Nissan Embraces Gigacasting Process To Bolster Its Efficiencies: Report

Nissan Motor reportedly announces a bold shift towards gigacasting, mirroring Tesla's innovation strategy. By 2027, Nissan aims to roll out EVs boasting rear floors crafted with 6,000-ton force machines, slashing production costs by 10%.

Nissan Motor Co (OTC:NSANY) (OTC:NSANF) reportedly plans to adopt gigacasting, a manufacturing technique popularized by Tesla Inc. (NASDAQ:TSLA), to produce components for its electric vehicles (EVs).

Nissan will utilize machines exerting approximately 6,000 tons of force to fabricate the rear floors of EVs slated for sale starting fiscal year 2027, reported Bloomberg.

This move aligns with the automaker’s goal to utilize innovative methods in reducing production expenses, thereby enhancing competitiveness in the burgeoning EV market.

By implementing this process, the company anticipates a 10% reduction in manufacturing costs, as per the report.

This strategic initiative forms part of Nissan’s broader agenda to achieve cost parity between electric and traditional combustion-engine vehicles by fiscal year 2030.

Gigacasting involves the use of large-scale machinery to cast entire chassis sections in a single step, eliminating the need for assembling numerous individual parts through welding.

This approach, championed by Tesla, promises significant savings in time, equipment, labor, and expenses.

As per the report, Japan’s Toyota Motor Corp (NYSE:TM) has also committed to adopting gigacasting, further highlighting its industry-wide appeal.

“We have been using casting boards for front air conditioner structural parts at our Tochigi plant for more than 10 years,” the report quoted Hideyuki Sakamoto, executive vice president for manufacturing and supply chain management. 

Nissan is “quite experienced in casting car structures,” he said at the company’s plant in Yokohama, as per the report.

Price Action: NSANY shares closed lower by 1.31% at $7.55 on Monday.

Disclaimer: This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors.

Photo by michelmond on Shutterstock

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