automotive companies achieve long-term resilience while maintaining or enhancing their competitiveness and without compromising on sustainability?

Competition intensifies as new players arrive

The question of how to maintain or enhance competitiveness will take on increased importance after the IAA Mobility event, where it was impossible not to notice the presence of new market entrants, mainly from China. BYD, XPENG, and MG led the way, but there were many more, including some that I had not heard of before.

Having been at the Paris Motor Show last year and seeing the Chinese players take their first big step in the European market, the IAA Mobility event confirmed that their intentions to compete in Europe (and beyond) are serious. 

Generally, the quality of the products is good (which we perhaps would not have said 10 years ago) and there are some areas where these Chinese brands lead the way. For sure, they benefit from being ‘digital and electric first’, which means they are able to focus all of their efforts on one form of powertrain, without worrying about the ‘other’ part of their business.

The speed with which they have entered the market with credible products and continue to innovate in areas like the digital cockpit experience is truly impressive. Not every feature or function will be to the taste of European customers, but they provide food for thought and serve as a strong statement of intent to the established global automotive players.

The presence of Chinese automotive brands has stirred emotions here in Europe. Many customers will be pleased about the increased choice. Many carmakers will not be pleased about the arrival of strong competition, especially after the turbulence they have experienced in recent years. My personal view is that the arrival of these new players should serve as strong motivation for existing automotive brands and their partners to accelerate their own transformations and intensify their own innovation efforts.

Electrification, autonomous driving, and digitalization have shaken up the competitive landscape. The needs of customers – and society – today, are different. Demand for luxury and performance still exists, but there’s an increasing focus on practicality, economy, sustainability and circularity.

Traditional automotive manufacturers still have strong hands to play in this high-stakes game – established brand reputations, knowledge of the local market, strong dealer and service networks, mature supply chain ecosystems, and the trust of many customers. These are powerful assets that can help ‘traditional’ car makers offer strong and confidence-inspiring customer experiences in a way that new players cannot yet do.

And then, of course, there is the passion for automotive and mobility that established brands continue to display today. As a Frenchman who has had the pleasure of working with many leading French and European automotive brands, I can testify that the deep-rooted passion for automotive and mobility still burns strongly across Europe today. How established brands leverage this passion and their strong assets as part of their transformation will be key to their future success or failure.

Collaboration is key

As I reflect on the topics above, it is clear to me that collaboration is key to success in today’s fast-evolving automotive and mobility landscape. A key message that arose during my panel discussion with Oliver Ganser of BMW Group and Shan Liu of Aurobay is that “we cannot do this alone�.

This came in the context of the automotive supply chain and debates about how automotive companies can work more closely with their suppliers and the broader automotive ecosystem to create long-term resilience, but it rings true for almost every major trend taking place in the automotive industry today. Here are just a few examples.

1. Software-driven transformation and autonomous driving will be achieved more quickly if you collaborate with the right tech companies and partners. Semiconductor and chip companies, as well as software giants and hyper-scalers, are all active in automotive – partnerships can help automotive companies achieve their goals faster, save money, and create a competitive advantage 
2. Most automotive companies – established and new – have partnered with Chinese battery manufacturers. Not doing so would severely slow the transition towards electric mobility. New partnerships can play a key role in growing the battery industry in Europe and other regions.
3. Catena-X represents an ecosystem approach to enabling automotive OEMs and their suppliers to share data in a way that improves transparency, builds trust and will enable companies to reach their sustainability goals faster.
4. Partnering with start-ups or lifestyle companies (e.g. gaming or health tech) can help you add new products and services to your in-car and out-of-car experiences, and make vehicles an integrated and central part of the customer’s digital ecosystem.
5. Working with an enterprise IT solutions provider (e.g. SAP or Microsoft) and a company like ÎÚÑ»´«Ã½ can help you use data to drive your climate action planning and sustainability initiatives.

There is more happening in the automotive industry than at any time in the past century. The pace of change is relentless and the topics are complex and significant. In the past, any one of electrification, software transformation, sustainability or digital customer experiences would be a big enough challenge on its own. Today, they are all happening together. Collaboration is key to addressing these topics at the pace that’s required today.

Those companies that master the partnership play and the art of collaboration will be well-placed to thrive in the future.

Those who don’t? We may not see them at too many industry events in the future.

Learn more about the impact of continued volatility on the automotive supply chain and what automotive companies – OEMs and their suppliers – can do to achieve long-term resilience, without compromising on competitiveness or sustainability.

These big decisions will set corporate direction for years. They must properly analyse the full life cycle impact of any choice, whilst also considering systems outside of their control, from land, to energy infrastructure, to competition from other industries.

To take a top-level example, what is the most sustainable vehicle propulsion method – Electric, Hydrogen and E-fuels? We need to understand the full life cycle – by performing an integrative Life Cycle Assessment – in order to reliably make the comparison.

So we would need to look at the original fuel (eg energy mix of grid, power source for an electrolyser, or biomass) and its emissions profile. Then we’d need to look at the energy efficiency of each step between the energy inputs and the vehicle’s propulsion. Then you can compare how much of each you need to produce the same amount of propulsion.

We must also look at the inputs of creating the propulsion system itself – such as battery or engine components and materials.

We can then combine these to work out the most sustainable option. Maximum sustainability will need to address the fuel, the vehicle design and the energy systems that power it. The results will of course vary in different scenarios.

Making good decisions needs highly sophisticated system-of-systems modeling, combining your own engineering and supply chain models with climate, energy, demographic and macroeconomic models.

In our new whitepaper offer an introduction to planning strategic decisions for a sustainable transition, and provide top level worked examples of propulsion and battery choices, alongside some initial answers.