Artificial intelligence is providing carmakers with the ability to increase production efficiency and improve quality control. The automotive industry is one of the most promising sectors for AI adoption, and many carmakers are already using AI in some capacity. In this article, we take a close look at how AI is being used in car manufacturing and the benefits it brings to the industry.
What Is AI And Why Is It Important In Car Manufacturing
The AI in Automotive Market topped USD 1 billion in 2019 and is expected to expand at a CAGR of more than 35% between 2020 and 2026. Artificial intelligence is the future of business: it’s the technology of the twenty-first century.
Even though most experts believe that AI will not replace human labor anytime soon, it’s already making a big impact on nearly every industry imaginable, including auto manufacturing.
AI is a set of computer algorithms that are designed to perform artificial tasks more efficiently than can be performed by humans. It uses rules, logic and data to make decisions rather than relying on emotions and personal preferences.
AI is also able to analyze data in a systematic way to learn how different factors affect outcomes, which makes it invaluable for manufacturing companies who are using analytics in their day-to-day operations.
AI at its highest level is a technological advancement that’s supposed to make the world a better place, while at its lowest, it’s the stuff of science fiction and dystopian nightmares. Regardless of where you stand on the idea of AI and its potential impact on humanity in general, you can’t deny that significant changes are occurring in business and society as a result.
There are many different types of AI that are being used in various industries and businesses today, so let’s jump into this guide to see how it can work for car manufacturing companies.
How Can AI Help To Improve Car Manufacturing Process
Artificial intelligence is being used in a variety of ways to assist in the design, production and delivery of parts to meet performance requirements.
Tesla said in October 2019 that it will introduce fully autonomous vehicles by Q1 2020 in order to get an early mover advantage.
The majority of the tools used for this purpose are related to machine learning and analytics. Predictive analysis allows machines to recognize when certain events have taken place, such as defective parts and impending failures.
This data can then be used in conjunction with historical manufacturing data that is already stored on a company’s servers to improve quality control procedures at every stage of production.
Benefits Of AI In The Automotive Industry
According to the National Highway Traffic Safety Administration, the adoption of autonomous cars may save USD 300 billion by 2025 by decreasing collisions and facilitating safe driving.
The automotive value chain is undergoing a major transformation as autonomous driving technology and AI-based technologies are increasingly being used in automotive manufacturing. The average passenger car is becoming more and more technologically advanced, and this is having a major impact on the way that the automotive value chain operates.
1. Machine Learning Is Used To Optimize Production Planning
The automotive industry has a complex manufacturing process and requires advanced planning to meet schedules and avoid downtime. Optimal production planning involves working with different departments and collecting data on relevant factors such as supply chain, revenue, margins and delivery dates.
Machine learning is used by companies to optimize the process of working out how to plan the production process while taking into consideration the above-mentioned factors.
2. AI Will Be Integrated Across All Stages Of A Car’s Lifecycle
Manufacturing a car involves a number of different departments or stages, including design, development, testing and engineering. By bringing AI into each stage of the lifecycle, carmakers can deliver higher quality vehicles with better performance. AI is also improving the overall quality of vehicles by identifying potential issues, such as how different components affect each other.
3. AI Improves Production Efficiency
AI can be used to optimize production efficiency by increasing efficiency in the design and manufacturing processes. The use of AI is also serving as a safeguard against production delays.
Carmakers are able to monitor parts and monitor the current status of a vehicle, ensuring all components are produced in time for delivery. By introducing sensors that check for specific problems, manufacturers can avoid unnecessary errors and problems at all stages of the manufacturing process.
4. Data Is Used To Improve Car Designs
The amount of data collected by car manufacturers is massive. This data covers different stages of a vehicle’s lifecycle, including engineering, manufacturing and customer feedback. AI is used to analyze this end-to-end data to identify the factors that affect different parts of a car design.
For instance, AI can be used to identify how a specific component affects the overall performance or safety of the vehicle. Machine learning algorithms are also used to sift through this data to find trends that can be applied across different vehicles or future models having similar structures.
5. AI Increases Quality Control Efforts
When it comes to quality control, manufacturers don’t just put more effort into ensuring the quality of their products. They also combine new technology with human supervision to make sure the product meets quality standards.
AI is used in this aspect of quality control by determining the best way to deploy existing and new technology. By combining machine learning with previous inspections, manufacturers can gain a clear understanding of how to improve quality control results.
6. AI Is Being Used For Real-Time Analysis
Real-time data analysis is increasingly being used in the automotive industry. The most common example of real-time analysis is predictive maintenance, which involves formulating a plan before an issue occurs so it can be averted or if multiple issues are detected, they can be fixed before they affect production schedules or budgets.
7. AI Is Necessary For Improving Original Equipment Manufacturer (OEM) Processes
When it comes to OEM processes, a company also has to ensure its products are produced efficiently. When conducting a test, companies need a tool that can be used in efficient and effective ways to collect data to determine the best way to solve the issue or problems at hand.
While this data can be collected manually, it is inefficient and time-consuming. AI can be programmed with the relevant information, allowing OEMs save time and money on their product testing process.
8. AI Can Be Used To Assist In The Design Of New Vehicles
AI is also being used by carmakers to help design new models. Many start-ups have shown that AI can be used to improve vehicle design. For instance, an autonomous vehicle can learn more about uneven roads and specific obstacles on the road that it encounters.
This information is then converted into a 3D model for a vehicle’s future designs. By using this information, however, a company must ensure the accuracy of data collected through AI is sufficient enough to ensure it won’t cause any issues with future models or designs.
Challenges Of Implementing AI In Car Manufacturing
Tesla now offers level 2 autonomous vehicles, and the firm plans to introduce level 4/5 autonomous vehicles by the end of 2020. The self-driving car is the natural evolution of the car. It is powered by artificial intelligence (AI) and autonomous driving technology.
The car is equipped with sensors and computers that allow it to see and navigate its environment. Data science is used to interpret the data collected by the sensors and to make decisions about how to drive.
AI-powered robots are being used by automotive manufacturers to improve efficiency in the automotive sector. Connected cars are also becoming more popular, and this is expected to boost vehicle sales.
1. A Highly Regulated Industry
The auto manufacturing industry is one of the most highly regulated in the world. Not only are carmakers required to abide by strict federal guidelines, but they also have to follow local laws and adhere to international standards.
2. High Costs Of Implementation
It’s easy to say that AI can drive up production efficiency, but it’s another thing entirely to make it happen. The cost of implementing AI-driven technology in an automotive factory is significant and will require significant capital investment from manufacturers. This could be problematic for smaller companies that don’t have enough money to invest in automation technology.
3. Small Return On Investment
Even though AI technology can make an impact in a car factory, it’s not the kind of technology that will yield significant returns on investment. Businesses in this industry are unlikely to see returns within the first year of making massive changes to their production lines and implementing advanced technology.
4. Lack Of Technological Skills In The Industry As A Whole
Most manufacturers are reluctant to implement new technologies because they don’t have the skills or resources in-house necessary to manage new digital innovations. Even with a wealth of information available from companies like Google and Microsoft, most manufacturers don’t know where to start when it comes to implementing AI.
5. Lack Of Understanding
Many automakers don’t seem to understand how AI can be used in their business model. They may not be able to clearly explain how the technology will benefit them or what it will do for them in future years. This can make it difficult for engineers and manufacturing experts on staff to convince management that AI is worth investing in.
6. Possible Job Loss As A Result Of Automation
Automating certain aspects of car manufacturing could eliminate jobs within an organization and lead to significant employee turnover rates. Workers that are unable to change their skillsets to meet the needs of the company will leave, and workers that are not up-to-date on current technology may have difficulty finding another job in the industry.
7. Merger Risk
As AI algorithms become more sophisticated, companies with many different manufacturers under one roof could face some serious merger risk if they don’t take steps to protect themselves from potential liabilities.
For example, if one manufacturer releases an AI-driven car with a faulty or poorly designed part or software module, it could be all the ammunition needed to file suit against the company for any injuries or damages caused by its faulty product. It is projected that by the end of 2030, 95-98% of new automobiles will feature AI technology.
8. Lack Of Standardization
Most of today’s AI algorithms are unique and have been developed specifically for one business without being widely shared. This means that many businesses will have to develop their own algorithms from scratch, which could be an expensive and time-consuming process. Automakers may not see real results from AI until they agree on a universal standard of how to implement it.
Final Note
According to FutureBridge forecasts, autonomous vehicle sales accounted for 33 million units in 2019, with the industry predicted to reach 80 million units by 2030, with a CAGR of 8-10% between 2019 and 2030.
AI has yet to overturn the manufacturing industry, but it’s already making a big impact on modern-day business practice. The technology is designed to use analytics, data and logic to make decisions that are supposed to improve production efficiency and provide better customer service.
Last Updated on September 26, 2023 by himani
