Why the Automotive Industry Needs Data Analytics to Survive Digital Transformation
Digital transformation has disrupted the automotive industry with new technologies and challenges, while recalls are growing in number and cost.
“The vehicle electrification market is projected to grow at a CAGR of 11.9% to reach USD 129.6 billion by 2025 “
From cloud computing and artificial intelligence to electrification and autonomous driving, the automotive industry is evolving at a fast pace as new innovations are introduced every year. The cloud market alone in the automotive industry is forecasted to reach $66.95 billion by 2022, vehicle electrification is expected to hit $126 billion by 2025, and the predicted CAGR of the global autonomous vehicle market is predicted at 18.06% between 2020 and 2025 after being valued at $24.1 billion in 2019.
In today’s fast-paced age of innovation and data, automotive manufacturers understand the importance of leveraging data to gain an advantage and are beginning to adopt technologies that rely on the latest cutting-edge innovations such as AI, 5G networks, and cloud computing. Nevertheless, many companies are still in the early stages of planning and deploying Smart Manufacturing systems. While this is a step in the right direction, most teams are still focused on process not product performance, and analyzing data in silos instead of holistically.
“ Hyundai’s recall of 82,000 electric cars is one of the most expensive in history - On a per-vehicle basis, the average cost is $11,000 ”
Despite impressive technologies, vehicles are still failing, leading to expensive recalls as a result of blind spots. Most recently is a Hyundai recall of 82,000 electric vehicles following reports of battery malfunctions. While the number of recalled units is considered small, the price per recalled vehicle is one of the most expensive in history at $11,000. Compared to GM’s recall of seven million vehicles due to deadly air bag defects at under $200 per vehicle, 11k is astronomically high.
Attractive opportunities in the Vehicle Electrification Market
Asia and Oceania is estimated to be the largest market for vehicle electrification as China, Japan, India, and South Korea accounted for 50% of global vehicle production in 2019
Note: This market represents the vehicle electrification market, excluding 48V vehicle market.
73.7
USD Billion
2020-e
2020-e
129.6
USD Billion
2020-e
2020-e
CAGR of11.9%
The vehicle electrification market is expected to be worth USD 129.6 Billion by 2025, growing at a CAGR of 11.9% during the forecast period.
The market growth can be attributed to stringent government regulations for emissions, fuel economy standards, and increase in demand for reliable electrical systems.
Acquisitions, expansion, and investment will offer lucrative opportunities for market players in the next 5 years.
The market is also driven by increasing govemment incentives and promotional policies for electric vehicles.
Slowdown in the global vehicle production may restrain the vehicle electification market.
Blind Spots in
Automotive Manufacturing
Automotive manufacturing processes today are filled with blind spots that lead to costly recalls. Adopting the latest technological innovations has led to a decentralized production chain while introducing millions of new components that need to work perfectly together. OEMs have limited visibility into what’s going on throughout the entire production chain, which includes their own factories, Tier 1s, and many additional suppliers of mechanical and electronic components and software. Data is unreliable and siloed in manufacturing machines and production lines as well as vehicles’ systems and components. Communication between these silos is limited and difficult to analyze for actionable insights. Additionally, this data is often not available in real time, which is critical in time sensitive situations that require fast decisions. Meanwhile, a defect in a component anywhere along the production line can’t be identified and tracked, leading to significantly more issues by the time the vehicle hits the road.
Things are only going to get more complicated as the industry evolves. According to the ‘Automotive software and electronics 2030’ report by McKinsey & Company, “The days of OEMs comprehensively defining specifications and suppliers delivering on them may be nearing an end. Neither OEMs nor traditional suppliers are in a position to fully define the technology requirements of new systems. Co-development between OEMs and suppliers is expected to become not just prevalent but necessary.”
OEMs are aware of the problem and invest massive budgets in simulations and testing processes at the design and manufacturing stages, but are often lacking visibility into the quality of the components they are using. At SEMICON Europa 2015, Audi illustrated the scale of the problem by pointing out that with 4,000 cars coming off the line each day, each with 7,000 semiconductor devices, a rate of one defective component per million would still translate into one defective vehicle leaving the production line every hour.
Using Data to Identify & Prevent Blind Spots
Industry 4.0, also called The Fourth Industrial Revolution, involves automation and data analytics, i.e., smart technology, in traditional industrial and manufacturing practices. Production processes today create vast amounts of data, which needs to be collected, connected, and analyzed in order to identify when something is not behaving as it should. Typically, automotive companies use data analytics for the production process, with a focus on production and capacity-related metrics such as throughput. For example, many factories are looking to use data to implement predictive maintenance to maximize equipment uptime. There is an assumption that if the machines are all working OK, the products will also be OK.
From our experience working with automotive tier 1 suppliers, subtle interactions between components caused by minor variations in the manufacturing process can cause significant issues later on. Many of these issues can only be identified by approaching the problem holistically, collecting and modeling data across manufacturing silos while using end-to-end tracking to ensure that data is reliable and without gaps. Once the data can be efficiently collected, it needs to be analyzed and translated into machine AND product insights, expanding the focus from the machines to the products. That’s also where artificial intelligence and machine learning technologies come in, enabling real-time analysis of big data and automated decision-making processes. This opens the door to truly “smart” manufacturing where data analytics can drive actions on the manufacturing floor in real time.
Eliminate Blind Spots with NI
If you are already thinking about data and analytics, that’s the right first step. Now it’s time to ensure you have real-time access to a holistic data set and are using it to make product, not just process, optimizations. This is the only way to prevent increasingly costly recalls in an ever-evolving automotive landscape.
NI provides OEMs with an end-to-end AI and big data analytics platform, fully equipped with the tools they need to get actionable product insights. Operating on premise or as a cloud solution, NI’s platform integrates directly into OEM’s systems and machines throughout the supply chain, and acts as a unified data model, bridging the different data sources and collecting the data in one place for analysis. By deploying AI-powered big data analytics, OEMs can automate smart decision making, accelerate time to market, streamline manufacturing processes, and significantly reduce recalls.
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