Can artificial intelligence reduce vehicle time to market?

The automotive industry has long grappled with the challenge of time to market, facing numerous hurdles from conception to product shipment. With the advent of connectivity features and electrification, this process has become even more complex, extending design and manufacturing timelines. Notably, Jabil reported a substantial increase in product development cycles among automakers, with 96% of them now experiencing cycles longer than 24 months, compared to just 29% in 2018.

To address this issue, Ignazio Dentici, Vice President of Global E-mobility and Automotive at Hexagon, asserts that AI holds the potential to revolutionize the entire product development cycle. As vehicles become increasingly complex, AI emerges as an indispensable tool for automakers, offering insights and efficiencies across various stages of design and production.

The proliferation of electric vehicles (EVs) underscores the need for AI-driven innovation. Unlike traditional internal combustion engine (ICE) vehicles, EVs present novel challenges due to their unfamiliar technology. Consequently, design and manufacturing processes may be prolonged, especially for OEMs initially hesitant to embrace EVs. In a rapidly evolving landscape, delays in time to market can render products obsolete upon release, underscoring the urgency for faster cycles.

Dentici highlights the role of AI in expediting vehicle design through simulation. Leveraging data is essential in modern vehicle design, particularly in simulation, where comprehensive datasets lead to more accurate results and significant time savings. By creating digital twins and employing generative AI, automakers can stress test virtual designs and iterate more efficiently, mitigating delays caused by redesigns later in the development cycle.

Moreover, AI enhances manufacturing efficiency by harnessing data insights to optimize production processes. By monitoring existing processes and collecting quality data, automakers can identify areas for improvement and implement predictive maintenance strategies. Additionally, AI facilitates collaboration between OEMs and suppliers by providing valuable data insights, thereby streamlining supply chain operations and mitigating potential supply issues.

While Dentici acknowledges the challenges of implementing AI, he remains optimistic about its transformative potential. As automakers continue to invest in AI-driven solutions, building the necessary skill sets and infrastructure will be paramount. Moreover, securing talent in an increasingly competitive job market is crucial for realizing AI’s promise in the automotive sector.

Overall, Dentici envisions a future where AI plays a central role in vehicle design and production, with significant investments driving innovation in the industry. If AI can deliver on its promise to reduce time to market, these investments may prove to be worthwhile, paving the way for a more efficient and competitive automotive landscape.


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