In this exclusive for Tech Crunch, Cosmo Tech’s Co-Founder and Executive Chairman, Michel Morvan, explains that the industrial metaverse is coming and why simulation will be key to its success.
The industrial metaverse is arriving to change the way that decisions are made and accelerate transformation for established firms in the face of new rivals.
With the capacity to represent each part of a real-world system in a virtual environment, the industrial metaverse is quite simply a universe of universes. At the nexus of the Internet of Things, artificial intelligence, and mixed reality, the industrial metaverse enables the creation of digital twins of places, processes, real-world objects, and the humans who interact with them. In this industrial metaverse, organizations can monitor their complex systems in real-time, interact with them, analyze them, and unlock the value trapped within them.
The industrial metaverse allows an executive to know precisely where their company stands at any point in time. Fueled with real-time data from sensors and connected objects, the digital twins in the metaverse pinpoint precisely the state of a system at a point in time. Success in one area or a decline in another can be identified near-instantly, and decision-makers can act swiftly to address a critical failure or reposition levers for growth.
Yet while all this real-time representation and the influx of data is already game-changing, what executives really want to know is still out of reach. Knowing what has happened or what is happening right now is far less valuable than knowing what will happen next. Decision-makers want to know what will happen in the future and, for that, the metaverse needs an additional transversal component: simulation.
With the addition of simulation, the metaverse adds an additional universe, the universe of time.
Simulation, and complex systems simulation in particular, enables organizations to test the impact of their potential choices on their systems and ecosystem, and to understand what lies ahead on any future timeline. The results of these simulations offer decision makers the possibility to test hundreds, thousands, even hundreds of thousands of different scenarios and to choose the tactics and strategies that lead to an optimal future for their organization. In other words, the addition of simulation to the metaverse enables companies to have clear visions of not only their past and present state, but their future state, too, whether that future is the next quarter-hour or the next 20 years.
For industry this is truly transformational. All the data from the past and present that these actors have collected, modeled, parsed, and analyzed only has real value in informing the best choices for the future. With simulation in the metaverse, they now have a means to harness this data and their digital twins to accurately predict what comes next.
What’s more, with continuous simulation, industry can run simulation upon simulation, each time drawing on the latest real-time data fed into the digital twins of their industrial metaverse. Simulations might be launched every five days, five hours, five minutes, even every five seconds to provide an always up-to-date picture of the system as it evolves.
All of this can be automated, too, so that a new simulation is launched on a regular schedule or when real-time data — say, sensor readings from a machine or production levels in a factory — hit a predetermined mark. Advanced simulations not only respond to the ‘what-if’ questions associated with a particular decision or an external event that might impact an organization but also generate plans that identify the ‘how-to’ steps to optimally realize a given goal. Even the implementation of this optimized plan can be automated and, of course, the impacts of any decision are captured in real-time for the next simulation to consider.
A continuous simulation capacity offers organizations four distinct advantages: the opportunity to answer every ‘what-if’ question in real-time, the chance to generate optimal ‘how-to’ plans, a means to continuously and automatically optimize a system, and the possibility to maintain continuous and holistic visibility of their entire system and its future.
And no organization benefits more from these advantages than established companies in the manufacturing sector or the energy and automotive industries still pursuing their digital transformation in the face of competition from newer, less-encumbered rivals.
Established companies face challenges to transform their complex organizations in a period of great uncertainty that new entrants to the market do not. With headcounts in the tens or hundreds of thousands and operations in countries worldwide, they have to manage a changing world with a corporate inertia their younger rivals avoid. Long histories often breed conservative approaches to risk and uncertainty that focus on sustaining market positions and reliable growth, something almost antithetical to the ‘move fast and break things’ culture of the startup world.
The automotive sector stands as an example of an industry where established companies are facing difficult transitions. The same companies that transformed production lines in the early 20th century and reimagined manufacturing processes in the 1970s and 1980s, now find themselves challenged on multiple fronts. ‘Just-in-time’ supply chains have been disrupted by a global pandemic, software engineers are as important to vehicle design as mechanical engineers, and the days of the internal combustion engine (ICE) that have sustained the industry from its birth are numbered. Automakers find themselves faced with making a once-in-a-lifetime transition from ICE vehicle production to battery-powered electric vehicles yet lack clear deadlines for change. Consumer sentiment is shifting — sometimes faster than regulators can or will act — and choosing the right path through all this insecurity is essential for the survival of the firm.
In contrast, more recently established all-electric firms face few of these challenges. With no legacy ICE vehicles to remove from production lines and already embraced by environmentally conscious consumers and emissions-wary governments alike, they are perfectly positioned to expand their reach in the shifting auto landscape. It’s little wonder that Reuters reports that Tesla, launched in 2003, has a current market capitalization that eclipses that of its five largest rivals Toyota (founded 1937), Volkswagen (1937), Daimler (1926), Ford (1903), and GM (1908).
In a simulation-powered metaverse, however, lies the key to future-proofing for these companies — and many more besides.
The metaverse will give these long-established firms the ability to accelerate their transformation while leveraging the assets they’ve built over decades. They will have an opportunity to understand their own complex organization at different levels of granularity at different scopes of space and time. They will have a chance to experiment with their simulation digital twins and continuously project themselves into the future.
The industrial metaverse powered by simulation will allow them to better manage uncertainty about their existing capabilities, the demand for their legacy products, and the shifts in market and regulatory sentiment. They will identify new ways to leverage the assets they’ve developed over decades such as logistics and supply chains and the capacity to manufacture millions of products on a global scale.
Most of all, they will have the capacity to choose the best strategies to advance their interests and adapt those strategies in real-time to accelerate and truly transform. This is the promise of an industrial metaverse where the investment in simulation unlocks present-day value while bolstering confidence in the strategies that will deliver tomorrow.
