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Overall Equipment Effectiveness (OEE) is built on a deceptively simple insight: every minute of production time is either being used to make good products at the right speed, or it is being lost to one of six specific categories of inefficiency. These are the 6 Big Losses of OEE<\/strong> \u2014 a framework originally developed as part of Total Productive Maintenance (TPM) and now the universal language for production efficiency improvement worldwide. Understanding, measuring and systematically eliminating these losses is the most direct path to OEE improvement in any manufacturing environment.<\/p>\n The 6 Big Losses divide OEE losses across three dimensions: Availability losses (time when the equipment could run but doesn’t), Performance losses (time when the equipment runs but below its rated speed) and Quality losses (time spent producing parts that don’t meet specification). Each dimension contains two specific loss types.<\/p>\n Equipment failures are the most visible and most costly of the 6 Big Losses. A breakdown stops production completely \u2014 no output, labour costs continue, downstream processes are disrupted and recovery time often exceeds the failure duration itself as equipment is restarted, warm-up cycles run and quality checks complete.<\/p>\n How TeepTrak addresses it:<\/strong> JEMBA AI identifies failure precursor patterns in production data \u2014 recurring micro-stoppage sequences, performance degradation trends and energy signature changes \u2014 and generates predictive maintenance alerts before the breakdown occurs. Across 450+ global factory deployments, equipment failures typically account for 20 to 35% of total OEE loss.<\/p>\n Key metric:<\/strong> Mean Time Between Failures (MTBF). Tracking MTBF per machine and per failure mode enables prioritisation of maintenance investment.<\/p>\n Every product changeover, format change, tooling swap or cleaning cycle is a period when the equipment is unavailable for production. Setup and adjustment time is the largest planned downtime loss category in most manufacturing environments \u2014 and the most systematically underestimated.<\/p>\n Manual tracking consistently underestimates setup time because operators measure from “wrench on machine” to “first good part” while ignoring preparation activities, material staging, documentation and the micro-delays within the changeover sequence. Automated measurement typically reveals actual setup time is 20 to 45% longer than the standard.<\/p>\n How TeepTrak addresses it:<\/strong> Automatic capture of actual changeover time from last good part of previous run to first good part of new run. SMED analysis per product family, per machine and per operator identifies where changeover time variability is highest and which improvement actions will deliver the fastest cycle time reduction.<\/p>\n Key metric:<\/strong> Actual vs standard changeover time per product transition. The gap is your SMED improvement opportunity.<\/p>\n Minor stoppages \u2014 equipment pauses of typically 1 to 5 minutes caused by jams, misfeeds, sensor faults, operator absences or brief material shortages \u2014 are the most underestimated category in the 6 Big Losses. A packaging line with 60 minor stoppages per 8-hour shift, each averaging 2 minutes, loses 2 hours of production per shift. Manual tracking captures almost none of this: operators do not log 90-second events, and memory-based shift reports simply omit them.<\/p>\n How TeepTrak addresses it:<\/strong> Second-by-second equipment state capture records every stoppage regardless of duration. JEMBA AI then analyses the accumulated minor stoppage data to identify patterns \u2014 specific equipment stations, specific products, specific shift times or specific sequences that generate disproportionate minor stoppage rates. These patterns are invisible to manual systems and immediately actionable when surfaced.<\/p>\n Key metric:<\/strong> Minor stoppage count and cumulative duration per shift, per line, per equipment station.<\/p>\n Equipment running below its rated speed generates a performance loss even when it is technically “running.” Speed losses occur when operators intentionally slow machines to avoid defects or stoppages, when equipment wear degrades speed capability, when material quality variation forces speed reduction or when product changeovers result in conservative speed settings that are never optimised back to standard.<\/p>\n How TeepTrak addresses it:<\/strong> Continuous performance rate tracking compares actual production speed to ideal cycle time at the part or batch level. Speed loss is automatically separated from availability loss in the OEE calculation. JEMBA AI identifies whether speed losses are systematic (a process design issue affecting all operators) or variable (an operator-specific behaviour or maintenance issue).<\/p>\n Key metric:<\/strong> Performance rate (%) = (actual output \/ theoretical output at rated speed) \u00d7 100. A performance rate below 95% on a nominally running line signals a speed loss requiring investigation.<\/p>\n Every part that fails quality inspection represents a quality OEE loss \u2014 both the material and labour cost of the defective part and the machine time consumed producing it. In stable production runs, process defects typically trace to equipment wear, tooling degradation, material variation or process parameter drift. Systematic tracking of defect rates per shift, per operator, per material lot and per equipment condition enables root cause identification that batch-level quality records cannot provide.<\/p>\n How TeepTrak addresses it:<\/strong> Quality rate tracking at the production event level correlates defect rates with equipment state data \u2014 identifying which equipment conditions (temperature, speed, cycle count since last maintenance) correlate with elevated defect rates. JEMBA AI detects emerging quality issues before they reach the threshold of a formal non-conformance.<\/p>\n Key metric:<\/strong> First-pass yield (%) = (good parts produced \/ total parts produced) \u00d7 100.<\/p>\n The period immediately after a changeover, a maintenance intervention or an equipment startup is typically a period of elevated scrap and rework \u2014 as the process stabilises, parameters settle and operators confirm first-piece acceptance. Startup losses are separate from in-run defects because their root cause is the transition itself, not the steady-state process.<\/p>\n How TeepTrak addresses it:<\/strong> Startup loss tracking measures the time and output from first part after changeover to first confirmed good part within specification. This data quantifies the true cost of each changeover more completely than setup time alone \u2014 a 20-minute changeover with a 15-minute startup loss has a 35-minute total OEE impact. SMED improvement that reduces changeover time without addressing startup stabilisation captures only part of the opportunity.<\/p>\n Key metric:<\/strong> Startup loss duration and yield rate in the first N cycles after a changeover or restart.<\/p>\n A production line with 480 minutes of scheduled production time per shift, after accounting for all 6 Big Losses, might look like this: Equipment failures take 45 minutes (Availability loss 1). Changeover takes 60 minutes planned (Availability loss 2). Minor stoppages accumulate to 35 minutes (Performance loss 3). Speed losses reduce effective output by 8% (Performance loss 4). Process defects reject 3% of output (Quality loss 5). Startup losses after changeover consume 15 minutes (Quality loss 6). Resulting OEE: approximately 58%. The same line, with world-class management of all 6 Big Losses, achieves 85%+ OEE \u2014 a 27-percentage-point improvement that requires no capital investment, only systematic measurement and targeted improvement.<\/p>\n It varies by industry and production type. In high-mix low-volume manufacturing (job shops, aerospace, precision machining), setup and adjustment time (Loss 2) is typically the largest loss. In high-speed continuous production (food and beverage, packaging, consumer goods), minor stoppages (Loss 3) and speed losses (Loss 4) are often dominant. In pharmaceutical and regulated industries, quality losses (Losses 5 and 6) carry disproportionate cost. TeepTrak JEMBA AI automatically identifies which loss category dominates in your specific environment within the first week of deployment.<\/p>\n Non-intrusive IoT sensors (current clamps, vibration sensors) and PLC connections capture equipment state data automatically without any production interruption. TeepTrak deploys in 48 hours on a first production line \u2014 you have baseline data across all 6 Big Loss categories within the first week, without manual data collection or production disruption.<\/p>\n OEE is the output metric \u2014 a single percentage that summarises overall equipment effectiveness. The 6 Big Losses are the analytical framework that explains what drives the OEE number. OEE tells you your equipment is at 63%; the 6 Big Losses tell you that 15 percentage points are lost to minor stoppages, 12 to changeover time, 8 to speed losses and so on. The 6 Big Losses transform OEE from a reporting metric into an improvement roadmap.<\/p>\n Yes. TeepTrak captures all six loss categories in real time from the moment of deployment. Equipment failures and minor stoppages are captured via IoT sensor data. Setup and adjustment time is captured from last good part to first good part of new run. Speed losses are calculated from actual vs ideal cycle time. Process defects and startup losses are tracked via quality count integration and operator input. JEMBA AI then analyses all six categories simultaneously to identify correlations and prioritise improvement actions.<\/p>\n Identify your biggest OEE losses \u2014 free 48h POC<\/a><\/p>\n See how TeepTrak quantifies all 6 Big Losses across manufacturing sectors. Visit our customer success stories<\/a>.<\/p>\nWhat Are the 6 Big Losses?<\/h2>\n
Availability Loss 1: Equipment Failures (Unplanned Downtime)<\/h2>\n
Availability Loss 2: Setup and Adjustment (Planned Downtime)<\/h2>\n
Performance Loss 3: Idling and Minor Stoppages<\/h2>\n
Performance Loss 4: Reduced Speed<\/h2>\n
Quality Loss 5: Process Defects (Scrap and Rework)<\/h2>\n
Quality Loss 6: Startup Losses (Reduced Yield)<\/h2>\n
How the 6 Big Losses Add Up: A Real-World Example<\/h2>\n
FAQ<\/h2>\n
Which of the 6 Big Losses typically has the biggest impact on OEE?<\/h3>\n
How do you measure the 6 Big Losses without stopping production?<\/h3>\n
What is the difference between OEE and the 6 Big Losses?<\/h3>\n
Can TeepTrak track all 6 Big Losses simultaneously?<\/h3>\n