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Predictive Maintenance & Work Orders

IoT sensor monitoring with predictive failure alerts 7-14 days ahead and auto-generated work orders reducing unplanned downtime 40-60% and maintenance costs 30-50% versus reactive maintenance.

Business Outcome

time reduction in work order generation and assignment

Complexity:

Medium

Time to Value:

3-6 months

Why This Matters

What It Is

IoT sensor monitoring with predictive failure alerts 7-14 days ahead and auto-generated work orders reducing unplanned downtime 40-60% and maintenance costs 30-50% versus reactive maintenance.

Current State

(Traditional)

1. Equipment operates normally until failure occurs (HVAC stops cooling, elevator breaks down). 2. Employee discovers failure and reports to facilities via phone or email: 'AC not working in Building 2'. 3. Facilities admin manually creates work order, assigns to technician. 4. Technician dispatched 2-4 hours later, diagnoses failed compressor requiring replacement part. 5. Part ordered, arrives in 2-5 days, technician returns to complete repair. 6. Total downtime 2-7 days due to reactive response. 7. 60-80% of downtime is unplanned (run-to-failure maintenance).

Characteristics

• IBM Maximo
• FMX
• Eptura
• IoT Sensors
• AI and Machine Learning Analytics
• Email Communication Tools
• Mobile Apps
• Excel (limited use)

Pain Points

⚠ Data Integration Challenges: Difficulty integrating diverse sensor data and legacy systems into a unified CMMS platform.
⚠ Manual Processes: Reliance on manual data entry or inspections can delay issue detection and work order generation.
⚠ Resource Allocation: Inefficient prioritization or assignment of work orders can lead to delays and underutilized technician skills.
⚠ Cost and Complexity: Implementing advanced predictive maintenance requires upfront investment in sensors, software, and training.
⚠ Compliance and Documentation: Ensuring maintenance processes meet multiple standards adds complexity to workflow design and data handling.
⚠ Scalability Issues: Smaller organizations may struggle with scaling predictive maintenance due to limited budgets or technical expertise.
⚠ Dependence on Technology: Over-reliance on automated systems may lead to challenges if systems fail or data is inaccurate.

Future State

(Agentic)

1. Predictive Maintenance Agent monitors IoT sensors on equipment: HVAC compressor vibration increased 30%, operating temperature elevated 15°F above normal baseline. 2. Agent applies ML model predicting compressor failure in 7-14 days with 85% confidence. 3. Work Order Automation Agent generates preventive work order automatically: 'HVAC Compressor - Predicted Failure in 10 days, schedule replacement before breakdown'. 4. Agent checks parts Inventory Management, auto-orders compressor if not in stock (arrives in 2-3 days, before predicted failure).

Agent assigns work order to certified HVAC technician with availability during predicted timeframe.
Technician replaces compressor proactively during scheduled maintenance window (no unplanned downtime).

7. 40-60% unplanned downtime reduction and 30-50% maintenance cost savings through predictive intervention before failures occur.

Characteristics

• IoT sensor data (vibration, temperature, pressure, current draw)
• Equipment operating baselines and normal ranges
• Historical equipment failure patterns and fault signatures
• Work order history with failure modes and resolution times
• Parts Inventory Management availability and lead times
• Technician skills, certifications, and availability
• Maintenance schedules and downtime windows

Benefits

✓ 40-60% unplanned downtime reduction through predictive maintenance
✓ 30-50% maintenance cost savings (avoid emergency repairs, overtime, rush shipping)
✓ 7-14 day advance warning enables proactive intervention before failures
✓ Auto-generated work orders eliminate 2-4 hour manual creation delay
✓ Equipment lifespan extended 20-30% through condition-based maintenance
✓ Parts pre-ordered before failure (in stock when technician arrives)

Is This Right for You?

39% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

• Applicable across multiple industries
• Higher complexity - requires more resources and planning
• Moderate expected business value
• Time to value: 3-6 months
• (Score based on general applicability - set preferences for personalized matching)

You might benefit from Predictive Maintenance & Work Orders if:

You're experiencing: Data Integration Challenges: Difficulty integrating diverse sensor data and legacy systems into a unified CMMS platform.
You're experiencing: Manual Processes: Reliance on manual data entry or inspections can delay issue detection and work order generation.
You're experiencing: Resource Allocation: Inefficient prioritization or assignment of work orders can lead to delays and underutilized technician skills.

This may not be right for you if:

High implementation complexity - ensure adequate technical resources
Requires human oversight for critical decision points - not fully autonomous

Parent Capability

Facilities Management & Maintenance

Transforms reactive break-fix maintenance into predictive asset management with significant downtime reduction, faster repairs, and cost savings through IoT monitoring, AI predictions, and automated work order management.