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AI is everywhere. The possibilities are endless, but in practice, the biggest challenge often isn’t the technology itself. Where does AI actually add value? And exactly what problem does it solve? Mobina helps industrial companies not only make this transition but also implement it. &nbsp;&nbsp;<img class="image_resized" style="aspect-ratio:6016/4016;width:97.52%;" src="https://cms.aihub-oost.nl/uploads/DSC_2481_0e0c6a2141.JPG" alt="DSC_2481.JPG" srcset="https://cms.aihub-oost.nl/uploads/thumbnail_DSC_2481_0e0c6a2141.JPG 234w,https://cms.aihub-oost.nl/uploads/small_DSC_2481_0e0c6a2141.JPG 500w,https://cms.aihub-oost.nl/uploads/medium_DSC_2481_0e0c6a2141.JPG 750w,https://cms.aihub-oost.nl/uploads/large_DSC_2481_0e0c6a2141.JPG 1000w," sizes="100vw" width="6016" height="4016"><a target="_blank" rel="noopener noreferrer" href="https://mobina-services.nl/">Mobina</a>, based in Enschede, operates at the intersection of process, IT, and organization. The company supports organizations in the manufacturing, food, chemical, and construction industries, among others, with issues related to ERP systems, supply chains, and process improvement. According to Marlène Verburg, Mobina’s director and co-founder, this combination is essential. “We understand our clients’ processes, systems, and challenges. This allows us to critically assess where AI actually adds value.”According to Verburg, the greatest opportunities for AI in industry lie not in general-purpose applications that work in every organization, but rather in the processes specific to industrial companies. “You see a lot of AI solutions that are actually relevant to every sector,” she explains. “But in industry, you’re dealing with very specific processes, think engineering, production planning, or procurement. These often involve a lot of data, a lot of knowledge, and a great deal of complexity.”<h2>Impact on the Entire Supply Chain</h2>Machinery and equipment manufacturers often have thousands of parts, drawings, and technical designs that have been accumulated over the years. That information exists, but it isn’t always easy to find. As a result, engineers spend valuable time every day searching for parts that already exist but can’t be located.That may seem like a minor problem, but the consequences are significant. Parts are redesigned, inventories grow, procurement complexity increases, and margins shrink. “We often call this the ripple effect. A single new part impacts the entire supply chain,” explains Verburg. These effects can be particularly significant in companies where products are developed or configured to customer specifications. &nbsp;To address this problem, Mobina developed an Engineering AI agent that automatically analyzes and classifies existing technical documentation, parts, and designs. This makes information scattered across different systems much easier to search.“An engineer no longer needs to know exactly where something is located or what part number corresponds to it,” explains Verburg. “For example, you can search for a stainless steel part with specific properties and dimensions. You’ll then receive suggestions for parts that have already been developed.” This allows engineers to spend their time on new innovations, rather than reinventing something that already exists.<h2>Hidden Knowledge &nbsp;</h2>In other parts of the organization as well, knowledge that is often not explicitly documented is used on a daily basis. One example is production planning. Many manufacturing companies work with complex schedules that are constantly changing due to disruptions. Materials arrive late, machines break down, or orders change at the last minute. For a client, Mobina therefore developed a solution that simulates various scenarios and supports planners in making new decisions. “The question isn’t just what the optimal schedule is, but also how you help employees work with it,” says Verburg. &nbsp;<blockquote>With relatively simple applications, we’ve already seen annual savings of one to two percent.</blockquote>Mobina also sees similar opportunities in procurement. Many procurement professionals have years of experience and market knowledge. In addition, companies often have large amounts of historical data available that is rarely utilized. By combining price trends, indices, and historical procurement data, Mobina developed an AI assistant that helps make better procurement decisions. “With relatively simple applications, we’ve already seen annual savings of one to two percent,” says Verburg. For many industrial companies, that translates directly into a substantial financial impact.<h2>Start Small, Develop Together</h2>Despite all the possibilities, Mobina has consciously opted for a step-by-step approach. Large AI projects sound appealing, but in practice they often prove difficult to implement. “We don’t believe in a single large project that solves everything. You have to start small, make it tangible, and build on that.” Users are therefore involved from the start and test the solution directly in their daily work. Further development is based on those experiences.Looking ahead, Verburg expects AI to increasingly shift toward the primary production process. While many applications today are still supportive in nature, she sees opportunities for systems that actively help manage processes. Think of production environments that can respond more quickly to disruptions or, in the future, even operate partially autonomously. For Mobina, one guiding principle remains central: it’s important to start with a specific problem, not with the technology itself. &nbsp;

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How Mobina Uses AI to Unlock Hidden Knowledge in Manufacturing Companies

AI is everywhere. The possibilities are endless, but in practice, the biggest challenge often isn’t the technology itself. Where does AI actually add value? And exactly what problem does it solve? Mobina helps industrial companies not only make this transition but also implement it.