Predict Failures
Before They Happen.
Maximise Every Machine.
DevsBot combines real-time sensor data with AI to calculate live OEE, detect anomalies before breakdowns occur, and identify the exact losses costing you production time — down to the machine and shift.
The Three Pillars of Manufacturing Efficiency
Overall Equipment Effectiveness is the gold standard for measuring manufacturing productivity. DevsBot calculates all three components in real time — down to machine and shift level.
Availability
Actual run time vs planned production time. Measures all downtime losses — planned stops, breakdowns, changeovers.
Run Time ÷ Planned Time
Performance
Actual speed vs ideal speed. Captures all speed losses — minor stops, reduced speed, idling and empty running.
(Ideal CT × Parts) ÷ Run Time
Quality
Good parts vs total parts produced. Accounts for all quality losses — scrap, rework, startup rejects and in-process defects.
Good Parts ÷ Total Parts
OEE Score
World-class OEE is 85%+. Typical manufacturing sits at 60%. DevsBot's AI helps you close the gap systematically.
0.88 × 0.91 × 0.91 = 73%
The Six Big Losses — What DevsBot Tracks
Equipment Failure
Unplanned stops due to breakdowns, mechanical failure or electrical faults. The biggest loss category in most plants.
Setup & Adjustment
Time lost during product changeovers, die changes, tooling adjustments and warm-up periods after setup.
Minor Stops
Short stops under 5 minutes — jams, misfeeds, sensor checks. Individually small but cumulatively significant.
Reduced Speed
Machine running below rated speed due to mechanical wear, poor settings or operator caution.
Process Defects
Scrap and rework produced during stable production. Includes in-process defects and rejects.
Startup Rejects
Quality losses during startup, warmup or after changeover before stable production conditions are reached.
How DevsBot AI Predicts Failures
A 4-stage AI pipeline continuously monitors every machine, learns normal behaviour patterns and raises early warnings before failure — not after.
Continuous Signal Monitoring
Vibration, temperature, current signature, pressure and acoustic emissions are sampled at high frequency from every critical asset — motors, compressors, pumps, spindles and gearboxes.
Machine Learning Baseline
AI models learn the normal operating signature of each asset across loads, speeds and temperatures. Seasonal and shift-based variations are factored in to eliminate false alarms.
Degradation Curve Analysis
DevsBot tracks the rate of signal deviation from baseline and fits a degradation curve. Extrapolating forward gives an estimated time-to-failure with a confidence interval — typically 48–96 hours ahead.
Actionable Alert & Work Order
When the model crosses a confidence threshold, a prioritised alert is sent to maintenance — with the specific failure mode, estimated time window and recommended corrective action.
Everything You Need to Master OEE
Live OEE Dashboard
Real-time OEE, Availability, Performance and Quality calculated per machine, line, plant and shift — with trend sparklines and shift comparisons.
AI Anomaly Detection
LSTM and Isolation Forest models detect abnormal machine behaviour from vibration, current and temperature signals — typically 48–96 hours before failure.
Pareto Loss Analysis
Automated Pareto charts rank your biggest downtime and quality loss contributors — so maintenance teams focus effort where it creates the most value.
Downtime Root Cause Logging
Operators log downtime reasons against each stop. AI correlates stop patterns with sensor data to identify systemic root causes automatically.
Predictive Maintenance Scheduling
Maintenance windows are auto-suggested based on Remaining Useful Life estimates — scheduling work during planned stops to avoid unplanned breakdowns.
Vibration & Condition Monitoring
Continuous high-frequency monitoring of motors, bearings, compressors and gearboxes. FFT spectrum analysis identifies early-stage bearing and gear defects.
Multi-Asset, Multi-Line View
Compare OEE performance across machines, lines and plants. Identify best and worst performers and replicate best practices across the facility.
Automated Shift Reports
Shift-end OEE summaries delivered automatically to supervisors and managers — with top losses, maintenance alerts and performance vs target.
CMMS & ERP Integration
Predictive alerts automatically create work orders in your CMMS. Production data flows to ERP for accurate scheduling and cost tracking.
Monitors Every Critical Asset
Know Your OEE.
Fix What Costs You Most.
Book a free OEE assessment. Our engineers will map your current losses, identify your biggest opportunities and show you a deployment plan for DevsBot — tailored to your plant and equipment.