Technology-driven trends and growing customer expectations have combined with new advances around the internet of things (IoT) and artificial intelligence to induce digital transformation in the automotive industry. This trend took center stage post the pandemic-led lockdowns when everything from product design process to manufacturing, maintenance to sales and marketing saw an acceleration. Of course, the original shift came two decades ago with the founding of Tesla, which became the most valuable car-maker in June 2020. Their massive investments in R&D, focus on vehicles encouraging sustainability through electric vehicles and self-driven cars put Elon Musk and his ideas in pole position. What’s more, Tesla also became the most valuable carmaker, valued at 60x over giants like General Motors.
In addition to this tectonic shift, automotive manufacturers have gravitated towards digitally transforming their processes to enhance manufacturing uptime and cycles, improve resource efficiency, tighten cybersecurity, and support the agility required for autonomous and electric vehicles. Specifically, automotive companies are focusing on digitizing data, processes, connectivity, and cybersecurity to attain the above advantages aimed at redefining customer value propositions and positioning operations to deliver that value
A thought paper from IBM puts the entire onus of this change on the shift in customer expectations, which pressured automakers to change the way they establish their strategies and manage their organizations. “New requirements to incorporate information and interactivity quickly drive up costs and complexity. At the same time, the auto industry must be more creative to capture a larger share of the consumer’s attention and overall transportation spend – both in and beyond usage of vehicles,” says the note. Of course, there are other roles that digitization plays in the industry, such as helping automakers capture a strong market and developing products that comply with changing environmental regulations. Activities such as deep learning algorithms, process mining, task mining and robotic process automation have driven the change towards better quality and productivity, lower operating costs and an optimized supply chain.
Key Drivers of Digital Transformation
So, what exactly is driving this widespread adoption of digital technologies in the automotive sector? Without doubt it is the growing customer expectation. Late last year, McKinsey came out with a report aptly titled “Smartphones on wheels: New rules for automotive product development” wherein it said, “moving from a conventional car to an automotive ecosystem — a kind of smartphone on wheels — requires changes to the vehicle’s electronics and software architecture. That means shifting from the traditional use of scattered, embedded electronic-control units (ECUs) to a domain-focused system with central vehicle controllers.” This requires more sophisticated software, ethernet usage and connectivity at scale, besides better microprocessors that boosts performance, reduces power consumption and centralizes the automobile’s controls. Here are some immediate use cases:
So, what exactly is driving this widespread adoption of digital technologies in the automotive sector? Without doubt it is the growing customer expectation. Late last year, McKinsey came out with a report aptly titled “Smartphones on wheels: New rules for automotive product development” wherein it said, “moving from a conventional car to an automotive ecosystem — a kind of smartphone on wheels — requires changes to the vehicle’s electronics and software architecture. That means shifting from the traditional use of scattered, embedded electronic-control units (ECUs) to a domain-focused system with central vehicle controllers.” This requires more sophisticated software, ethernet usage and connectivity at scale, besides better microprocessors that boosts performance, reduces power consumption and centralizes the automobile’s controls. Here are some immediate use cases:
Digital Twin is a virtual representation of a physical system that can be used to simulate and analyze the system’s behavior. The use of digital twins in the automotive industry can improve the design and development of vehicles and enhance the testing and validation of new technologies. With a compound annual growth rate of 31.34%, the digital twin industry is expected to be worth US$ 71.9 Billion by 2028.
Industrial IoT (IIoT) refers to integrating internet-connected devices in industrial systems. The use of IIoT in the automotive industry can improve the manufacturing process’s efficiency, reliability, and sustainability. It helps improve efficiency and reliability of the manufacturing process by integrating internet-connected devices and enabling real-time monitoring and control of production. It enhances sustainability by enabling optimization of energy usage and reducing waste and improves safety by providing real-time alerts for potential hazards and enabling remote monitoring of dangerous environments.
Machine Vision enables machines to interpret and understand visual information. The use of machine vision in the automotive industry can improve the quality control process, enhance the safety of vehicles, and enable new applications such as autonomous driving. By 2030, the worldwide machine vision industry is predicted to be worth USD $25.92 billion. The technology helps improve the entire quality control process, enhances safety via real-time data inputs and cuts costs by reducing human involvement and optimizing production lines.
Augmented & Virtual Reality (AR / VR) create immersive and interactive experiences in the automotive industry to enhance customer experience, improve L&D activities and enable new applications such as virtual showrooms and digital test drives.
Automated Guided Vehicles (AGVs) or mobile robots help transport material and products in the industrial and manufacturing environments. The use of AGVs in the automotive industry can improve the efficiency and reliability of the manufacturing process and reduce the costs associated with manual labor. In addition, it also enhances safety by reducing human interventions in high-risk tasks
The need for an entire ecosystem that is agile
Given the above use cases, there is an immediate need to build agility not just at the plant-level but across the entire automotive ecosystem. Be it OEMs or suppliers, they need to shift their R&D processes and operating models from hardware engineering to a combination of software and technology-driven systems engineering. In other words, upgrades occur right through a vehicle’s life cycle, including over-the-air updates to fix bugs, include software features, enhance customer experiences or add features that aren’t available at the point of sale.
Moreover, the growing shift towards electric vehicles caused by the market and regulations are resulting in fresh requirements coming up at a fast pace – be it new powertrains, thermal management and heating systems, ventilation and air conditions or new infotainment panels. These challenges require automakers and suppliers to shift focus in product development capabilities, processes, and operating models. From mechanical engineering to electricals and electronics, software and data engineering. And, all of these needs to happen in the Asian economies at scale as the centers of gravity shifts from global to regional auto markets. Listed below are some use cases for digital transformation.
Product design has been on the radar for designing and developing connected and auto-driving vehicles for some time now. Ever since Tesla embraced this concept, there is an upswing in other auto majors following suit. In fact, research indicates that the market for autonomous vehicles will expand to USD 15.55 billion by 2030, representing a CAGR of 31.19% between 2023 and 2030. Now designers can leverage machine learning to create better vehicles leading to predictions that by 2026, the market size for automotive AI could reach $7.78 billion, growing at a CAGR of 40.79%.
Manufacturing robots have moved from being a good-to-have initiative to a must-have in the automotive industry, with each year about 400,000 new robots getting introduced with total global revenues for industrial robotics automation pegged at $43.8 billion. This optimization of the manufacturing process has brought in a reduction in waste, improved productivity and an ability to monitor output in real time. BMW implemented machile learning on its production process and reported a 5% reduction in quality issues.
Predictive maintenance is another area gaining popularity, given that it helps automakers reduce downtime and enhance reliability of their vehicles. Consulting firm Next Move Strategy Consulting projects a massive expansion of the worldwide predictive maintenance business between 2020 and 2030. The market is expected to grow to $64.3 billion by 2030 from its predicted value of $4.5 billion in 2020. The same, when applied to manufacturing, helps identify potential issues. Volkswagen used a SaaS option to monitor their conveyor motor to boost production efficiency for large-scale car assembly and assembly of electric vehicles by 30%. Markets and Markets, the global predictive maintenance market is expected to reach USD 15.9 billion by 2026, at a CAGR of 30.6% during the forecast period.
Sales could be described as the final frontier of how digital transformation could revolutionize the universe of customers. We have seen how interactive showrooms offer customers an immersive experience with AR and VR helping them get a better grasp of what the automobile is capable of. BMW’s showrooms now allow customers to see a virtual representation of the car they are interested in purchasing, paving the way for more informed decision-making.
Brace up for more challenges
While the future looks ready for a digital revolution in the automotive business, there are some challenges that the industry must brace itself for. The most important of these being the massive capital infusion that is required for this turnaround. With tight budgets and limited resources, automakers may find it tough to shift gears on the digital transformation journey. And the reasons aren’t tough to fathom. Benefits of a digital transformation on productivity and objectives aren’t apparent in the short term. This means, the analytics that align stakeholders towards such a move slows down, which in turn impacts infusion of fresh funds.
Other challenges include data privacy, given the connected nature of modern automobiles. Personal data protection becomes an overarching concern as auto companies and insurers gather massive amounts of customer data and vehicle data to enhance features while designing new products. Added to this is the additional complexity involving the revamp of existing business models and customer definition. This takes into account amendments to the operations for effective transformation and therefore generates additional complexities in the digital transformation processes.
In order to overcome the challenges, industry needs to improve the factors that help smooth the transformation from a labor-intensive to a more digitized manufacturing unit in order to drive economies of scale and enhance the productivity of the operations to a greater extent. By 2028, the globally connected car industry is expected to be worth USD 191.83 billion, with major players like Audi and Ford Motors focusing on fostering the uptake of cutting-edge automotive technologies. Around the same time, the global market for electric vehicles is projected to be worth USD 1,318.22 billion, with a CAGR of 24.3%.
Keeping these numbers in mind, the automotive industry needs to collaborate more closely with its supply chain, making it essential to optimize systems and processes that allows them to stay ahead of competition and meet user needs at a customized level.
