• SIGMA's AI acts actively and operationally, not just as a recommendation, but as a decision agent.
• Operational decisions are executed automatically based on intelligent data and rules.
• There is a measurable reduction in operational errors through AI-driven automation.
• AI learns continuously from historical data and operational behavior (machine learning).
• The system guarantees high reliability in automated decisions.
• Processes can operate with minimal or no human intervention.
• AI is applied in critical maintenance and operational processes.
• Total integration with work orders, planning, and execution.
• All decisions have complete traceability (audit trail).
• Direct impact on improving indicators such as MTBF and MTTR.

The equipment controls the operating time by date, hour, minute, and second, and can work online or offline. The main collection topics are:

• Operating time: Monitors the operating time or use of machines and vehicles, automatically informing the next maintenance date every time the machine stops working.
• Hours per shift/machine/operator: Reports the hours worked per shift, machine, and operator.
• Activation count: Counts the number of times the equipment was activated (running/stopped), and records the reason for the stop.
• Production control: Monitors the quantity of parts produced, the manufacturing time, and the start and end time of manual work, inspections, and routes.
• Industrial variables (via dashboard): The dashboard can collect data from sensors that monitor variables such as temperature, pressure, humidity, and liquid levels.

SIGMA CMMS now has a new application developed in Flutter, allowing full operation on mobile devices.

• Opening and execution of work orders in the field
• Registration of photos, videos, and audios
• Reading QR Code or RFID of equipment
• Asset history consultation
• Digital checklists
• Online or offline operation

This allows technicians to perform tasks directly in the field, eliminating paper forms. Mobility is an important feature in modern CMMS, as technicians can access and record activities directly via cell phone or tablet.

• KPIs aligned with the organization's strategic objectives.
• Data updated in real time.
• High data reliability and integrity.
• Dashboards used continuously by management.
• Predictive, prescriptive, and historical indicators.
• Direct support for decision-making.
• Detailed analysis with drill-down.
• Total integration between operation and maintenance.
• Standardization of indicators between units.
• Automatic generation of insights via AI.

• Communication via email, app, SMS, and other integrated channels.
• Intelligent, contextualized, and noise-free notifications.
• Personalization by user profile.
• Real-time alerts.
• Read and execution confirmation.
• Elimination of duplicate notifications.
• Support for quick decision-making.
• Prioritization by criticality.
• Intelligent control of notification volume.
• Integration with critical operational events.

• All orders feature automatic real-time tracking.
• Intelligent alerts for delays, deviations, and operational risks.
• Reduced need for manual management and human follow-up.
• Complete visibility of the status of activities.
• Automatic notification to responsible parties.
• Automatic identification of operational bottlenecks.
• Complete and traceable tracking history.
• Increased compliance with deadlines and SLAs.
• Performance indicators linked to monitoring.
• Scalability for multiple units and complex operations.

• Automated execution of critical operational processes.
• Drastic reduction of the need for manual intervention.
• Automatic generation and execution of work orders.
• Intelligent management of operational exceptions.
• Increased operational reliability and predictability.
• Complete traceability of automated actions.
• Risk control with configurable validation rules.
• Total adherence to the organization's business rules.
• Significant reduction in rework.
• Continuous evolution of processes through system learning.

• Resource allocation performed automatically and optimized.
• Intelligent workload balancing between teams and assets.
• Automatic consideration of technical skills and specialties.
• Significant reduction in idleness and operational overload.
• Elimination of bottlenecks through dynamic task distribution.
• Automatic response to emergency events and operational changes.
• Prioritization based on criticality, risk, and operational impact.
• Continuous improvement of execution times.
• Full visibility of the capacity vs. demand relationship.
• Consideration of constraints such as shifts, availability, and operational calendar.

Uses Machine Learning algorithms to understand historical KPI behavior. When the system detects a significant deviation from the expected pattern, it sends an automatic alert (notify), avoiding manual monitoring.

• Intelligent Alerting: Unlike simple alerts based only on a fixed value, intelligent alerts learn seasonalities and only notify if there is a real anomaly, reducing false alarms.
• Active Monitoring: Transforms passive data into active intelligence, where the system warns the manager about the problem instead of the manager looking for it.
• Reduction of Manual Analysis: No need to check spreadsheets or dashboards daily to identify performance drops.
• Speed in Response: Real-time alerts allow correcting deviations before they severely impact results.
• Operational Efficiency: Allows analysts to focus on solution strategies, not searching for failures.

• Systematic and automated execution of the PDCA cycle (Plan, Do, Check, Act).
• Total integration between planning and actual execution.
• Automatic registration and monitoring of corrective and preventive actions.
• Automatic generation of analyses for continuous improvement.
• Complete traceability: failure → cause → action → result.
• Automatic updating of indicators.
• Standardization of improvement processes.
• Integration with preventive, predictive, and corrective maintenance.
• "Check" phase based on reliable, real-time data.
• Strategic and operational application of the PDCA.

• Automatic transformation of data into actionable insights.
• Generation of intelligent visual charts and analyses.
• Automatic identification of patterns and anomalies.
• Reports generated without manual intervention.
• Contextual interpretation of data.
• Reduction in the need for deep technical analysis.
• Customization of reports by profile.
• Correlation of multiple operational variables.
• Significant reduction in analysis time.
• Direct impact on performance and operational results.