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by EOS Intelligence EOS Intelligence No Comments

Automotive Industry Gearing towards Digital Transformation with AI

Artificial intelligence (AI) has become an integral part of almost every industry and the automotive sector is no exception. From self-driving cars to predictive maintenance, AI is evolving as a major disruptor in the auto industry, slowly transforming how automobiles are designed, manufactured, and sold. This digital swing is driven mainly by an increased competition, consumer preferences for smart mobility, and benefits of AI. However, AI adoption in the automotive industry is not mainstream yet, with the technology deployed only at the pilot level and in selective business segments. As the world gears toward an era of digital transformation and automation, AI is expected to be part of various business processes in the automotive industry in the coming years.

Artificial intelligence in the auto industry is typically associated with autonomous and self-driving cars. However, the technology has increasingly found its way into other applications over the last few years. Leading auto OEMs are showing an interest in deploying AI-driven innovations across the value chain, investing in tech start-ups, partnering with software providers, and building new business entities.

For instance, a venture capital fund owned by Japanese automaker Toyota, Toyota AI Ventures (rebranded as Toyota Ventures now) with US$200 million in assets under management, invested in almost 35 early-age startups that focus on AI, autonomy, mobility, and robotics between 2017 and 2020. Similarly, in 2022, South Korean automotive manufacturer Hyundai invested US$424 million to build an AI research center in the USA to advance research in AI and robotics. In the same year, CARIAD, software division of the Germany-based Volkswagen Group, acquired Paragon Semvox GmbH, a Germany-based company, that develops AI-based voice control and smart assistance systems, for US$42 million.

Changing consumer preferences, competitive pressures, and various advantages of AI are driving this transformation. According to a 2019 Capgemini research study, nearly 25% of auto manufacturers in the USA implemented AI solutions at scale, followed by the UK (14%), and Germany (12%) by the end of 2019.

There are numerous applications of AI in the automotive industry. Some of the more common and innovative uses of AI include virtual simulation models, inventory management, quality control of parts and finished goods, automated driver assistance systems (ADAS), predictive maintenance, and personalized vehicles, to name a few.

Automotive Industry Gearing towards Digital Transformation with AI by EOS Intelligence

AI-based virtual simulation models used for effective R&D processes

Due to changing customer preferences, increasing regulations concerning safety and fuel emissions, and technological disruption, OEMs are finding it more expensive to make cars nowadays. A 2020 report by PricewaterhouseCoopers says that conceptualization and product development account for 77% of the cost and 65% of the time spent in a typical automotive manufacturing process.

To make R&D cost-effective and more efficient, some auto manufacturers and tier-I suppliers are turning to AI. AI enables simulation of digital prototypes eliminating a lot of physical prototypes, thus reducing the costs and time for product development. One interesting concept that is emerging and catching attention in this area is the “digital twin”. The concept employs a virtual model mimicking an entire process or environment, and its physical behavior. There are numerous uses of digital twins – in vehicle design and development, factory and supply chain simulations, autonomous driving simulations, etc. In vehicle design and development, digital twins make simulations easier, validate each step of the development in order to predict outcomes, improve performance, and identify possible failures before the product enters the production line.

For instance, in 2019, Continental, a Germany-based automotive parts manufacturing company, entered into a collaboration with a Germany-based start-up, Automotive Artificial Intelligence (AAI), to develop a modular virtual simulation program for its Automated Driver Assistance System (ADAS) application, and also invested an undisclosed amount in the company. The virtual simulation program could generate phenomenal vehicle test data of 5,000 miles per hour compared to 6,500 miles of physical test driving per month, reducing both time and costs.

Many leading automotive companies are also looking to utilize this innovative concept in streamlining the entire manufacturing operations. For example, in early 2023, Mercedes-Benz announced that the company is partnering with Nvidia technologies, a US-based technology company specializing in AI-based hardware and software, to build a digital twin of one of its automotive plants in Germany. Mercedes-Benz is hoping that the digital twin can help them monitor the entire plant, and make quick changes in their production processes without interruptions.

General Motors, Volkswagen, and Hyundai use AI for smart manufacturing

Automation processes and industrial robots have been in automotive manufacturing for a long time. However, these systems can perform only programmed routine and repetitive tasks and cannot act on complex real-life scenarios.

The use of AI in automotive manufacturing makes these production processes smarter and more efficient. Some of the applications of AI in manufacturing include forecasting component failures, predicting demand for components and managing inventory, using collaborative robots for heavy material handling, etc.

For instance, General Motors, a US-based automotive manufacturing company, has been using AI-based design strategies since 2018 to manufacture lightweight vehicles. In 2019, the company also deployed an AI-based image classification tool in its robots to detect equipment failures on a pilot-level experimentation.

Similarly, a Germany-based luxury car manufacturer, Audi, has been using AI to monitor the quality of spot welds since 2021 and is also planning to use AI in its wheel design process starting in 2023. In 2021, Audi’s parent company, Volkswagen, also invested about US$1 billion to bring technologies such as cloud-based industrial software, intelligent robotics, and AI into its factory operations. With this, the company aims to drive a 30% increase in manufacturing performance in its plants in the USA and Mexico by 2025.

In another instance, South Korean automotive manufacturer Hyundai uses AI to improve the well-being of its employees. In 2018, the company developed wearable robots for its workers who spend most of their time in assembly lines. These robots can sense the type of work of employees, adjust their motions, and boost load support and mobility, preventing work-related musculoskeletal disorders. Thus, AI is transforming every facet of automobile manufacturing from designing to improving the well-being of employees.

Companies provide more ADAS features amidst increasing competition

Automated Driver Assistance System (ADAS) is one of the powerful applications of AI in the automotive industry. ADAS are intelligent systems that aim to make driving safer and more efficient. ADAS primarily uses cameras and Lidar (Light Detection and Ranging) sensors to generate a high-resolution 360-degree view of the car and assists the driver or enables cars to take autonomous actions. Demand for ADAS is growing globally due to consumers’ rising preference for luxury, better safety, and comfort. It is estimated that by 2025, ADAS will become a default feature of nearly every new vehicle sold worldwide. ADAS is classified into 6 levels:

Level 0 No automation
Level 1 Driver assistance: the vehicle has at least a single automation system
Level 2 Partial driving automation: the vehicle has more than one automated system; the driver has to be on alert at all times
Level 3 Conditional driving automation: the vehicle has multiple driver assistance functions that control most driving tasks; the driver has to be present to take over if anything goes wrong
Level 4 High driving automation: the vehicle can make decisions itself in most circumstances; the driver has the option to manually control the car
Level 5 Full driving automation: the vehicle can do everything on its own without the presence of a driver

At present, cars from level 0 to level 2 are on the market. To meet the growing competitive edge, several auto manufacturers are adding more automation features to the level 2 type. Companies have also been making significant strides toward developing autonomous vehicles. For instance, auto manufacturers such as Mercedes, BMW, and Hyundai are testing level 3 autonomous vehicles, and Toyota and Honda are testing and trialing level 4 vehicles. This indicates that the future of mobility will be highly automated relying upon technologies such as AI.

Volkswagen and Porsche use AI in automotive marketing and sales

There are various applications of AI in marketing and sales operations – in sales forecasting and planning, personalized marketing, AI-assisted virtual assistants, etc. According to a May 2022 Boston Consulting Group (BCG) report, auto OEMs can gain faster returns with lower investments by deploying AI in their marketing and sales operations.

Some automotive companies have already started to deploy AI in sales and marketing. For instance, since 2019, Volkswagen has been leveraging AI to create precise market forecasts based on certain variables and uses the data for its sales planning. Similarly, in 2021, a Germany-based luxury car manufacturer, Porsche, launched an AI tool that suggests various vehicle options and their prices based on the customer’s preferences.

Automakers integrate AI-assisted voice assistants into cars

Cars nowadays are not only perceived as a means of transportation but consumers also expect sophisticated features, convenience, comfort, and an enriching experience during their journey. AI enhances every aspect of the cockpit and deploys personalized infotainment systems that learn from user preferences and habits over time. Many automakers are integrating AI-based voice assistants to help drivers navigate through traffic, change the temperature, make calls, play their favorite music, and more.

For instance, in 2018, Mercedes-Benz introduced the Mercedes Benz User Experience (MBUX) voice-assisted infotainment system which gets activated with the keyword “Hey Mercedes”. Amazon, Apple, and Google are also planning to get carmakers to integrate their technologies into in-car infotainment systems. It is expected that 90% of new vehicles sold globally will have voice assistants by 2028.

Integration and technological challenges hamper the adoption of AI

The adoption of AI in the automotive industry is still at a nascent stage. Several OEM manufacturers in the automotive industry are leveraging various AI solutions only at the pilot level and scaling up is slow due to the various challenges associated with AI.

At the technology level, the creation of AI algorithms remains the main challenge requiring extensive training of neural networks that rely on large data sets. Organizations lack the skills and expertise in AI-related tools to successfully build and test AI models, which is time-consuming and expensive. AI technology also uses a variety of high-priced advanced sensors and microprocessors thus hindering the technology to be economically feasible.

Moreover, AI acts more or less like a black box and it remains difficult to determine how AI models make decisions. This obscurity remains a big problem, especially for autonomous vehicles.

At the organizational level, integration challenges make it difficult to implement the technology with existing infrastructure, tools, and systems. Lack of knowledge of selecting and investing in the right AI application and lack of information on potential economic returns are other biggest organizational hurdles.

EOS Perspective

The applications of AI in the automotive industry are broad and many are yet to be envisioned. There is an upswing in the number of automotive AI patents since 2015 with an average of 3,700 patents granted every year. It is evident that many disrupting high-value automotive applications of AI are likely to be deployed in the coming decade. Automotive organizations are bolstering their AI skills and capabilities by investing in AI-led start-ups. These companies together already invested about US$11.2 billion in these startups from 2014 to 2019.

There is also an increase in the hiring pattern of AI-related roles in the industry. Many automotive industry leaders are optimistic that AI technology can bring significant economic and operational benefits to their businesses. AI can turn out to be a powerful steering wheel to drive growth in the industry. The future of many industries will be digital, and so will be for the automotive sector. Hence, for automotive businesses that are yet to make strides toward this digital transformation, it is better to get into this trend before it gets too late to keep up with the competition.

by EOS Intelligence EOS Intelligence No Comments

Chip Shortage Puts a Brake on Automotive Production

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The world is currently witnessing a semiconductor shortage and one of the worst-hit sectors is the automotive industry. A new vehicle uses an average of 1000-1500 microchips, making semiconductors an integral part of automobile manufacturing. Thus, the current shortage has resulted in a slowdown (and in some cases a halt) in production by several car manufacturers, especially of high-feature vehicles that require more chips. This has had a severe impact on auto manufacturers’ revenues in 2021, expecting to cost them close to US$200 billion this year. With no sight of recovery in the near future, the automobile sector must get creative with its supply chains and make some long-term changes in order to sustain production.

The automobile sector globally has been hit by the shortage of semiconductor chips, which are a key component in automobile manufacturing and are used for numerous features, such as fuel-pressure sensors, digital speedometers, and navigation displays.

The shortage stems from the increased demand for chips in the consumer electronics segment (such as laptops, phones, TV sets), which witnessed a spike in demand and sales during the early onset of the COVID-19 pandemic. This was coupled with a subdued demand for chips from the automobile segment during the same time as the environment was less favorable for new vehicle purchase.

Although the demand for automobiles quickly recovered in the second half of 2020, auto manufacturers had already withheld large chip orders due to sales uncertainty, and hence they could not secure a steady supply of chips to fulfill the recovered demand, as most foundries had already adjusted their production and increased their focus on catering to alternative industries.

Moreover, the nature of order contracts largely differs between the automobile and the consumer electronics sectors. The auto sector follows primarily the just-in-time manufacturing principle with focus on short-term orders and purchase commitments for chips. On the other hand, other sectors such as consumer electronics work with long-term orders, which in turn bind the suppliers that have switched production from auto sector chips to other chips. Furthermore, semiconductor players are happier with long-term binding contacts as such contracts provide them with more stability and facilitate better planning of their own supply chain.

The shortage was further aggravated by a storm in Texas in February 2021 that halted production in two of the world’s largest semiconductor factories and a subsequent fire in one of the largest semiconductor factories in Tokyo in March 2021.

Chip Shortage Puts a Brake on Auto Production by EOS Intelligence

Chip Shortage Puts a Brake on Auto Production by EOS Intelligence

Given these factors, the supply has tightened, forcing several automotive companies to curtail their production levels, which in turn has significantly affected their revenue. To give just a few examples, General Motors saw a 30% dip in sales in 2021 while Ford expected its 2021 earnings to be affected to the tune of US$2.5 billion.

Moreover, there is no short-term sight of respite. On an average, the lead time for chip production is anywhere between four to six months, with setting up new production lines or switching foundries taking even longer (six to twelve months). Further, switching to a new manufacturer may even take longer than 12 months in case new design or licensing requirements need to be met.

To counter this problem in the short run, auto manufacturers are reducing the number of features they offer and are focusing on fewer high-feature models. For instance, Japan-based Nissan is now omitting the navigation system in several of its models. Similarly, Renault has stopped adding a large digital screen behind the steering wheel, while BMW announced that it will remove touchscreen functionality from the Central Information Display in several models. However, these are short-term measures and not ideal for premium car segment as they may impact brand reputation.

Thus, given the circumstances, auto companies have to be innovative with their supply chains to solve this problem in the long run. They also need to ensure that they do not land in a similar situation in the future.

Traditionally, most auto manufacturers deal with only one key supplier (known as tier 1 supplier), who in turn sources all parts from specific component suppliers, including semiconductors from foundries. While this was convenient for the auto manufacturers, this resulted in lack of transparency across the supply chain. Moreover, this meant that the manufacturers did not have direct relations with foundries to ensure smooth supply.

However, in the face of the unfolding shortage, several leading players, such as BMW, Mercedes, and Volkswagen, started building strategic relations with chip manufacturers to get better and direct access to supply lines for semiconductors. In December 2021, BMW signed an agreement with German-based Inova Semiconductors and US-based GlobalFoundries to lock in a steady chip supply for their cars. Similarly, Ford also entered into a strategic collaboration with GlobalFoundries to purchase directly from the chipmaker. Furthermore, in November 2021, General Motors entered into an agreement with Foxconn Technology Group to co-develop chips that can be used in its vehicles.

Additionally, the auto sector is also moving away from the widely followed just-in-time model that facilitated lean inventory and pushed up profits. Companies are now keener to secure long-term non-disrupted supply of chips and are willing to enter into long-term contracts ranging 2 to 3 years.

Apart from this, car manufacturers are also looking at altering designs to limit the number of chips needed. Currently, most chips needed by the auto sector are large and outdated compared with those used for smart phones and other gadgets. Most foundries are now producing new generation microchips for these devices and do not want to switch back to old chips used in cars as investing in old technology is much less lucrative for them.

For this reason, auto manufacturers are considering revamping their chip designs, however, this comes with its own set of limitations. Automobiles need to undergo a host of certifications and safety testing to ensure road readiness. Any changes in designs regarding features such as cruise control, navigation, etc., would require the vehicles to get re-certified and clear safety testing again across all geographic markets, which has significant cost and lead time attached to it. Moreover, a complete overhaul in the chip board would require large amount of investment as it would impact the overall mechanical design of the vehicle.

However, several companies have already started working on this. In late 2021, General Motors announced that it is working with chip suppliers, Qualcomm, STM, TSMC, Renesas, NXP, Infineon, and ON Semi to develop a new set of microcontrollers that will consolidate many functions handled by individual chips and reduce the number of chips required by 95% for all future vehicles.

In the long run, it is expected that several auto companies will work on updating their chips as foundries refuse to downgrade the chips they produce. Moreover, while it will be costly and cumbersome in the beginning, it will be beneficial in the long run as companies will be less dependent on a number of chips, and instead work with a single chip overseeing multiple functions.

EOS Perspective

Chip shortage has significantly crippled the automotive sector stalling production in an unprecedented manner. It has also cost auto companies billions of dollars, while creating an inconvenience for users as car prices have risen significantly and customers have to wait for months, if not more, for their new cars.

But this shortage has also been a learning opportunity for the automobile sector, which is now working on restructuring its supply chain to reduce reliance on one key supplier. The industry is also placing more emphasis on supply chain visibility to ensure that a similar shortage does not occur in the future. This will mean a real-time insight not just into the key suppliers, but also further into their vendors, i.e. individual part suppliers. This is likely to bring the use of technologies such as IoT and AI to automotive supply chain monitoring in a more prominent manner.

The chip shortage is also likely to result in vehicle design upgradation by several leading manufacturers, so that the new upgraded chips can be used. This upgradation in design to incorporate new chips has been long due, however, auto manufacturers were stalling it because of costs and cumbersome re-certification processes.

The current pressures resulting from the semiconductor and chip shortage, are likely to bring a deep overhaul in the automotive sector, with companies and suppliers willing to invest in supply chain and design-based creative solutions, striving to gain a long-term competitive edge amid the new and challenging environment.

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