Why Equip Your Automotive ERP With AI Algorithms

While rooting out supply chain disruptions completely is unrealistic, automotive companies can mitigate their negative effects by using safety stock—a reserve of materials, components, and products that prevent stockouts. However, extra inventory can cause overstocking, which should also be avoided due to increased warehousing costs and unnecessary employee workload.

Such solutions can discover and filter suitable resumes based on preconfigured parameters, such as education, skills, or experience with specific equipment or software tools, and submit them to an HR specialist, allowing them to contact and hire qualified candidates quickly.


The average consumer reported nearly two issues per vehicle in 2023. Equipping an ERP system with a dedicated AI algorithm allows companies to improve quality management and mitigate issues. Quality managers can automate BOM analysis to detect and track problematic materials and suppliers.

Enhanced human resource management

Equipping an ERP system with a dedicated AI algorithm allows companies to improve quality management and mitigate the aforementioned issues. In particular, quality managers can automate bill of material (BOM) analysis, as this document allows specialists to detect and track the most problematic materials and suppliers.

Beyond recruiting, AI algorithms integrated into ERP allow HR managers to better retain existing employees and address talent shortages in this way. In particular, AI can analyze performance data, such as the number of units produced, work in process, or profit per employee, to help HR professionals fairly evaluate workers and reward the best-performing ones, fostering employee motivation and loyalty.

Final thoughts

Resilinc’s EventWatchAI registered more than 5,014 supply chain disruptions in the automotive industry in the first six months of 2023, which accounted for 60% of supply chain disruptions across all industries.

Labor shortages also remain a critical automotive industry challenge. It’s considered by 35% of dealers as their main business issue, while 36% of manufacturers rate talent shortage as their top challenge as well, as highlighted in 2023 reports by CDK Global and AMS & ABB, respectively.

An automotive company can improve resource management processes at least partially by using ERP equipped with a human resource management module, enabling more efficient hiring of new talent and managing of existing employees.

Supply chain disruptions, lack of skilled employees, and other industry challenges make automotive business management more complex than ever. Fortunately, automakers and auto dealers can address these challenges by equipping their ERP systems with AI algorithms. Such solutions can process the wealth of ERP data and help a company forecast consumer demand, improve product quality, assess suppliers more accurately, or manage human resources better.

This article explores four ERP workflows that AI algorithms can enhance, bringing more business value to an automotive business.

More precise demand forecasting

Declining product quality is another challenge emerging in the automotive industry. The latest J.D. Power U.S. Initial Quality Study (IQS) reveals that an average consumer reported nearly two issues per vehicle in 2023, which was “a phenomenon not seen in the 37-year history of the IQS.” While most of these problems do not pose a direct risk to human life (defective audio systems and poor-sounding horns are some common ones), they still negatively affect customer satisfaction and loyalty.

Capgemini’s 2023 Automotive Supply Chain report shows that value procured from offshore locations has reduced by 22% from 2021 to 2023, signaling that more and more automotive companies are switching to nearshoring procurement.

By implementing a dedicated supplier management AI algorithm into ERP, procurement teams can evaluate supplier performance, even in real time, to identify best-performing companies or uncover hidden supplier risks. Such AI tools can provide a comprehensive supplier assessment based on parameters such as lead time, price of material, or product defect rate, and generate automatic analytical reports to help tailor procurement strategies and make smarter business decisions.

Improved product quality

The majority of automotive companies already actively utilize automotive enterprise resource planning (ERP) systems to centralize and manage their key operations, including supply chain, production management, and quality control. However, by equipping their ERP with tailored AI algorithms, automotive companies can achieve advanced automation and analytics capabilities to help them better address emerging business challenges.

In 2024, operating in the automotive market has become increasingly difficult. Global and local supply chain disruptions, product quality issues, and ongoing talent shortages are some of the greatest challenges automotive businesses face globally.

Suppose an automaker is planning to enter the electric vehicle market. The company needs an experienced cobalt and nickel procurement specialist to produce electric vehicle batteries. Instead of manually browsing LinkedIn, Glassdoor, or other job hiring sites, an HR team member can use AI tools built into ERP to find qualified candidates online.

Supply disruptions of critical components, such as auto parts or microchips, can cause production delays, negatively affecting automotive companies’ sales and revenue.

Customizing automotive ERP with artificial intelligence algorithms is a challenging project requiring solid multidisciplinary expertise and skills. Companies can streamline artificial intelligence adoption by turning to third-party ERP experts proficient in AI development with a proven success record in the automotive industry.

منبع: https://www.qualitydigest.com/inside/innovation-article/why-equip-your-automotive-erp-ai-algorithms-031424.html

A predictive analytics algorithm implemented into an ERP system can generate highly precise market forecasts, helping companies balance safety stock levels accurately. Such a system uses various statistical and machine learning algorithms to create forecasts based on data from numerous sources, including internal (such as production, sales, or shipping data) and external ones (such as social media, weather forecasts, or automotive industry news feeds).

More efficient supplier management

Also, AI-powered ERP can help companies automate quality control inspections, from document generation to results analysis. In particular, AI can automatically transform data obtained during inspections into fishbone diagrams, visually mapping the root causes of problems or defects.

However, the shift to nearshoring can be challenging for automotive businesses, because they have to work with new and less familiar suppliers. In turn, choosing the wrong local supplier can lead to product delivery delays and quality problems, which can lead to customer dissatisfaction.