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Published: February 3, 2025
Reading Time: 16 minutes
Author: Stratagem Systems
Why AI Predictive Maintenance is Transforming Asset Management
Unplanned equipment downtime costs industrial companies $50 billion annually. AI-powered predictive maintenance reduces this by 35-50%, cuts maintenance costs by 20-40%, extends asset lifespan by 20-40%, and delivers ROI of 400-2,000% within the first year.
Traditional maintenance strategies—reactive "run-to-failure" and time-based preventive maintenance—are fundamentally inefficient. Reactive maintenance causes costly unplanned downtime, production losses, and collateral damage. Preventive maintenance performs unnecessary work on healthy equipment while missing emerging failures between scheduled intervals.
AI predictive maintenance uses machine learning to continuously monitor equipment health, detect early failure indicators invisible to human inspection, and predict exactly when components will fail—enabling perfectly timed maintenance that prevents downtime without wasteful early replacement.
"Our AI predictive maintenance system reduced unplanned downtime from 127 hours to 18 hours annually—a 86% reduction. We're catching bearing failures 3-4 weeks before they occur, preventing catastrophic breakdowns that previously cost us $285K per incident. Year 1 ROI was 1,547%."
Robert Martinez
Director of Maintenance, Automotive Manufacturing Plant
The Business Impact of AI Predictive Maintenance
Organizations implementing AI predictive maintenance report transformative improvements:
- Downtime Reduction: 35-50% decrease in unplanned outages
- Maintenance Cost Savings: 20-40% reduction through optimized scheduling and reduced emergency repairs
- Asset Lifespan Extension: 20-40% increase through optimal operating conditions
- Spare Parts Inventory: 20-30% reduction through accurate demand forecasting
- Safety Improvements: 25-35% fewer incidents from equipment failures
- Energy Efficiency: 10-20% improvement through optimal equipment performance
- Failure Detection Lead Time: 7-45 days advance warning vs. reactive response
- Maintenance Productivity: 25-35% increase through prioritized work and reduced false alarms
Maintenance Strategy Evolution: From Reactive to Predictive
| Approach | Description | Advantages | Disadvantages |
|---|---|---|---|
| Reactive (Run-to-Failure) | Fix equipment only after it breaks | No upfront cost, simple approach | Unplanned downtime (2-10x more costly), safety risks, collateral damage, production losses |
| Preventive (Time-Based) | Scheduled maintenance at fixed intervals (hours/cycles) | Planned downtime, reduced failures vs reactive | 30-50% unnecessary work, misses 40-60% of failures between intervals, high labor/parts costs |
| Condition-Based | Monitor equipment health, maintain when thresholds exceeded | Maintenance based on actual need, reduced waste vs preventive | Limited to simple threshold rules, reactive to current state, no failure prediction |
| Predictive (AI-Powered) | ML models predict failures 7-45 days in advance based on patterns | Prevents 70-90% of failures, optimal timing, minimal waste, planned downtime, spare parts optimization | Upfront investment ($150K-$2M+), requires sensors and data infrastructure, 6-12 month implementation |
| Prescriptive (Advanced AI) | AI predicts failures AND recommends optimal maintenance actions, schedules, resource allocation | Autonomous optimization, considers production schedules, parts availability, multi-asset dependencies | Higher complexity, requires mature predictive capability first, integration with ERP/CMMS |
Cost Comparison
| Maintenance Strategy | Relative Cost Index | Typical Maintenance Budget Allocation |
|---|---|---|
| Reactive | 100 (baseline - most expensive) | 100% reactive |
| Preventive | 60-70 (30-40% savings vs reactive) | 70% preventive, 30% reactive |
| Condition-Based | 50-60 (40-50% savings vs reactive) | 35% condition-based, 50% preventive, 15% reactive |
| AI Predictive | 35-45 (55-65% savings vs reactive) | 50% predictive, 30% preventive, 10% condition-based, 10% reactive |
How AI Predictive Maintenance Works
The Four-Stage Process
Stage 1: Continuous Data Collection
IoT sensors continuously monitor equipment health parameters:
- Vibration: Accelerometers detect bearing wear, misalignment, imbalance (most common failure indicator)
- Temperature: Thermal sensors identify overheating, friction, electrical issues
- Acoustic: Ultrasonic microphones detect air/gas leaks, electrical arcing, mechanical stress
- Pressure/Flow: Monitor hydraulic/pneumatic systems, fluid dynamics
- Electrical: Current, voltage, power quality for motors, transformers, circuits
- Oil Analysis: Particle counters detect contamination, wear metals
- Visual: Computer vision monitors corrosion, cracks, leaks
- Operational: RPM, load, cycles, runtime, starts/stops from control systems
Typical sampling rates: 1-100 Hz for most applications, up to 50 kHz for specialized vibration analysis.
Stage 2: AI Pattern Recognition
Machine learning algorithms analyze sensor data to identify failure patterns:
- Baseline Learning: AI learns normal operating signatures for each asset under various conditions (load, temperature, speed)
- Anomaly Detection: Unsupervised learning identifies deviations from normal patterns
- Failure Signature Recognition: Supervised learning trained on historical failures recognizes specific fault patterns (e.g., bearing defect frequencies in vibration spectra)
- Degradation Tracking: Time series models monitor gradual deterioration trends
- Multi-Sensor Fusion: Combines signals from multiple sensors for higher accuracy (vibration + temperature + acoustic)
Stage 3: Predictive Analytics
AI forecasts equipment failures and remaining useful life (RUL):
- Failure Probability: Calculates likelihood of failure over time horizons (7 days, 30 days, 90 days)
- RUL Estimation: Predicts remaining operating hours before maintenance required
- Root Cause Analysis: Identifies specific failure modes (bearing, seal, electrical winding, etc.)
- Severity Scoring: Prioritizes issues by criticality and business impact
- Confidence Intervals: Provides uncertainty estimates for predictions
Stage 4: Prescriptive Actions
Advanced systems recommend optimal maintenance decisions:
- Maintenance Scheduling: Balance failure risk vs. production impact vs. resource availability
- Work Order Generation: Auto-create maintenance tasks in CMMS
- Parts Forecasting: Predict spare parts needs for proactive procurement
- Resource Optimization: Assign technicians based on skills, location, workload
- Root Cause Mitigation: Recommend operating parameter adjustments to slow degradation
Prevent Costly Equipment Failures
Get a free predictive maintenance assessment. We'll analyze your equipment reliability data, identify high-risk assets, and calculate your potential ROI from AI predictive maintenance. Schedule your consultation today.
Key Applications by Industry
1. Manufacturing
Critical Assets: CNC machines, industrial robots, conveyor systems, hydraulic presses, motors, pumps
Common Failure Modes: Bearing wear, gearbox degradation, motor winding failures, hydraulic seal leaks
Business Impact: Production line downtime costs $5K-$50K per hour depending on industry
ROI Drivers: 40-60% downtime reduction, 25-35% maintenance cost savings, improved OEE from 65% to 85%+
2. Energy & Utilities
Critical Assets: Turbines, generators, transformers, circuit breakers, compressors, heat exchangers
Common Failure Modes: Turbine blade cracking, generator winding insulation breakdown, transformer oil degradation
Business Impact: Forced outages cost $500K-$5M per event, regulatory compliance issues
ROI Drivers: Prevent catastrophic failures ($2M-$10M each), extend asset life 20-30%, compliance assurance
3. Transportation & Logistics
Critical Assets: Fleet vehicles, aircraft engines, rail locomotives, material handling equipment
Common Failure Modes: Engine component failures, brake system wear, transmission issues, tire degradation
Business Impact: Vehicle/aircraft downtime, missed deliveries, safety incidents, warranty claims
ROI Drivers: 30-45% reduction in unscheduled maintenance, 15-25% fleet availability improvement, safety enhancement
4. Oil & Gas
Critical Assets: Drilling equipment, pumps, compressors, pipelines, offshore platform systems
Common Failure Modes: Pump seal failures, compressor valve issues, corrosion, equipment fatigue
Business Impact: Production shutdowns cost $1M-$5M per day, safety/environmental risks
ROI Drivers: Prevent production losses worth millions, extend maintenance intervals by 30-50%, safety improvement
5. Mining
Critical Assets: Haul trucks, excavators, conveyors, crushers, mills, drills
Common Failure Modes: Tire failures, hydraulic system leaks, bearing wear, structural cracks
Business Impact: Equipment downtime halts production, repair costs $50K-$500K per major failure
ROI Drivers: 35-50% downtime reduction, 40-60% maintenance cost savings, 20-30% asset life extension
6. Data Centers
Critical Assets: HVAC chillers, UPS systems, generators, cooling towers, electrical distribution
Common Failure Modes: Chiller compressor failures, UPS battery degradation, generator starting system issues
Business Impact: Downtime costs $5K-$9K per minute, SLA violations, customer churn
ROI Drivers: 99.99%+ uptime assurance, 25-40% energy savings through optimization, deferred capital investment
AI & IoT Technologies Enabling Predictive Maintenance
1. Machine Learning Algorithms
Anomaly Detection (Unsupervised Learning):
- Isolation Forest: Identifies outliers in high-dimensional sensor data
- Autoencoders: Neural networks learn normal patterns, flag reconstruction errors as anomalies
- One-Class SVM: Defines normal operation boundary, detects deviations
- k-Means Clustering: Groups similar operating states, identifies unusual patterns
Failure Classification (Supervised Learning):
- Random Forests: Ensemble learning for fault type classification (bearing, gearbox, motor, etc.)
- XGBoost/LightGBM: Gradient boosting for high-accuracy failure prediction
- Deep Neural Networks: Learn complex non-linear relationships in multi-sensor data
- Convolutional Neural Networks (CNN): Process spectrograms for vibration/acoustic analysis
Time Series Forecasting:
- LSTM (Long Short-Term Memory): Recurrent neural networks capture temporal dependencies
- Prophet: Forecasting remaining useful life with trend and seasonality
- ARIMA/SARIMA: Statistical models for degradation trend prediction
2. IoT Sensor Technologies
| Sensor Type | What It Detects | Typical Cost Range |
|---|---|---|
| Wireless Vibration Sensors | Bearing wear, imbalance, misalignment, looseness, resonance | $200-$1,500 per sensor |
| Thermal Cameras/Sensors | Overheating, electrical hotspots, insulation breakdown, friction | $50-$500 per sensor, $3K-$50K cameras |
| Acoustic/Ultrasonic | Air/gas leaks, electrical arcing, mechanical stress, cavitation | $800-$5,000 per sensor |
| Current/Voltage Sensors | Motor issues, electrical imbalance, power quality, insulation degradation | $100-$1,000 per sensor |
| Pressure Transducers | Hydraulic/pneumatic system leaks, pump cavitation, line blockages | $100-$800 per sensor |
| Oil Quality Sensors | Contamination, wear particles, viscosity changes, water ingress | $1,000-$5,000 per sensor |
3. Edge Computing
Processing data at the asset for low-latency insights:
- Real-Time Processing: Run ML models on edge devices (NVIDIA Jetson, Intel NUC) for millisecond response
- Reduced Bandwidth: Process 99% of data locally, send only anomalies/predictions to cloud (reducing data costs by 90%+)
- Offline Operation: Continue monitoring even without network connectivity
- Cost: $500-$3,000 per edge gateway
4. Digital Twin Technology
- Virtual Asset Models: Physics-based simulations of equipment behavior
- Scenario Testing: Simulate impact of operating parameter changes on equipment health
- RUL Refinement: Combine physics models with ML for more accurate predictions
- Fleet Learning: Aggregate insights across identical assets to improve predictions
AI Predictive Maintenance Platform Comparison
| Platform | Core Capabilities | Best For | Pricing |
|---|---|---|---|
| GE Digital APM (Asset Performance Management) | Condition monitoring, failure prediction, RUL estimation, work order integration, asset health dashboards | Large industrial enterprises, energy, manufacturing | $500K-$2M+ implementation |
| IBM Maximo Application Suite | CMMS integration, Watson AI for anomaly detection, mobile inspections, computer vision | Organizations with existing IBM infrastructure | $300K-$1.5M annually |
| SAP Predictive Asset Insights | IoT sensor integration, failure prediction, ERP/EAM integration, fleet benchmarking | SAP customers, asset-intensive industries | $200K-$800K annually |
| Uptake Fusion | Industrial AI platform, pre-built models for common assets, prescriptive actions, mobile apps | Transportation, construction, manufacturing fleets | $150K-$600K annually |
| C3 AI Predictive Maintenance | Enterprise AI platform, multi-sensor fusion, digital twin, advanced analytics | Large enterprises, complex assets, utilities | $400K-$1.5M+ annually |
| Senseye Predictive Maintenance | Cloud-based, AutoML, easy deployment, pre-built integrations, transparent AI | Mid-market manufacturers seeking quick deployment | $80K-$300K annually |
| Azure IoT + ML | Build custom solutions on Azure platform, IoT Hub, ML Studio, Power BI | Organizations with Azure commitment, custom requirements | $200K-$800K dev + $20K-$80K/month |
| AWS IoT + SageMaker | Custom ML development, IoT Core, Lookout for Equipment (automated ML), S3 data lake | AWS customers, data science teams | $150K-$600K dev + $15K-$60K/month |
AI Predictive Maintenance Implementation Roadmap
Phase 1: Assessment & Planning (Weeks 1-4)
- Asset Criticality Analysis: Identify highest-impact equipment based on downtime cost, failure frequency, safety risk
- Current State Baseline: Document current maintenance costs, downtime hours, MTBF, MTTR
- Data Availability Assessment: Review existing SCADA, historian, CMMS data quality and coverage
- ROI Modeling: Calculate expected benefits and payback period
- Platform Selection: Evaluate commercial vs. custom solutions
Phase 2: Pilot Asset Selection & Sensor Deployment (Weeks 5-12)
- Pilot Scope: Select 5-15 critical assets for initial deployment
- Sensor Installation: Deploy wireless vibration, temperature, current sensors
- Edge Gateway Setup: Configure local data processing and connectivity
- Data Integration: Connect to CMMS, ERP, historian systems
- Historical Data Collection: Gather 6-12 months of failure history for model training
Phase 3: ML Model Development (Weeks 13-20)
- Baseline Model Training: Learn normal operating signatures for each asset
- Anomaly Detection: Develop unsupervised models for deviation detection
- Failure Prediction: Train supervised models on historical failures
- Model Validation: Backtest against known failures (target 85-95% accuracy)
- Alert Threshold Tuning: Balance early warning vs. false positive rate
Phase 4: Pilot Operation (Weeks 21-32)
- Live Monitoring: Deploy models for real-time equipment monitoring
- Alert Management: Maintenance team reviews AI-generated alerts
- Feedback Loop: Technicians validate predictions, label outcomes for model improvement
- Performance Tracking: Measure lead time to failure, false positive rate, cost savings
- Process Integration: Refine workflow for AI-driven work orders
Phase 5: Expansion & Optimization (Weeks 33+)
- Scale to Additional Assets: Deploy sensors and models across facility (50-200+ assets)
- Advanced Capabilities: Add RUL estimation, prescriptive maintenance scheduling
- Fleet Learning: Aggregate insights across similar asset classes
- Continuous Improvement: Quarterly model retraining with new failure data
AI Predictive Maintenance Cost Analysis
Mid-Market Manufacturing Plant (50-100 Critical Assets)
| Cost Category | Year 1 | Ongoing (Annual) |
|---|---|---|
| IoT Sensors (vibration, temperature, current) | $180,000 | $15,000 |
| Edge Gateways & Networking | $60,000 | $8,000 |
| PdM Software Platform License | $150,000 | $150,000 |
| Implementation & Integration Services | $200,000 | — |
| Training & Change Management | $50,000 | $15,000 |
| Internal Project Resources | $80,000 | $20,000 |
| Total Investment | $720,000 | $208,000 |
Large Enterprise Deployment (200-500 Critical Assets)
| Cost Category | Year 1 | Ongoing (Annual) |
|---|---|---|
| IoT Sensors & Installation | $850,000 | $70,000 |
| Edge Computing Infrastructure | $280,000 | $35,000 |
| Enterprise PdM Platform | $600,000 | $600,000 |
| Implementation Services | $800,000 | — |
| Training & Change Management | $180,000 | $50,000 |
| Internal Project Team | $250,000 | $80,000 |
| Total Investment | $2,960,000 | $835,000 |
ROI Case Studies: Real-World Results
Case Study 1: Automotive Manufacturing - Production Line PdM
Company Profile: $2.1B automotive parts manufacturer, 4 plants, 380 critical assets (robots, CNC machines, presses)
Challenge: Average 127 hours annual unplanned downtime per line (cost: $42K/hour), reactive maintenance approach, 18% of parts budget spent on emergency repairs.
Solution: Deployed Senseye PdM platform with wireless vibration sensors on 85 critical assets across pilot line.
Investment: $485K Year 1 (sensors, platform, implementation), $165K ongoing annually
Results After 18 Months:
- Unplanned Downtime: 127 hours → 18 hours annually (-86%)
- Mean Time Between Failures: 42 days → 127 days (+202%)
- Emergency Repair Costs: $2.8M → $720K annually (-74%)
- Bearing Failures Prevented: 14 major incidents (avg cost: $285K each)
- Advance Warning Time: Average 28 days lead time before failure
- False Positive Rate: 8% (92% of alerts were accurate)
Financial Impact (Year 1):
- Downtime Elimination: 109 hours × $42K/hour = $4.58M
- Emergency Repair Savings: $2.08M
- Prevented Catastrophic Failures: 14 × $285K = $3.99M
- Spare Parts Optimization: $420K inventory reduction
- Total Year 1 Benefit: $11.07M
- Year 1 Investment: $485K + $165K = $650K
- Year 1 Net Benefit: $10.42M
- Year 1 ROI: 1,603%
- Payback Period: 21 days
Case Study 2: Mining Company - Haul Truck Fleet PdM
Company Profile: $1.4B mining operation, 85 haul trucks (240-ton capacity), remote location
Challenge: Truck failures halt production ($850K loss per 8-hour shift), tire failures cost $45K each, reactive maintenance causing 42% equipment availability.
Solution: Deployed Uptake Fusion with vibration, temperature, pressure, tire pressure monitoring sensors on entire fleet.
Investment: $1.65M Year 1, $540K ongoing annually
Results After 12 Months:
- Equipment Availability: 42% → 71% (+69% improvement)
- Unplanned Maintenance Events: 342 → 87 annually (-75%)
- Mean Time To Repair: 18 hours → 6 hours (-67% through better preparation)
- Tire Life Extension: 3,800 hours → 5,200 hours (+37%)
- Maintenance Cost per Truck: $380K → $215K annually (-43%)
- Production Uptime: Additional 1,840 truck-hours annually
Financial Impact (Year 1):
- Increased Production: 1,840 truck-hours × $4,250/hour productivity = $7.82M
- Maintenance Cost Reduction: $165K × 85 trucks = $14.03M
- Tire Cost Savings: 37% life extension × $2.1M annual tire spend = $777K
- Emergency Part Expediting: $890K savings (eliminated air freight)
- Total Year 1 Benefit: $23.52M
- Year 1 Investment: $1.65M
- Year 1 ROI: 1,325%
Case Study 3: Power Utility - Turbine Generator PdM
Company Profile: Regional utility, 12 power generation sites, 45 turbine-generator units
Challenge: Forced outages cost $2.1M per event, aging fleet (average 28 years old), regulatory compliance pressure, traditional time-based maintenance missing 55% of developing failures.
Solution: Deployed GE Digital APM with vibration, thermal, oil analysis sensors on all 45 units.
Investment: $2.85M Year 1, $920K ongoing annually
Results After 24 Months:
- Forced Outages: 8 events → 1 event over 24 months (-87.5%)
- Planned Maintenance Optimization: Reduced unnecessary inspections by 40%
- Asset Life Extension: Deferred $18M in capital replacement investment
- Generator Winding Failure Prevention: Detected insulation degradation 6 months early (would have cost $4.2M to repair in emergency)
- Turbine Blade Crack Detection: Early identification prevented catastrophic failure ($8.5M+ potential cost)
Financial Impact (2-Year Period):
- Prevented Forced Outages: 7 × $2.1M = $14.7M
- Prevented Catastrophic Failures: $4.2M + $8.5M = $12.7M
- Maintenance Optimization: $1.8M annual savings × 2 = $3.6M
- Deferred Capital Investment: $18M
- Total 2-Year Benefit: $49M
- 2-Year Investment: $2.85M + $920K + $920K = $4.69M
- 2-Year Net Benefit: $44.31M
- 2-Year ROI: 945%
AI Predictive Maintenance Best Practices
1. Start with High-Impact Assets
Prioritize based on:
- Downtime Cost: Assets where failure halts production or causes safety/environmental risk
- Failure Frequency: Equipment with history of frequent unexpected failures
- Criticality: Single points of failure without backup/redundancy
- Data Availability: Assets with existing sensors or easy retrofit capability
2. Invest in Quality Sensor Data
- Proper Installation: Vibration sensors must be mounted on bearing housings with specific orientation—poor mounting kills accuracy
- Adequate Sampling: Vibration analysis requires minimum 10-20 kHz for bearing fault detection
- Multi-Modal Sensing: Combine vibration + temperature + current for 30-50% higher accuracy than single-sensor
- Wireless vs. Wired: Wireless sensors easier to deploy but ensure reliable connectivity in industrial environments
3. Manage False Positives Carefully
- Target Rate: Aim for 5-10% false positive rate—lower causes missed failures, higher erodes trust
- Feedback Loop: Technicians must validate alerts and feed outcomes back to improve models
- Severity Tiers: Separate critical alerts (immediate action) from watch list (monitor closely)
- Trust Building: Early pilot success is critical—one missed catastrophic failure can kill program
4. Integrate with Maintenance Workflow
- CMMS Integration: Auto-create work orders from AI alerts with predicted failure mode and recommended action
- Production Scheduling: Coordinate predicted maintenance windows with planned production downtime
- Parts Procurement: Trigger parts orders based on RUL predictions to ensure availability
- Mobile Access: Technicians need smartphone access to alerts, asset history, vibration spectra in field
5. Continuous Model Improvement
- Retraining Cadence: Retrain models quarterly as new failure data accumulates
- Fleet Learning: Aggregate insights across identical assets to improve predictions
- Seasonal Adjustments: Account for temperature, load, and environmental variations
- Performance Tracking: Monitor prediction accuracy, lead time, false positive rate monthly
6. Change Management is Essential
- Maintenance Culture Shift: From "fix it when it breaks" to "prevent it before it breaks"
- Show Early Wins: Publicize successful failure predictions that prevented downtime
- Trust + Verify: Initially review all AI recommendations, gradually increase automation as trust builds
- Skill Development: Train technicians on vibration analysis, thermography, AI system usage
Start Your Predictive Maintenance Journey
Schedule a free assessment with our predictive maintenance experts. We'll analyze your equipment reliability data, identify high-impact pilot assets, calculate ROI projections, and create a custom implementation roadmap. Reduce downtime and maintenance costs starting today.
Conclusion: Predict and Prevent, Don't React and Repair
AI predictive maintenance represents one of the highest-ROI industrial AI applications available today. Organizations across manufacturing, energy, transportation, and mining report 35-50% downtime reductions, 20-40% maintenance cost savings, and ROI of 400-2,000% within the first year.
The economics are compelling: unplanned downtime costs $50 billion annually across industrial sectors, averaging $260K per hour for large manufacturers. A single catastrophic failure—turbine blade fracture, bearing seizure, motor burnout—can cost $500K-$10M in repairs, lost production, and collateral damage. AI predictive maintenance prevents these failures by detecting subtle indicators weeks before human-visible symptoms emerge.
Success requires more than sensors and software. The most effective implementations combine:
- Strategic focus on high-impact critical assets
- Quality multi-modal sensor data (vibration, temperature, current)
- Proven ML algorithms trained on asset-specific failure patterns
- Integration with maintenance workflows (CMMS, scheduling, parts)
- Change management to shift from reactive to predictive culture
- Continuous model improvement through feedback loops
The technology has matured significantly. Cloud-based platforms, wireless sensors, and AutoML have dramatically reduced implementation complexity and cost. Entry points start at $150K-$300K for mid-market deployments on 20-50 assets, with 6-12 month payback periods.
Equipment will continue to age. Operating environments will remain harsh. Production demands will intensify. Traditional reactive and time-based maintenance cannot keep pace. AI predictive maintenance provides the intelligence to maximize asset availability, extend equipment life, and prevent costly unplanned failures.
The question is not whether AI predictive maintenance delivers value—the case studies prove it does. The question is how quickly your organization can capture these benefits before competitors do.
Next Steps: Launch Your PdM Program
- Asset Criticality Analysis: Identify 10-20 highest-impact assets by downtime cost and failure frequency
- Baseline Metrics: Document current MTBF, MTTR, maintenance costs, downtime hours
- ROI Calculation: Model benefits based on industry benchmarks (35-50% downtime reduction)
- Pilot Scope: Select 5-15 assets for initial deployment
- Platform Evaluation: Assess commercial solutions vs. custom development
- Proof of Concept: 6-month pilot to validate ROI before full-scale rollout
Contact Stratagem Systems for a free predictive maintenance assessment. We'll analyze your equipment reliability data, prioritize pilot assets, design your sensor architecture, and develop an implementation roadmap with ROI projections. Transform maintenance from cost center to competitive advantage.