How AI is Revolutionizing Automotive Supply Chains

The automotive supply chain faced a global semiconductor chip shortage in 2020, with repercussions lasting into 2023. The shortage led to the production of 11 million fewer vehicles in 2021 alone, resulting in a loss of $210 million in missed global revenue when supply could not meet consumer demand. 

Although the chip shortage has ended, its impact has left many business owners cautious. Delayed or scaled-back production has created significant challenges in recovery, highlighting the need for specialized automotive supply chain consulting.

According to McKinsey, early adopters of AI-enabled supply chain management have improved logistics costs by 15%, inventory levels by 35%, and service levels by 65% compared to their competitors. Businesses in this position, or those interested in making their operations more efficient, should consider the possibilities of AI-powered automotive supply chain software. 

What Struggles Do Automotive Supply Chains Face?

The chip shortage was one of the most prominent supply chain setbacks in recent history. However, it isn’t the only problem that business owners must contend with. 

A 2022 survey conducted by BCG and APQC found that 80% of companies across various industries aren’t built for long-term resilience. These companies are reactive and unprepared to address disruptions quickly. The same survey also found that OEMs and suppliers fell into the reactive category, which was more concerning because chip content per vehicle increased by 7% yearly. 

Automotive supply chains can also face variations in pricing, demand, and lead times depending on the markets they're based in, or be disrupted by economic and geopolitical shifts. 

Artificial Intelligence and Automotive Supply Chains

Artificial intelligence (AI) is well-known for its varied uses- from market research and analytics to forecasting potential crises. Business owners across industries are now seeking AI solutions to automate tasks that would take human workers hours, if not longer. Unsurprisingly, AI’s benefits can also optimize operations throughout the automotive supply chain process. 

Research and Planning

The research stage is vital for an effective supply chain, impacting operational decisions and offering companies a valuable look into what they should and shouldn’t invest in. In the past, business owners often turned to demand forecasting methods like Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA), two common methods that reference past observations to predict future trends. 

However, while these methods have their merits, they struggle to keep up with an increasingly dynamic market. Companies must factor unpredictable scenarios such as climate change, supply-demand mismatches, and capacity shortages into their supply chains, which requires flexibility that these classical models can’t always manage. 

AI solutions excel at making predictions in variable environments. Machine learning (ML) combines self-learning algorithms with historical data to produce more accurate and dynamic predictions than static models. One study found that ML techniques like deep learning “significantly improve the accuracy of demand forecasting methods” for supply chain management, both alone and in combination with statistical methods. 

Another study explored the use of ML for transporting highly perishable goods, as quick, accurate readings are vital for avoiding financial hits. Researchers found that ML-based methods consistently outperformed classical forecasting methods for time-sensitive sales predictions.

These solutions can save companies hours of market research and offer more precise results. Still, though more accurate, AI isn’t perfect. Most AI methods currently draw from a low volume of data due to technical limitations and difficulties in accessing real data. AI is more effective with larger datasets, and users may encounter blind spots if insufficient data is provided.

Businesses can avoid some of these limitations by working with machine learning experts. By working with Subject Matter Experts (SMEs), companies can build a more comprehensive dataset for a stronger AI solution. 

Procurement

As with all businesses, procuring supply is critical to the automotive supply chain process. Deloitte's 2023 Global Chief Procurement Officer survey found that 75% of leading procurement organizations have already incorporated AI in their procurement process. 

Natural language processing (NLP) enables AI to parse through dense information that previously required human analysis. It allows companies to gather and analyze key supplier information, including performance levels, financial health, and environmental, social, and governance (ESG) credentials. This technology allows companies to make data-driven decisions about suppliers. Businesses can also leverage AI to automate tasks like invoicing and order processing.

In addition to informing better decisions about supplies, machine learning technology can also assist in predicting key disruptions. AI can identify possible disruptions in the supply chain before and as they happen, such as traffic jams or global supply problems. 

Google DeepMind, for instance, is working on an ML model that can not only predict weather in seconds but also outperforms 90% of the targets used by the top weather prediction systems in the world. As storms often delay shipments, especially overseas, this data can improve supply chains' adaptability. Companies can use this information to weigh the costs of shifting suppliers, plan different routes, and make other preparations to reduce financial impact. 

Manufacturing

The manufacturing stage sees procured materials take shape into vehicles and parts, and is especially vulnerable to human error or equipment malfunctions. McKinsey has found that AI-based machines can detect defects up to 90% more accurately than humans, potentially increase productivity by up to 20%, and reduce forecasting errors by 30 to 50%. This accuracy reduces the risk of mistakes disrupting companies’ manufacturing processes or causing vehicle issues that will haunt their reputations. Workers, meanwhile, enjoy a safer work environment, fewer rework tasks, and a quicker and more efficient workday. 

PepsiCo, while not an automotive company, has realized the benefits of AI in industrial-scale manufacturing. They have placed AI-powered sensors on equipment throughout their factories, which can warn the company of potential faults in machinery and recommend improvements based on historical data. The global vice president of PepsiCo Labs, David Schwartz, noted that AI does more than future-proof their factories: it also benefits their workers and customers.

"It's helping enhance how people work,” said Schwartz, “so we can bring better efficiency to meet the needs of our people, of our customers, and we can be prepared to lean into the future to meet their needs on a daily basis.”

Distribution and Repair

AI can benefit the supply chain even after vehicles leave the factory. Its prediction capabilities can factor market trends into pricing and optimize vehicle routes. It can also pair with Internet of Things (IoT) technologies, which use sensors embedded in physical objects, to offer real-time shipment tracking and transparency to both customers and distributors, like car dealerships. 

Researchers are also exploring AI for predictive maintenance, which involves detecting equipment failure before it happens through online and offline analysis methods, and then alerting a user so they can plan a scheduled maintenance activity. This can improve industry resilience and drive sales to high-quality aftermarket parts as customers hold onto their vehicles for longer. 

The Future of AI for Automotive Supply Chains

AI’s popularity isn’t unwarranted, being a versatile tool that saves companies time and money while delivering effective results. It has been adopted into the supply chains of enterprises like Pepsi and Amazon. 

As the technology used for it advances, it will grow more effective in reducing the risk of human error, planning around future catastrophes, and optimizing operations. Companies that don’t incorporate AI into their automotive supply chains aren’t just missing out on a useful tool: they could very well be left behind by their competitors. 

A custom AI solution can meet your company’s unique needs and enhance supply chain efficiency. To explore AI possibilities for the automotive industry, contact FullStack Labs today Our team specializes in innovative software solutions to elevate your business.