OptimaFlow

TPM

Maximizing equipment effectiveness through proactive and preventive maintenance

Scaling - Active

AI Query Interface

Ask questions in natural language

Try these sample queries:

Core Principles

Autonomous maintenance by operators

Planned maintenance optimization

Predictive maintenance using IoT

Equipment reliability and MTBF improvement

Equipment Uptime

92.3%

+2.8%

Target: 95%

97%

Predicted: 93.5%90% confident

MTBF (Mean Time Between Failures)

180hours

+8.5%

Target: 220hours

82%

Predicted: 195hours87% confident

Maintenance Cost Reduction

18%

+12.3%

Target: 25%

72%

Predicted: 21%85% confident

Real-Time Events & Alerts
2

INCIDENT

critical

...

Equipment Vibration Anomaly Detected

CNC Mill #3 vibration levels exceeded normal range by 35%. Temperature rising. Predictive model forecasts bearing failure in 18-24 hours.

Impact: Unplanned downtime risk. Estimated $45K loss if failure occurs during production shift. Potential cascading delays in 8 orders.

Recommended Action: Schedule immediate maintenance window. Order replacement bearing (SKU: BRG-7845). Plan production reroute to Mill #2.

Explainability: Vibration signature matches historical failure pattern (94% similarity). Thermal imaging confirms hotspot. RL recommends preemptive action over run-to-failure based on cost-benefit: $3.5K maintenance vs $45K downtime.

RL Score: 95%

AI Confidence: 94%

OPPORTUNITY

medium

...

Energy Efficiency Opportunity

Equipment utilization analysis shows 3 machines running idle during low-demand periods. Smart scheduling could reduce energy waste.

Impact: Potential 12% energy reduction on these machines, worth $22K annually. Also extends equipment life.

Recommended Action: Implement demand-responsive scheduling algorithm. Auto-standby mode during idle periods >15 min. Monitor impact on responsiveness.

Explainability: Energy audit shows 8 hours weekly of unnecessary runtime. Smart scheduling (based on ML demand forecast) balances energy savings vs quick-start requirements. Payback: 4 months.

RL Score: 79%

AI Confidence: 86%

AI-Powered Improvement Opportunities

Total Potential: $634K/year

AI-Powered Predictive Maintenance

Impact: high

Effort: high

$285K/yr

5-7 months

92% AI confidence

Deploy machine learning models analyzing vibration, temperature, and acoustic data to predict equipment failures 2-4 weeks in advance with 92% accuracy.

RL Recommendation: Start with top 3 critical assets (Line 3 press, CNC mill, packaging robot). Install IoT sensor suite. Build 6-month baseline model. Validate predictions manually for first month before auto-scheduling maintenance.

Explainability: Predictive maintenance reduces unplanned downtime from 8% to 2%, saving $180K in lost production annually. Additional $105K saved through optimized spare parts inventory and labor scheduling.

Digital Twin for Equipment Health

Impact: high

Effort: high

$198K/yr

6-9 months

85% AI confidence

Create virtual replicas of critical equipment to simulate degradation patterns, test maintenance strategies, and optimize performance parameters in real-time.

RL Recommendation: Pilot with one production line. Model physics-based degradation + data-driven ML hybrid. Use for maintenance planning and operator training. Scale to 5 lines by Q4.

Explainability: Digital twin enables "what-if" analysis before physical changes, reducing trial-and-error downtime by 65%. Optimized maintenance schedules increase uptime 3.2%, worth $145K annually.

Autonomous Maintenance Gamification

Impact: medium

Effort: medium

$87K/yr

3-4 months

88% AI confidence

Mobile app for operators to log daily equipment checks with AR-guided inspections, earn badges, and compete on leaderboards. AI validates inspection quality via image recognition.

RL Recommendation: Deploy to Shift A first (most tech-savvy). Use AR overlays to show normal vs abnormal conditions. Reward top performers with quarterly prizes. Track correlation between engagement and equipment issues caught.

Explainability: Gamification increases operator inspection compliance from 72% to 94%, catching minor issues early. Prevents escalation to major failures, saving $87K annually in emergency repairs.

Condition-Based Lubrication System

Impact: medium

Effort: low

$64K/yr

2-3 months

91% AI confidence

Automated lubrication dispensing based on real-time equipment condition (vibration, temperature) rather than fixed schedules. Reduces lubricant waste by 35% and bearing failures by 28%.

RL Recommendation: Retrofit automated lube systems on high-speed bearings first. Monitor oil condition sensors. Adjust dispensing algorithms weekly for first month. Track bearing life extension and lubricant consumption reduction.

Explainability: Smart lubrication prevents over/under-lubrication, the #2 cause of bearing failures. Extended bearing life saves $42K annually. Reduced lubricant costs save $22K.