The automotive sector, long viewed as a crucible for engineering efficiency and cost management, has long been at the forefront of evolving technologies aimed at reducing costs and product development time, such as the ‘Virtual Twin’. Other industries, such as energy, infrastructure, and construction, are now borrowing such technologies and concepts, to manage complexity and reduce timelines, most notably, the energy and infrastructure sectors.
Need For Speed
For decades, auto makers have faced relentless pressure to deliver highly complex products at consumer-friendly prices, thereby pushing themselves as among leading industries in the manufacturing sector in terms of productivity efficiency. The core technology driving this transformation is the Virtual Twin, which is a comprehensive digital replica of a vehicle, factories, or manufacturing and other processes.
As other heavy industries such as energy, infrastructure, and construction seek to streamline processes and maximize output, they are turning to this technology, says Olivier Sappin, CEO of CATIA at Dassault Systemes. Engineers use the virtual twin to anticipate how a vehicle will behave in the real world, eliminating the need for expensive and time-consuming physical prototypes to validate design.
According to Sappin, the gains are staggering. Historically, when designing complex parts like the vehicle body structure or chassis, engineers could typically only study two or three design alternatives because validating each option checking for safety compliance, noise, vibration, and harshness (NVH) would take months, often four to five months per alternative.
Today, the integration of specialized artificial intelligence (AI) with modeling and simulation tools has changed the game entirely. The turnaround time for validation has shrunk from months to mere hours.
“In some of the design loops, the productivity factor is 10,” Sappin stated, clarifying that people are achieving a 90% reduction in time. OEMs are now able to explore hundreds more alternatives than they could before.
The efficient manufacturing practices are now being ported to sectors where complexity and precision are equally critical, such as energy, infrastructure, railway and construction.
While the automotive industry uses the Virtual Twin approach to model the vehicle itself, this concept extends to creating a “virtual twin of the enterprise” or the “virtual twin of the business”. This capability allows non-engineering functions, such as sourcing managers, purchasing agents, and HR, to use the platform to optimize operations.
For instance, companies can model their global business to manage supply chains and anticipate the impact of geopolitical challenges like tariff changes. Sappin explained that the platform can run “what if” scenarios, such as anticipating the impact of raw material price inflation on existing products and exploring alternative sourcing options.
Ultimately, whether optimizing the design of a new electric car or improving efficiency in energy infrastructure, the foundational principle remains the same: a model-based approach where engineers leverage sophisticated platforms and AI for industry, where accuracy and precision are mandatory to make better decisions and be more agile