Industrial manufacturing thrives on the expertise of its frontline operators, whose skills and decision-making capabilities are unmatched. Yet, this human reliance brings challenges, such as the loss of institutional knowledge with retiring workers and shorter tenures of younger generations. How can companies preserve this critical expertise and make it accessible to all operators, regardless of experience? oee.ai has the answer with its generative AI-powered Root-Cause Problem-Solving function, turning human insights into actionable, multilingual guidance. The result? Faster repairs, reduced downtime, and measurable improvements in Overall Equipment Effectiveness (OEE).
Industrial manufacturing heavily relies on humans—operators equipped with highly advanced sensory capabilities, exceptional dexterity, sensitive handling skills, and unmatched decision-making abilities. A heartfelt thank you to our dedicated factory frontline staff for their invaluable contributions!
However, this reliance also presents challenges. Globally, an aging workforce is a growing trend, and as experienced employees retire, decades of invaluable knowledge often leave with them. Meanwhile, younger generations are more likely to change jobs frequently, limiting the time they spend mastering the intricacies of running equipment at peak efficiency.
The solution? Companies must capture and retain the expertise housed within their employees’ minds. That’s where oee.ai’s generative AI-powered Root-Cause Problem-Solving function comes in, preserving and leveraging this critical knowledge for long-term success.
The process is simple and highly effective. It begins with capturing the expertise of various operators. Whenever equipment experiences a loss and is repaired, oee.ai prompts the operator during the restart to document the cause and solution. This can be done in any language, either typed or spoken directly to oee.ai.
Knowledge collection occurs over an extended period, especially when dealing with a complex loss reason catalog. Capturing multiple instances of each loss reason—potentially from different operators—ensures comprehensive coverage. A detailed catalog enables operators to document more precise troubleshooting advice, which results in even more specific and actionable insights later.
The data is processed through our generative AI module, which transforms it into clear and concise guidance for equipment operators. The AI generates simple, easy-to-understand instructions, often in bullet points or short sentences, with no restriction on the output language. Whether your equipment is operated in the US, Poland, Italy, or Germany, oee.ai provides unified root-cause problem-solving knowledge in any language, for any location.
The collection and training process incorporates a human-in-the-loop approach to ensure quality control. This crucial step is typically managed by shift leaders, maintenance staff, or highly experienced operators. Through this additional review, the AI-generated content can be refined, adjusted, or entirely overwritten. For instance, if a specific equipment issue requires the attention of a trained electrician rather than an equipment operator, this can be clearly indicated. This collaborative process ensures that the root-cause problem-solving knowledge remains accurate, safe, and aligned with operational best practices.
Once the knowledge base is live, oee.ai provides operators with a list of root-cause problem-solving suggestions directly on their tablets or other devices whenever the specific loss occurs. This functionality works seamlessly with automatic loss reason ingestion from the PLC but can also operate using manual loss reason entries. With these insights, even less experienced operators can quickly determine the appropriate corrective action or make an informed decision to call a maintenance specialist. This accelerates the Mean Time to Repair (MTTR), minimizing downtime, and ultimately improving Overall Equipment Effectiveness (OEE). This feature applies to all three major loss categories: Availability, Performance, and Quality, ensuring comprehensive support for optimized operations.
Based on oee.ai’s extensive experience, implementing our solution leads to an average reduction in Mean Time to Repair (MTTR) by 10%. The resulting OEE improvements depend on factors such as the structure of the equipment’s losses and the ability to accurately define and communicate effective countermeasures.
Across the range of equipment we monitor, OEE improvements typically fall between 2%pts on the lower end and up to 5%pts on the higher end. The improvements are dependent on the current OEE levels. These gains highlight the significant impact of precise root-cause problem-solving and efficient corrective actions on overall operational performance.
If you are interested in how oee.ai can help you gain a competitive advantage in your industry, don’t hesitate and contact us at info@oee.ai.