Digital tools have already transformed product development. Multiphysics simulation tools allow engineers to evaluate more options more quickly, improving product performance while reducing development time and costs. Today, artificial intelligence and machine learning (AI/ML) technologies have the potential to change the game again, promising faster time to market, better product performance, and disruptive improvements in simulation speed.
In 2023, a McKinsey survey conducted in partnership with NAFEMS showed that technological advances, changing market conditions, and increased confidence in advanced engineering simulation tools are shifting user priorities. Respondents told us that improved time to market is the primary value driver for the use of simulation in their organizations, surpassing better product performance for the first time.
That survey also revealed a high level of interest in the use of AI/ML simulation tools. Two-thirds of respondents said they had used AI/ML simulation, but only 5 percent reported using these technologies at scale. By comparison, the application of traditional simulation tools is ubiquitous in engineering organizations (99 percent), with just over half of respondents using them at scale.
Our previous analysis showed that engineering simulation has entered a dynamic phase of development. But it left some important questions unanswered: What types of problems are users solving with simulation? At what level of maturity? How well are simulation tools integrated into product development processes and engineering workflows? Where should companies concentrate their investments in digital product development and simulation to secure the best returns?
To address these gaps in knowledge, we collaborated with NAFEMS on a new study designed to take a deeper look at the use of simulation within a single industry. We chose the automotive industry for this analysis because its companies deal with a wide range of engineering problems across multiple domains and because our previous work has shown that the automotive industry is a leader in the adoption and integration of simulation tools, especially those using AI and machine learning.
Our study reveals rapid but uneven progress, with some engineering domains and application areas benefiting more than others. We also found dramatic differences in adoption, growth, and business impact.
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