Transforming Asset Management Into Intelligent, Autonomous Operations

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CAPABILITY 1: PREDICTIVE MAINTENANCE

MAS 9 analyzes historical data, IoT sensor readings, and equipment logs using machinelearning algorithms to detect early signs of failure.

This predictive capability enables:

→ 30-50% reduction in unplanned downtime→ Optimized maintenance scheduling aligned with actual asset condition→ Reduced emergency repair costs through proactive intervention→ Extended asset lifespan through preventive action

CAPABILITY 2: INTELLIGENT DECISION-MAKING

Through AI Broker, Monitor, and Predict modules, MAS 9 delivers contextual insightsand automated recommendations.

Maintenance teams access real-time asset conditions and AI-generated insights—enablingdata-driven decisions at machine speed rather than human pace.

CAPABILITY 3: PROCESS AUTOMATION

Combining Python scripting with intelligent workflows, MAS 9 automates:

→ Automatic Work Order creation upon anomaly detection→ Intelligent prioritization of maintenance interventions→ Dynamic inventory management aligned with maintenance needs→ Escalation workflows based on asset criticality

Result: Operations teams focus on strategic decisions, not routine administrative tasks.

CAPABILITY 4: CONTINUOUS LEARNING

MAS 9's AI models continuously improve as operational data accumulates.

Each action, alert, and failure outcome refines predictive accuracy. Over time, thesystem becomes increasingly intelligent—delivering better predictions and more accuraterecommendations.

This continuous improvement cycle means ROI accelerates over the asset managementsystem's lifespan.

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THE SHIFT TO AI-AUGMENTED MAINTENANCE

MAS 9 transcends traditional monitoring and prediction.

It fundamentally transforms how organizations interact with assets—moving from reactivecrisis management to proactive, sustainable strategies aligned with Industry 4.0 principles.

AI is no longer just a tool. It's a strategic partner driving reliability, sustainability,and competitive advantage.

Organizations that embrace this shift today will outperform competitors locked in manual,reactive maintenance paradigms.

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GETTING STARTED WITH AI-AUGMENTED MAINTENANCE

If you're evaluating MAS 9, consider these strategic questions:

1. Which assets would benefit most from predictive insights?
2. How can AI-driven Work Order automation reduce manual overhead?
3. What IoT data streams are available to feed ML models?
4. How will your team transition from reactive to predictive operations?
5. What measurable outcomes matter most: reduced downtime? Lower maintenance costs?Improved asset reliability?

MAS 9 and AI capabilities are available today. The question isn't whether AI-augmentedmaintenance is possible—it's whether your organization is ready to lead this shift.

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