From enhancing design processes to predicting maintenance needs, artificial intelligence transforms automotive suppliers from traditional manufacturers into innovative trailblazers.
The market for AI in this space is projected to hit a whopping $15.9 billion by 2027, expanding at a breakneck pace of 20.7% annually. That’s a lot of zeroes! But what’s even more exciting is how much of that growth is driven by generative AI. This tech doesn’t just analyze data—it creates, optimizes, and predicts, turning traditional manufacturing on its head.
Bosch, for instance, has integrated AI to reduce production costs by 10% and improve component quality by 15% (Bosch Media Service). Continental has partnered with Google Cloud to equip cars with generative AI, creating more intelligent and interactive vehicles for drivers (Continental AG). Meanwhile, Magna uses AI to streamline operations and reduce production costs (PR Newswire).
In this article, we will explore the transformative impact of generative AI on automotive suppliers, highlighting key use cases and practical insights. You will learn how generative AI can optimize design processes, assist in maintenance, enhance supply chain management, automate manufacturing, and offer customer-centric customization. We’ll also guide you in choosing the best use case for implementation in your organization. Discover how to stay ahead of the curve and drive innovation in the automotive supply industry!
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Artificial Intelligence in the Automotive Industry
While generative AI is a relatively new thing, the use cases of artificial intelligence have been proving their value for the automotive industry for years in different areas and across different types of organizations, from vehicle producers (Original Equipment Manufacturers, OEMs) to component suppliers of every tier.
If you think about automotive and AI, chances are that one of the first use cases that comes to mind is the development of advanced driver-assistance systems (ADAS) and autonomous driving technologies. These systems rely on machine learning algorithms to process sensor data and make real-time decisions, enhancing vehicle safety and paving the way for fully autonomous vehicles. But while this particular use case caught a lot of media attention, it’s not the only – and probably not even the most common – use case for AI in the automotive industries.
For years, artificial intelligence has been helping companies forecast demand more accurately, manage inventory efficiently, and reduce lead times. AI-powered predictive maintenance systems monitor the health of machinery and equipment, preventing downtime and costly repairs. Additionally, they help optimize production schedules and streamline supply chain management, which has proven especially beneficial for suppliers at various levels of the supply chain. These use cases have significantly boosted efficiency and reduced costs, making AI indispensable for automotive suppliers.
However, the real surge in the popularity of AI for automotive suppliers came at the end of 2022 with the growing popularity of generative AI.
Gen AI for Automotive Suppliers
Generative AI, a cutting-edge branch of artificial intelligence, leverages advanced algorithms to create new content, designs, or solutions from existing data. Unlike traditional AI, which focuses on analyzing and interpreting data, generative AI can produce original outputs such as images, text, or even complex designs. Its capabilities extend across various industries, enabling everything from automated content creation and personalized marketing to the design of new products and optimization of intricate systems. Let’s see how they apply for tier 1, tier 2, and tier 3 automotive suppliers!
Generative AI in Automotive Design Optimization
Generative AI is transforming how automotive suppliers approach design by enabling rapid exploration and optimization of components. By using algorithms like Generative Adversarial Networks (GANs), Gen AI can create and evaluate numerous design alternatives based on specific performance criteria such as weight, strength, and material efficiency and run simulations to find the best design. And the best part is that such generative AI solutions are already used in production!
Autodesk Generative Design Software
General Motors has utilized Autodesk’s generative design software to create a seat bracket. This AI-driven tool produced over 150 design alternatives, resulting in a final product that was 40% lighter and 20% stronger than its predecessor, demonstrating the profound impact of AI on automotive design.
(Photo: Autodesk Inc.)
According to McKinsey, generative-design use cases could improve R&D processes by 10 to 20 percent, translating into significant cost savings. And it’s just one use case! Where else can generative AI help automotive suppliers?
Maintenance Assistance
As reported by the Wall Street Journal, unplanned downtime costs industrial manufacturers an estimated $50 billion annually—and equipment failure causes 42% of this unplanned downtime! While predictive AI can successfully help organizations prevent such failures, no algorithm can ensure 100% accuracy. When the problem occurs, generative AI comes to the rescue.
The largest automotive suppliers have dozens of manufacturing facilities in numerous countries. Despite sharing similar challenges and experiencing the same maintenance issues, the facilities rarely share maintenance information with each other. The records are stored in different databases, structured in different ways (if structured at all), and written in various languages. But what is a big problem for humans is a piece of cake for generative AI.
ZF’s Maintenance Assistant
The ZF’s AI Maintenance Assistant was created to assist factory maintenance teams with efficiently diagnosing equipment failures by identifying root causes and solutions for maintenance challenges in manufacturing facilities across Europe. Powered by GPT and Cognitive Search based on the Azure cloud, the assistant is connected to the internal database containing information about past failures and downtimes. Additionally, every answer includes a link to the original information to limit the black box effect, reduce the hallucinations, and increase trust.
AI-Powered Supply Chain Management
Generative AI enhances demand forecasting, optimizes inventory management, and improves production scheduling. By crunching vast amounts of data, AI provides spot-on demand predictions, slashing excess inventory and stockouts. It also forecasts which parts are needed and when enabling manufacturers to keep lean inventories without the panic of running out. Plus, AI-driven algorithms fine-tune production schedules, making factories more efficient and minimizing downtime.
Unlike predictive AI, which primarily forecasts based on historical data, generative AI can simulate a variety of potential future scenarios. This ability allows automotive manufacturers to explore “what-if” situations, like sudden changes in demand or supply chain disruptions, and prepare accordingly. Additionally, it can suggest optimal solutions. For example, it can recommend the best way to reconfigure production lines or adjust inventory levels dynamically in response to real-time data. This level of optimization was not possible with traditional predictive models.
Volvo using AI to decarbonize supply chains
An interesting application of generative AI in the automotive supply chain comes from the north of Europe. Volvo Buses shared an ambitious goal of fully decarbonizing their supply chain by 2040 and reducing 25% of their emissions by 2025. As described in Volvo’s recent press release:
As a first step, it will take data from its own systems and gather all the details on each supplier and their respective part or component in a bus or coach. This will be combined with data from external environmental databases to calculate the greenhouse gas emissions of each material included in the parts.
Once all this information is collected into one system, AI will be used to generate suggestions on how each supplier can reduce their greenhouse gases. It will also give proposals for parts that can be exchanged and replaced with lower emission alternatives.
Fingers crossed!
Generative AI in Quality Control
Generative AI is giving quality control in the automotive industry a serious upgrade, bringing precision and consistency to the forefront. Picture this: AI that can detect if a crucial part, like the striker in a door latch mechanism, is missing or make sure every badge and decal is precisely where it should be. Using photos, sound, and vibrations, AI efficiently roots out defects, performing millions of inspections to keep quality top-notch. And it doesn’t just find flaws. It can stop the assembly line on a dime if something’s amiss, ensuring only the best parts make it through.
The best part? This high-tech wizardry can run on something as simple as a cellphone camera. When a part, like a pump in a transmission cover, is installed, the camera snaps a photo. That image is then compared against a cloud-based library of correctly and incorrectly installed parts. If there’s a problem, AI gives the thumbs up or halts production, just like in the case of Ford.
AI Eliminating Defects in Ford’s Components
In 2023, Ford paid $1.9 billion in warranty costs, according to the company’s 10-K filing. This called for an immediate action. By applying generative AI quality controls tools at the Van Dyke Electric Powertrain Center in Detroit, they have managed to go from 40 faulty pumps each month to zero in April 2024.
As reported by Autonews, the system is now deployed at 325 stations in 20 Ford plants worldwide, and it has been programmed to perform inspections on 463 types of manufacturing operations, from confirming the correct fitment of those squish tubes to inspecting body panels, such as hoods, for warping.
Automotive Supply Process Automation
Generative AI automates and optimizes the automotive supply process by analyzing vast amounts of data to predict demand, manage inventory levels, and streamline the supply chain. This ensures parts and materials are available exactly when needed, reducing delays and minimizing excess inventory. The technology enhances supplier coordination by identifying potential bottlenecks and suggesting optimal routes for materials, maintaining a smooth flow of components to the assembly line.
By continuously learning from production data, AI systems can simulate various scenarios to optimize workflows and resource allocation. This creates a more resilient supply chain capable of adapting to disruptions and maintaining high efficiency. Ultimately, generative AI automates routine tasks and provides strategic insights, driving innovation and competitiveness in the automotive industry.
NVIDIA Omniverse Enhancing Production Efficiency in BWM Manufacturing Facilities
BMW uses NVIDIA Omniverse to create digital twins of their manufacturing facilities, integrating generative AI to enhance production efficiency and design processes.
In March 2023, they have shared a demo of a virtual planning session for BMW’s Debrecen EV plant:
As we can read on NVIDIA’s blog:
With Omniverse, the BMW team can aggregate data into massive, high-performance models, connect their domain-specific software tools, and enable multi-user live collaboration across locations. All of this is possible from any location, on any device.
Starting to work in the virtual factory two years before it opens enables the BMW Group to ensure smooth operation and optimal efficiency.
With the help of generative AI, BMW can create digital twins of its facilities. This allows them to validate and test ideas in a virtual environment before actual production starts. This capability accelerates time to production, cuts costs, and boosts efficiency across all its plants.
How to Choose the Right Use Case for AI?
Choosing the proper use case for AI in the automotive supply industry requires strategic alignment with your business goals and careful consideration of prioritization criteria.
Start by ensuring the AI initiative addresses your current business needs or challenges. For example, if your primary issue is delays in construction while opening a new plant, AI may not be the right tool. Instead, focus on areas where AI can directly impact efficiency, such as inventory management, demand forecasting, or quality control.
Next, evaluate potential use cases based on reach, impact, feasibility, effort, and expected return on investment (ROI). Assess how many people will use the AI solution and the extent of its impact on their work.
Consider the solution’s feasibility, including the availability and quality of data, as well as any sensitive information involved.
Estimate the time and resources required for implementation and weigh this against the expected ROI.
Finally, gauge your confidence in these metrics to ensure a well-informed decision. By systematically prioritizing these factors, you can identify the most valuable and achievable AI applications for your automotive supply chain.
And if it all sounds complex (as it is), explore our custom software and AI solutions for automotive suppliers.
Read also: How to Identify the Right Use Case for Generative AI Adoption? Experts Advise
Automotive Suppliers and Gen AI – Key Takeaways
The market for AI in the automotive supply industry is set to skyrocket to $15.9 billion by 2027, growing at a rapid pace of 20.7% annually. And it’s easy to see why! AI has already shown its worth in countless applications, from enhancing automotive design and automating processes to streamlining maintenance, managing supply chains, and improving quality control. These advancements are not just trends but essential tools driving efficiency and innovation across the industry.
With the vast majority of automotive companies already implementing AI, those who don’t may quickly fall behind. If you haven’t joined the first group yet and don’t want to be part of the latter, join our AI workshop session to explore the potential use cases of AI in your organization and start benefiting from AI this year.
Identify the right use cases for generative AI development and make sure they provide measurable value for your organization. Leave us a message, and we will get back to you soon to schedule your workshop session.
