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      • IBM TRIRIGA (MREF)
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      • IBM TRIRIGA (MREF)
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About Maximo

 

IBM Maximo is an Enterprise Asset Management (EAM) platform used by organizations to manage the entire lifecycle of their assets—such as equipment, facilities, vehicles, infrastructure, and more—across industries like manufacturing, utilities, oil & gas, transportation, and government.

In simpler terms, Maximo helps companies:

  • Track assets: Know what assets they own, where they are, and their condition.
     
  • Schedule maintenance: Plan preventive and predictive maintenance to avoid costly downtime.
     
  • Manage work orders: Create, assign, and track maintenance and repair tasks.
     
  • Control inventory: Manage spare parts and materials efficiently.
     
  • Integrate data: Connect with IoT sensors, ERP systems, and AI tools for real-time insights.
     

Key components of Maximo include:

  1. Asset Management – Detailed tracking of asset details, history, and performance.
     
  2. Work Management – Planning, scheduling, and executing maintenance tasks.
     
  3. Inventory & Procurement – Managing spare parts, suppliers, and purchasing.
     
  4. Preventive & Predictive Maintenance – Reducing failures and extending asset life.
     
  5. Service Management – Handling service requests and service-level agreements (SLAs).
     
  6. Mobile & AI Integration – Technicians can work in the field via mobile apps, with AI providing decision support.

MAS Applications

MAS Manage

Deep functional expertise on the core Asset management functionalities, i.e Asset, work order, Inventory, Contract, Purchasing.

Mobile configuration via Maximo application framework tool and understanding of its functional modules.

Creating and segmenting different linear assets related to transportation industry solutions and their relatio

Deep functional expertise on the core Asset management functionalities, i.e Asset, work order, Inventory, Contract, Purchasing.

Mobile configuration via Maximo application framework tool and understanding of its functional modules.

Creating and segmenting different linear assets related to transportation industry solutions and their relation ship with spatial maps.

Creating inbound integration via map manager and scheduling cron to create linear asset automatically in maximo and linking those assets to the esri maps.



MAS Monitor

 

Transform raw IoT and operational data into actionable insights through advanced monitoring and anomaly detection. MAS Monitor allows you to:

  • Integrate IoT & Sensor Data – Import telemetry, meter readings, and SCADA feeds for real-time asset performance tracking.
     
  • Custom KPIs & Metrics – Build tailored dashboards with KPIs, thresholds, an

 

Transform raw IoT and operational data into actionable insights through advanced monitoring and anomaly detection. MAS Monitor allows you to:

  • Integrate IoT & Sensor Data – Import telemetry, meter readings, and SCADA feeds for real-time asset performance tracking.
     
  • Custom KPIs & Metrics – Build tailored dashboards with KPIs, thresholds, and alerts to identify abnormal operating conditions.
     
  • Anomaly Detection – Utilize rule-based and AI-driven algorithms to detect deviations early, preventing costly downtime.
     
  • Cross-System Data Correlation – Combine Monitor insights with Maximo Manage data for end-to-end visibility into asset health and performance.

MAS Health and Predict

MAS Health and Predict

 

Leverage AI-powered asset health scoring and predictive modeling to extend asset life and reduce unplanned outages. With MAS Health and Predict, you can:

  • Health Scoring Models – Configure health indices based on asset condition, performance trends, and failure history.
     
  • Predictive Maintenance – Use statistical models and machine learning 

 

Leverage AI-powered asset health scoring and predictive modeling to extend asset life and reduce unplanned outages. With MAS Health and Predict, you can:

  • Health Scoring Models – Configure health indices based on asset condition, performance trends, and failure history.
     
  • Predictive Maintenance – Use statistical models and machine learning to forecast failures before they occur, allowing for proactive intervention.
     
  • Template-Driven Modeling – Apply prebuilt templates that merge Manage, Monitor, and Health data to predict remaining useful life (RUL).
     
  • Risk-Based Prioritization – Focus resources on high-risk assets with the highest probability of failure.
     
  • KPI-Driven Decisions – Monitor asset performance KPIs over time to refine maintenance strategies and improve reliability.
     
  • Integration with Digital Twins – Simulate asset behavior to test scenarios and optimize maintenance planning.

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