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Digital Transformations Threatens to Turn the Automotive Business on Its Head

Consumer pressures for smart, autonomous and sustainable cars are fueling the automotive trends towards complexity, efficiency and ever-shrinking development times. For example, Elon Musk shook up the spotlight with his smart, green, electric vehicles. The man has controversies , but he also continues to make the news by pushing the market towards autonomy. For the automotive industry to meet these and other future needs, engineers have increasingly made yearly models into ever more complex ‘computer on wheels.’

But the digitalization of the automotive market doesn’t just live in the cars themselves. The entire lifecycle of the automobile is becoming digital. For instance, engineers are utilizing digital tools, such as simulation, to optimize every aspect of the car—from its design and manufacturing to the user experience.


(Image courtesy of Siemens.)


“Sustainability will drive electrification and the elimination of waste from a manufacturing standpoint,” says Nand Kochhar, VP of Automotive and Transportation Industries at Siemens Digital Industries Software. “That’s what the consumer is thinking about. The user experience and comfort features are what people are looking for. The desire to stay connected through cell phones, TV and now in the automobile. You can’t do the pen and paper design of an automobile like that.”

As cars become more complex and optimized, this has also inadvertently created another trend pushing automakers towards digitalization. Car makers must battle to produce increasingly complex and optimized automobiles on shrinking timelines. By digitizing the development processes, automotive manufacturers aim to streamline it as much as possible. So much so that it could turn the automotive business model completely on its head.

Smart Cars Bring Digital Transformations to Manufacturers

The electrification of the automotive industry is something of a struggle. It’s not like manufacturers could take their traditional vehicles and simply add a battery, computer and electric motor to it. The design of automotive electrical systems brings with it complexities the industry has only recently started to tackle.

“If you replace your source of power with batteries, you now have to do battery optimization. So, it is different from plugging in the two pieces,” says Kochhar. “Same with manufacturing. You’re dealing with high voltage and that becomes complicated, as well.”

A good way to see this increasing complication, from the driver’s point of view, is with the right-to-repair issue. A mechanic can’t always stick their head under the hood and visually see what’s wrong anymore. They require certified diagnostic equipment and training to find the issue. It could be mechanical and straight-forward, but it can just as easily be something invisible to the naked eye—such as a faulty chip or a sensor misalignment.

These issues can affect complex safety features; for example, consider blind spot monitoring. To get the system operational, the car will need sensors to detect nearby traffic, pedestrians and objects. But the sensor alone isn’t enough; the data it collects must be processed and analyzed in real-time to assess for danger. Then logic systems must compute the best course of action to avoid that danger: will a light notify the driver, or will an automated advanced driver-assistance system (ADAS) take control of the vehicle? If this system is faulty, it could cause an accident instead of preventing one.

As Kochhar puts it, “if a consumer demands these features, then you need a higher degree of electronics and software in the products. Adaptive cruise control is a feature people want to improve driving on the highway. It’s a comfort and safety thing. This comes with all the electronics and software to use it.”


(Image courtesy of Siemens.)


To develop, design and calibrate that sensor and logic system, it doesn’t just require computational power in the car—or in the machine shop. It also involves the engineers designing the car, and designing these features, who must use digital tools that were only recently introduced to the automotive industry.

Keeping track of those new digital tools—and the data and processes they are coupled with—can be overwhelming. Product lifecycle management (PLM) systems are a potential solution to collate those tools into a single source of truth.

Digital Transformations Optimize the Mechanical Lifecycle of Cars

To fully understand other factors pushing the automotive industry towards digital transformations and PLM software, consider a modern, fully mechanical car. Similar examples would be the extreme-budget car the Tata Nano, or a modern 50’s-style car for the DIY market. To be competitive with digitized cars it would need to be budget friendly, fuel efficient and powerful. To optimize and balance those three competing conditions, engineers would rely on an extensive number of digital tools.

“You still can use PLM tools to efficiently design the base car, reuse part data and keep track of versions of the car,” says Kochhar. “With the Tata Nano, a lot of the studies and innovation went into the car. It’s what’s called frugal engineering. To keep the costs down the team had to leverage a lot of digital tools.”

So, even though the car may have no sensors, smart features or digital systems, engineers will still use CFD simulations to optimize its aerodynamics, or topology optimization to minimize part weight without affecting safety. The benefit of both would be a more fuel-efficient car with a higher power-to-weight ratio. Not to mention the fact that a lighter car will naturally be less expensive as it utilizes less material.

“You need the basic digital design, simulation, manufacturing and data management technologies to design a base model car,” says Kochhar. “Then you’ll be able to deliver the most efficient car in a short period of time. It’s cost effective and you deliver on target.”

Kochhar adds another way PLM tools can improve efficiency and keep costs down. The team can utilize PLM software to keep track of its data to better reuse models and manufactured parts, from one yearly base model to the next. As previously mentioned, this reuse has been done before. However, by keeping data accessible, searchable and in one location, the reuse philosophy can be fully used.

Development Times Push Automotive Digital Transformations

When cars were less complicated, the concept of a yearly releases wasn’t as daunting as it is now. Between most yearly models, the base design didn’t change much. Engines might be improved; a new flair might be added to the frame and the interior might look fancier. But from year to year, many parts were interchangeable and feature creep was minimal.

Unfortunately for automotive designers, the concept of the yearly release isn’t going away—at least not right away. However, the complexity and optimization of each model year is ever increasing. The extra work needed to meet these goals still needs to happen within a yearly timeline. To accommodate this, many manufacturers have started to digitize various processes in the product lifecycle.

Using PLM and other digital tools, companies have been able to keep pace with shrinking development times. However, this isn’t likely to be sustainable. As a result, companies are looking towards process changes as well. “Automotive companies are finding ways to keep their base hardware platform fixed,” says Kochhar. “So now, when offering new features, a lot of that will be done through software updates.”

This isn’t just something coming in the future; Kochhar says that some progressive companies are already doing it. “You keep the hardware, and the new feature is updated over the air. You don’t have to leave the comfort of your home. It will happen more and more in the industry. There is a limit to where you need to offer hardware upgrades.”

In other words, automotive companies are increasingly becoming software companies. Just as Apple regularly offers new phones, there is an equal amount of buzz around the company offering new operating systems. The release cycle of each is a bit more flexible.

A similar business model appears to be developing within the automotive industry—and companies could make a lot of money with it. Nothing says those software updates must be free. If you buy a future car that is loaded with advanced sensors and computational power, it might have all the hardware needed to become fully autonomous; at that point, only the software would be missing. People are likely to pay a pretty penny to upgrade those ADAS systems into fully autonomous ones.

“Just imagine several hundred units all getting new software and keeping track of which hardware has which version,” Kochhar explains. “You need a digital ID for the car. That wasn’t done in the past. Overall, product and software lifecycle management will become very important.”

Advice for the Digital Future

Digital change has hit beyond the automotive world. Not a day goes by without hearing about another smart, automated product or system. To engineers designing these platforms, the change can seem daunting.

However, Kochhar is more optimistic. “That’s where we talk about turning complexity into a competitive advantage. Don’t be scared of complexity. Just get the tools to handle it.”

As for getting those tools and implementing them, it’s important to acknowledge that it’s a process. “See where you need to start, what offers the most value to the organization,” Kochhar says. “It’s important to start the transformation at that point and build out towards the entire business from there.”

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