10 Ways CMMS Tools Like Stilt Are Using AI

Stilt at its core is a CMMS tool built with modern artificial intelligence architecture. We are one of the few CMMS platforms built in the last 7 years that have leveraged LLMs and AI since inception. In light of that, we decided to put together a blog on the various ways Stilt might leverage AI with our CMMS  customers. Computerized Maintenance Management System (CMMS) tools can leverage artificial intelligence (AI) in various ways to enhance their capabilities and provide more efficient maintenance management solutions. Below are 10 ways CMMS tools like Stilt utilize AI:

  1. Predictive Maintenance: AI can be used to analyze historical maintenance data, equipment performance, and sensor readings to predict when equipment is likely to fail. By implementing predictive maintenance, organizations can schedule maintenance activities proactively, reducing downtime and minimizing the risk of unexpected breakdowns.
  2. Anomaly Detection: AI can monitor real-time data from equipment and systems to detect anomalies or abnormal behavior. This allows CMMS tools to identify potential issues early on, enabling maintenance teams to address them before they become critical problems.
  3. Work Order Prioritization: AI algorithms can analyze maintenance requests and automatically prioritize work orders based on factors like safety concerns, criticality of the equipment, resource availability, and historical data. This ensures that urgent and essential tasks are handled promptly.
  4. Optimization of Maintenance Schedules: AI can optimize maintenance schedules by considering various factors, such as equipment usage patterns, historical performance data, and production schedules. This helps in reducing downtime and minimizing disruption to operations.
  5. Inventory Management: CMMS tools with AI can analyze past usage patterns and predict future inventory needs, enabling more accurate inventory management. This prevents overstocking or stockouts of spare parts and reduces maintenance costs.
  6. Natural Language Processing (NLP): AI-powered NLP can enable more intuitive and user-friendly interfaces for CMMS tools. Users can interact with the system using voice commands or text, making it easier to input data, generate reports, or retrieve information.
  7. Image and Video Analysis: AI can process images and videos from equipment inspections to detect defects, corrosion, or other issues that may not be immediately visible to the human eye. This can streamline the inspection process and improve the accuracy of identifying maintenance requirements.
  8. Energy Optimization: AI algorithms can analyze energy consumption patterns and suggest energy-saving measures to optimize equipment performance and reduce overall energy costs.
  9. Resource Allocation: AI can assist in allocating maintenance resources efficiently by considering factors like technician skill sets, location, workload, and required expertise for specific tasks.
  10. Continuous Improvement: By continuously analyzing maintenance data and performance metrics, AI can identify areas where improvements can be made in maintenance processes and strategies.

Overall, integrating AI into CMMS tools empowers organizations to move from reactive maintenance practices to proactive and data-driven maintenance strategies. This results in reduced downtime, increased equipment reliability, optimized resource utilization, and improved overall operational efficiency. Demo Stilt to find out how we can update your CMMS with a more AI-driven solution!

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