Azzier Artificial Intelligence (AI)

Ask Azzier AI

Tero is currently incorporating artificial intelligence (AI) into Azzier, to enhance its capabilities and provide advanced functionalities. We are drawing together 44 years of data and training our AI to detect and diagnose faults, predict and prevent failure, and guide maintenance professionals on best practice procedures.

Our Azzier developers created the Azzier Chatbot below. It’s trained to answer questions about Azzier. Enter a question in the box below and see what Azzier has to say about itself. Read on below and learn more about what we are doing to enhance Azzier CMMS with artificial  Intelligence (AI).

Learn More About What We Are Doing

We are working on both Natural Language Processing (NLP) and Image/Video Analysis, to enable Azzier CMMS to interpret and process inputs, such as maintenance requests, comments, and visual data. Our AI algorithms can then analyze these to identify problems, provide diagnostics, or prescribe potential solutions, supporting technicians in troubleshooting and decision-making processes. System users may interact with the system using spoken or written language, as well as video streaming, improving the users’ experience and facilitating efficient and accurate data entry and problem resolution.

Within Azzier’s Predictive Maintenance Module, AI algorithms may analyze historical maintenance data, equipment sensor data, and other relevant parameters to identify patterns and predict equipment failures. By leveraging AI for predictive maintenance, Azzier may generate alerts and work orders proactively, enabling maintenance teams to address potential issues before they lead to equipment outages. Azzier is already doing this with SCADA integration, automating the work order generation and email notification features. AI will take this to a whole new level, further minimizing downtime, optimizing maintenance schedules, and improving asset performance.

With AI under the hood, Azzier will optimize work order scheduling and allocation. By analyzing factors such as technician availability, skill sets, certifications, location, and urgency of tasks, Azzier CMMS can intelligently assign work orders to the most appropriate technicians. AI algorithms may also consider historical data to estimate task durations more accurately, enabling better planning and resource allocation.

We are also exploring how AI algorithms can optimize Inventory Management by analyzing historical maintenance data, equipment usage patterns, and failure rates to optimize parts inventory management. By predicting future demand, Azzier can suggest optimal stock levels, reorder points, and identify critical spare parts. AI-powered inventory management reduces the risk of stockouts, minimizes carrying costs, and improves overall inventory efficiency.

Azzier’s AI may continuously tracking Anomalies that are outside normal operating parameters. Algorithms can learn normal operating patterns of equipment and systems by processing large amounts of both manually entered and sensor-collected data. By comparing real-time data to established baselines, Azzier can detect anomalies that may indicate equipment malfunctions or abnormal behavior. Anomaly Detection enables early detection of issues, triggering appropriate maintenance actions and preventing unexpected failures.

Data Analytics capabilities may be enhanced by automatically analyzing vast amounts of maintenance data. AI algorithms may identify trends, correlations, and patterns that might not be apparent to human analysts. By providing actionable insights, Azzier CMMS will help maintenance teams make informed decisions, optimize maintenance strategies, and improve overall efficiency. These data will feed dashboards providing real-time visualization with drill-down into AI-driven reports and analytics to give top-level managers more control over their maintenance practice than ever before.

Azzier will be learning continuously and improving through monitoring user interactions, maintenance data, and user feedback. This continuous learning allows Azzier to improve its performance over time, adapting to evolving maintenance needs, and enhancing its predictive and diagnostic capabilities.

Scroll to Top