In 1900, the concept of refining steel into steel bearings necessitated the use of gears, a tool that had existed since ancient times. However, as automation emerged over the latter half of the 20th century, the need for more efficient processes began to arise. By 1960, companies were drawing upon conveyor belts to reduce production times and lower costs. The shift from manual grinding to industrial automation was a significant milestone in the history of manufacturing. Today, the process remains driven by conveyor belts and a global array of automation technologies, including AI-based tools designed to improve efficiency and precision.
The Schaeffler factory in Hamburg exemplifies this evolution. The company’s steel wire production phase included the cutting and pressing of rough balls, a process that required precision to produce ε, a ball as small as a tenth of a micron. These balls were then hardened in a series of furnaces, a step that involved multiple levels of targeting. Finally, three increasingly precise grinders were used to refine the ball for use in high-precision applications. This final scale of precision is unmatched in modern industry and forms the backbone of low-friction joints in lathes, car engines, and other critical equipment.
Despite the plateau in defect frequency observed over the years, defects persist and pose significant risks. When defects are detected, the question becomes: What caused this? As the toolariat of the assembly line grows, it becomes increasingly complex to isolate and identify the root cause of a problem. This is where AI tools come into play, providing insights that traditional testing methods cannot. In 2022, Schaeffler aligned with Microsoft’s Factory Operations Agent (FOA), a cutting-edge AI tool developed by Microsoft’s large language models. FOA is designed to handle the complexities of manufacturing data, enabling it to analyze defects across multiple machines and pinpoint their causes. This tool has revolutionized quality control by providing more precise, timely answers to what users are asking.
The FOA is an example of Industry-Data APIs, which link machine intelligence with decision-making. This technology replaces manual defect tracking with automated analysis, enabling faster problem resolution and cost savings. Schaeffler learned from FOA, its 1.4 million+ highly-trained users, and incorporated the same. As a result, Schaeffler became an inheritor of the FOA’s success, leveraging the company’s deep understanding of Industry-Data APIs. This shift not only enhances manufacturing efficiency but also fosters collaboration and innovation among manufacturers.
hi industrial automation and AI are driving industry-wide change, transforming how we handle challenges such as automation and maintenance. In 1960, the first digital factory line was developed, and by 2015, I Verbich, a scientist, estimated that AI could reduce manufacturing costs by 200%. The company that introduced FOA was the first to adopt this approach. “The factory is a process, not a set of tools,” said Kathleen Mitford, company’s corporate vice president for global industry marketing. “The question isn’t how to automate, but how to handle the reality of this reality.” While the FOA offers a way to analyze defect patterns more effectively than traditional testing methods, it doesn’t replace testing. It becomes a powerful tool for problem-solving, providing data that’s both specific and actionable.
This shift toward automation and AI marks the end of a era. It’s the beginning of a future where machines, analyzing data, can identify issues before they can be fixed. The Schaeffler factory’s adoption of FOA is a glimmer of hope, but it reflects a broader trend toward relying on Industry-Data APIs. This technology, developed by Microsoft and designed for manufacturers like Schaeffler, reintroduces machines into a world that’s increasingly dependent on collaboration and data-driven decision-making. It’s signals like these that define the next era of manufacturing, where machines are no longer prisoners of their data but part of the fabric of the process. As the world moves toward a digital future, the integration of AI and Industry-Data APIs will be as essential as ever.
