Track Availability, Performance, and Quality in real time across every production line. iFactory's AI-powered OEE analytics pinpoints hidden losses, compares shifts, and delivers predictive insights — so you can push past the industry average of 60% OEE.
7-Day OEE Trend
From raw sensor data to actionable improvement plans — every signal flows through a structured analytics pipeline that turns production noise into clarity.
Auto-capture machine data from PLCs, IoT sensors, SCADA systems, and manual inputs — no production disruption.
Real-time OEE computed from Availability × Performance × Quality with automatic loss categorization by the Six Big Losses.
AI surfaces root causes — Pareto charts, waterfall breakdowns, shift comparisons, and trend anomalies flagged instantly.
Targeted action plans, OEE forecasts, and continuous benchmarking to drive sustainable improvement toward world-class 85%.
Watch Availability, Performance, and Quality update live as production runs. Every machine, every line, every shift — visible on a single screen with drill-down to individual asset level.
Second-by-second calculation from A × P × Q across all connected assets.
Plant → Line → Machine → Component — drill down to the exact bottleneck.
Instant SMS, email, or push alerts when OEE drops below your target.
Andon-style TV dashboards for operators and supervisors on the floor.
Automatically categorize every minute of lost production into the Six Big Losses framework — equipment failures, setup/changeover, micro-stops, reduced speed, startup rejects, and production defects. See exactly where capacity leaks away.
Every downtime event mapped to the correct loss category instantly.
Losses ranked by impact — fix the top 20% for 80% of the improvement.
Visualize exactly how planned time reduces to productive time step by step.
See the dollar impact of each loss type — prioritize by financial value.
Availability Losses
Performance Losses
Compare OEE across shifts, production lines, machines, products, and time periods side-by-side. Instantly spot which team, schedule, or product mix delivers the best results — and why.
Compare morning, afternoon, and night shift OEE with crew attribution.
Benchmark production lines against each other for hidden capacity.
See how product changeovers and SKU mix affect OEE scores.
This week vs. last, this month vs. same month last year — trend clarity.
Stop guessing why OEE dropped. iFactory's AI correlates downtime events, speed losses, quality deviations, and environmental data to surface the true root cause — then recommends the corrective action.
Finds hidden patterns across temperature, vibration, speed, and quality data.
AI suggests specific fixes — adjust settings, replace parts, retrain operators.
Detects unusual patterns before they impact OEE — early warning system.
Learns from past events — improves accuracy with every production run.
Root Cause Identified
Contributing Factors
Every stop, slow-down, and micro-interruption is captured automatically. Operators confirm reason codes on touch-screen terminals in seconds. No more clipboard logging or end-of-shift guesswork.
Sensor-based detection of every stop event, even sub-60-second micro-stops.
Configurable hierarchical reason codes for fast, consistent classification.
Ranked downtime reasons — focus resources where impact is greatest.
Visual Gantt chart of every shift showing run, idle, and stop periods.
Shift Timeline
Recent Events
Compare your OEE against industry benchmarks, internal targets, and world-class standards. Only ~3% of manufacturers consistently achieve 85%+ OEE — iFactory shows you the path to get there.
Compare to automotive (75%), pharma (35%), electronics (80%), and more.
Set custom OEE targets per line, product, and shift — track progress daily.
Move from reactive to predictive. iFactory's AI models forecast OEE scores for the next shift, day, and week — detecting downward trends before they impact production and enabling proactive intervention.
Predict next-shift OEE based on equipment health, schedule, and history.
Spot gradual degradation patterns weeks before a breakdown occurs.
Simulate OEE impact of adding a shift, changing speeds, or scheduling PM.
Seasonal pattern models tuned to your specific production environment.
iFactory's OEE analytics integrates seamlessly with all maintenance, production, and quality modules for a complete operational picture.
OEE history linked to every equipment record
PM schedules optimized by OEE data
Auto-generate WOs from OEE alerts
Quality inspection data feeds OEE quality score
Correlate energy usage with OEE performance
Track parts impact on availability losses
PLC, SCADA & sensor data feeds OEE engine
SAP, Oracle, Dynamics production sync
Plants Connected
Across 45 countries worldwide
Avg OEE Increase
Within first 6 months of deployment
Less Unplanned Downtime
Through predictive loss detection
Avg Annual Savings
From recovered production capacity
"We went from manually recording OEE on spreadsheets to real-time dashboards in 2 weeks. Our OEE jumped from 62% to 79% in 4 months — the shift comparison feature alone identified a 14% performance gap we never knew existed."
"The Six Big Losses breakdown was a game-changer. We discovered that micro-stops accounted for 22% of our performance losses — something we couldn't see before. Addressing just that one category recovered $380K in throughput."
"The predictive OEE forecasting warned us about a bearing failure 3 days before it would have shut down our packaging line. One preventive intervention saved us an estimated $120K in lost production and emergency repair costs."
Everything you need to know about iFactory's OEE analytics capabilities.
OEE (Overall Equipment Effectiveness) measures the percentage of planned production time that is truly productive. It's calculated as Availability × Performance × Quality. Availability tracks how much scheduled time the equipment actually ran. Performance measures whether it ran at its designed speed. Quality counts how many good parts were produced versus total output. A perfect OEE of 100% means manufacturing only good parts, as fast as possible, with zero downtime. iFactory calculates OEE automatically in real time from sensor and machine data — no manual data entry required.
OEE benchmarks vary significantly by industry. For discrete manufacturing, 60% is typical, 85% is considered world-class, and only about 3% of manufacturers consistently achieve 85%+. Industry-specific averages include automotive at approximately 75%, electronics in the low 80s, pharmaceuticals around 35-40% due to stringent compliance requirements, and medical devices at about 78%. Context matters enormously — a 70% OEE in pharma may be outstanding while the same score in continuous processing could indicate issues. iFactory's built-in benchmarking compares your performance to relevant industry peers, not generic targets.
The Six Big Losses framework categorizes all production losses into: (1) Equipment Failures — unplanned breakdowns that stop production, (2) Setup & Changeover — time lost switching between products or batches, (3) Micro-Stops — brief interruptions under a few minutes like jams or sensor trips, (4) Reduced Speed — running below the maximum designed speed, (5) Startup Rejects — defective parts produced during warm-up or changeover, and (6) Production Defects — reject parts during steady-state production. iFactory automatically classifies every loss event into these categories and ranks them by impact so you can focus improvement efforts where they deliver the most value.
iFactory connects to your existing infrastructure through multiple methods — PLC/SCADA direct integration via OPC-UA and Modbus protocols, IoT sensor gateways for retrofit on older equipment, manual operator input through touchscreen terminals, and API integration with existing MES or ERP systems. No production disruption is required for installation. Most plants achieve full data connectivity within 1–2 weeks, and our edge computing devices can pre-process data locally before sending to the cloud for advanced analytics.
Yes — iFactory provides multi-level drill-down from plant-wide OEE all the way down to individual machine or even component-level. You can filter and compare by shift (morning, afternoon, night), production line, individual machine, product/SKU, operator, and time period. This granularity reveals patterns like specific products causing more changeover losses, certain shifts underperforming due to staffing, or individual machines dragging down line-level OEE. All comparisons update in real time with exportable reports in Excel, PDF, or CSV format.
A single production line can go live in as little as 3–5 days with our plug-and-play IoT gateways. A full facility deployment with multiple lines, SCADA integration, and custom dashboards typically completes in 2–4 weeks. Enterprise rollouts across multiple sites take 6–10 weeks. The cloud-native platform requires zero on-premise servers. We recommend tracking OEE for at least 30 days to establish an accurate baseline — many plants discover their actual OEE is 10–15% lower than assumed once properly measured.
Join 500+ manufacturing facilities using iFactory to track, analyze, and improve Overall Equipment Effectiveness. Schedule a free 30-minute demo and see your production losses visualized for the first time.