In a highly competitive industry like automotive manufacturing, optimized purchasing management is a critical strategic lever. Due to fragmented data storage methods and a lack of visibility into existing references, companies face avoidable extra costs. PROBE leverages AI and 3D recognition to offer an innovative solution that enhances access to engineering data, eliminates duplicates, and maximizes economies of scale.
Economies of scale in automotive: A Major Challenge
OEMs and suppliers such as Valeo, Forvia, and others purchase billions of dollars worth of spare parts, raw materials, and equipment every year. Operating on a global scale, these manufacturers manage multiple sites across different continents.
However, even when a common database is shared across the group, the way data is stored and searched varies from one site to another. This lack of standardization leads to inefficiencies and additional costs.
A Recurring Issue: The Difficulty of Identifying an Existing Part
Consider a real-world example: a major international automotive supplier spends $9.5 billion annually on purchasing. At its German site, a specific screw is cataloged and used across multiple production lines.
An engineer based in the United States also needs this same screw. However, he is unaware that it already exists in the database because he cannot use 3D recognition to check for similar models. Text-based searches alone are unreliable—each site may name the same part differently in the PLM system.
Unable to find the existing reference, the engineer initiates a new request for proposals and places a separate order in smaller quantities, increasing the unit cost of the part. This lack of reference rationalization prevents the implementation of a group purchasing strategy, leading to significant financial losses.
Economies of Scale: A Priority for the Automotive Sector
The automotive industry is one of the most competitive sectors globally. Since the COVID-19 crisis, manufacturers have been dealing with rising raw material costs, supply chain disruptions, and fluctuating demand. In this context, cost control is essential.
Effective purchasing management enables companies to consolidate orders and negotiate optimized supply contracts. The larger the order volumes, the greater the bargaining power with suppliers, leading to better pricing conditions and secured supplies.
Faced with these challenges, AI-powered solutions like PROBE have become essential tools to eliminate duplicates, strengthen industry resilience and achieve economies of scale in the automotive sector.
The PROBE Solution: 3D Recognition and Cost Reduction
PROBE provides an effective solution by using 3D recognition to quickly determine whether a part already exists in the database. Instead of relying on an unreliable text-based search, an engineer can simply compare their part with those already stored in the system.
For example, an engineer at a U.S. site can discover that a screw matching their needs is already in use at another site, avoiding the need for a new request for proposals and unnecessary costs. This approach enables procurement teams to consolidate orders and negotiate supplier discounts, maximizing economies of scale.
Additionally, purchasing teams—who are not always technical experts—typically view parts as Excel lines with textual characteristics. With PROBE, they can easily explore 3D models, allowing them to:
Verify whether a part already exists in the database without relying on text descriptions,
Group orders to secure better pricing conditions,
Use existing references as a baseline to estimate the cost of a new part.
By leveraging AI and 3D recognition technologies, PROBE optimizes purchasing processes and significantly reduces redundancies.
A Major Financial and Organizational Impact
Thanks to PROBE, this automotive supplier achieved annual savings of $130 million by optimizing its purchasing strategy. These savings were driven by order consolidation and better stock management, while reducing unnecessary requests for proposals.
The impact of PROBE extends beyond engineers—procurement teams gain greater visibility into existing references, simplifying supplier negotiations and cost reductions. Additionally, production teams avoid delays caused by duplicate orders and extended lead times.
By enabling fast and reliable part identification through 3D recognition, PROBE eliminates duplicates, facilitates group purchasing, and maximizes economies of scale. Its AI-driven approach transforms procurement management into an optimized and cost-effective strategy.
This OEM's example demonstrates that optimizing purchasing processes with an intelligent platform is not just about cost reduction—it also leads to structural improvements in a company’s internal operations. In an environment of growing economic pressure, solutions like PROBE provide a decisive competitive advantage.