Manufacturers must produce more affordable cars. Technology will help them do that.

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Car manufacturers must embrace new technologies such as IoT, machine learning, and others to lower development time and costs.

Automakers need to strengthen their economic competitiveness. In recent months, news of layoffs at General Motors, Ford, Jaguar Land Rover, Nissan, and even Tesla have underscored that the industry needs to restructure the way it builds vehicles and the investments it needs to do so.

In an email to employees announcing the 7% cut in fulltime employee headcount in January 2019, Tesla founder Elon Musk laid bare the stark reality facing his company – and why it needs to find cheaper ways to build cars. “ … we face an extremely difficult challenge: making our cars, batteries, and solar products cost-competitive with fossil fuels. While we have made great progress, our products are still too expensive for most people,” he warned. “Tesla has only been producing cars for about a decade and we’re up against massive, entrenched competitors. The net effect is that Tesla must work much harder than other manufacturers to survive while building affordable, sustainable products.”

Meanwhile, GM Chairman and CEO Mary Barra, in her announcement in late 2018 of job cuts to reduce salaried and salaried contract staff by 15% (including 25% fewer executives), emphasized that GM’s move was about making it more competitive in a changing environment. “The actions we are taking today continue our transformation to be highly agile, resilient, and profitable while giving us the flexibility to invest in the future,” she said. “We recognize the need to stay in front of changing market conditions and customer preferences to position our company for long-term success.”

Simply put, GM knows it must adapt quickly. In her announcement, she called out the retooling the company needs to double the resources it puts into electric and autonomous vehicle development.

She also pointedly committed to “expanding the use of virtual tools to lower development time and costs.” Those virtual tools will go a long way to helping take costs out of the auto industry supply chain. They include:

  • Digital twin – As we outlined in this story in 2018, this is a digital representation of a physical object. It includes the model of the physical object, data from the object, a unique one-to-one correspondence to the object, and the ability to monitor the object. Digital twins are at the core of interoperability, allowing machines and people to communicate with each other more effectively and fostering transparency by creating a virtual copy of the physical world through sensor data. In short, they provide a deep and comprehensive view of what is happening in a manufacturing facility.
  • IoT (internet of things) – IoT technologies can make a huge difference in manufacturing, using sensors, cameras, and other smart devices to provide timely intelligence on the effectiveness of all manufacturing processes and producing the data needed to fine-tune them to deliver the best results.
  • Machine learning and analytics – As these and other new digital technologies are applied to manufacturing, they provide huge amounts of data about the effectiveness of factory equipment that can be used by machine-learning platforms to enable predictive maintenance and optimize manufacturing processes.

The goal of all this work is to help the industry shift to faster cycle times and produce vehicles more efficiently and at a lower cost.

To learn more about Capgemini’s automotive practice, contact Mike Hessler, North America Automotive and Industrial Equipment Lead, at michael.hessler@capgemini.com.

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