{"id":92002,"date":"2026-05-02T07:11:43","date_gmt":"2026-05-02T07:11:43","guid":{"rendered":"https:\/\/teeptrak.com\/six-big-losses-pareto-analysis-2026\/"},"modified":"2026-06-17T23:27:16","modified_gmt":"2026-06-17T23:27:16","slug":"six-big-losses-pareto-analysis-in-2026","status":"publish","type":"post","link":"https:\/\/teeptrak.com\/en\/six-big-losses-pareto-analysis-in-2026\/","title":{"rendered":"The 6 Big Losses \u2014 Pareto Analysis Framework (2026)"},"content":{"rendered":"

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The Six Big Losses, Pareto Analysis Framework for Manufacturing<\/h1>\n
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TL;DR<\/strong><\/p>\n

The Six Big Losses framework, developed by Nakajima as part of TPM, categorizes every manufacturing productivity loss into six categories mapped to OEE pillars. Combined with Pareto analysis, it identifies the 20% of causes producing 80% of losses. The framework is the foundation of structured OEE improvement, and plants applying it systematically gain +6 to +12 OEE points within 12 months.<\/p>\n<\/div>\n

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The Six Big Losses are: equipment breakdowns, setup and adjustments, idling and minor stops, reduced speed, process defects, and reduced yield (startup losses).<\/strong><\/p>\n<\/div>\n

The Six Big Losses framework categorizes every cause of manufacturing productivity loss into six categories, each mapped to one of the three OEE pillars (Availability, Performance, Quality).<\/strong><\/p>\n

Combined with Pareto analysis, the framework reveals which losses to attack first for maximum impact. This guide covers each loss with concrete data, typical magnitude, and the Pareto methodology to prioritize countermeasures.<\/p>\n

The 6 losses, 1-line summary each<\/h2>\n

Availability losses (machine not running):<\/strong><\/p>\n

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  1. Equipment breakdowns<\/strong>, unplanned mechanical\/electrical failures<\/li>\n
  2. Setup & adjustments<\/strong>, changeovers between products<\/li>\n<\/ol>\n

    Performance losses (machine running below ideal):<\/strong><\/p>\n

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    1. Idling & minor stops<\/strong>, brief stoppages under 5 minutes<\/li>\n
    2. Reduced speed<\/strong>, sustained underspeed below ideal cycle<\/li>\n<\/ol>\n

      Quality losses (bad parts):<\/strong><\/p>\n

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      1. Process defects<\/strong>, bad parts during steady-state operation<\/li>\n
      2. Reduced yield<\/strong>, bad parts during startup\/changeover<\/li>\n<\/ol>\n

        Loss 1 \u00b7 Equipment breakdowns<\/h2>\n

        Definition:<\/strong> Unplanned equipment failures that stop production. Mechanical failures, electrical failures, control system errors, sensor failures.<\/p>\n

        Typical magnitude:<\/strong> 5-15% Availability loss in mid-market plants. Largest hidden source of OEE loss in plants without real-time tracking.<\/p>\n

        Reduction approach:<\/strong> real-time downtime tracking + weekly Pareto + predictive maintenance triggers + cross-trained first-response.<\/p>\n

        Loss 2 \u00b7 Setup & adjustments<\/h2>\n

        Definition:<\/strong> Time lost during product changeovers, from “last good part of A” to “first good part of B”.<\/p>\n

        Typical magnitude:<\/strong> 5-25% Availability loss for high-SKU mix plants; 1-5% for stable production.<\/p>\n

        Reduction approach:<\/strong> SMED (Single-Minute Exchange of Die), separate internal vs external setup activities, convert internal to external. Typical gain: 50-80% reduction in 6 months.<\/p>\n

        Loss 3 \u00b7 Idling & minor stops<\/h2>\n

        Definition:<\/strong> Brief stoppages under 5 minutes, jams, sensor faults, brief operator interventions.<\/p>\n

        Typical magnitude:<\/strong> 5-10% Performance loss. Often the LARGEST hidden source of OEE loss<\/strong> because operators don’t report stops under 5 minutes.<\/p>\n

        Reduction approach:<\/strong> automatic micro-stop detection (impossible without real-time monitoring) + Pareto on top 3 micro-stop categories.<\/p>\n

        Loss 4 \u00b7 Reduced speed<\/h2>\n

        Definition:<\/strong> Equipment running below ideal cycle time (intentional or unintentional).<\/p>\n

        Typical magnitude:<\/strong> 5-15% Performance loss. Almost always invisible without comparing actual vs ideal cycle time at sub-second granularity.<\/p>\n

        Reduction approach:<\/strong> document demonstrated best cycle time per product + real-time speed deviation alerts.<\/p>\n

        Loss 5 \u00b7 Process defects<\/h2>\n

        Definition:<\/strong> Bad parts produced during steady-state operation (not startup). Includes scrap and rework.<\/p>\n

        Typical magnitude:<\/strong> 1-5% Quality loss for mature plants; 5-15% for plants with poor process control.<\/p>\n

        Reduction approach:<\/strong> real-time defect tagging + mandatory RCA on top 3 categories.<\/p>\n

        Loss 6 \u00b7 Reduced yield (startup losses)<\/h2>\n

        Definition:<\/strong> Bad parts produced during equipment startup or after changeovers.<\/p>\n

        Typical magnitude:<\/strong> 1-3% Quality loss for stable plants; 5-10% for high-changeover plants (pharma, semiconductors).<\/p>\n

        Reduction approach:<\/strong> standardize startup procedures, capture\/replay successful parameters.<\/p>\n

        How to apply Pareto analysis to the Six Losses<\/h2>\n

        The Pareto principle holds in manufacturing: 20% of stop causes account for 80% of downtime minutes.<\/strong><\/p>\n

        Standard Pareto methodology:<\/p>\n

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        1. Capture every stop<\/strong> with timestamp + standardized cause code (real-time, not end-of-shift)<\/li>\n
        2. Aggregate weekly<\/strong> by cause category, minutes not events<\/li>\n
        3. Rank top 5 causes<\/strong> by total minutes lost<\/li>\n
        4. Attack top cause first<\/strong>, the largest single category typically accounts for 30-50% of total loss<\/li>\n
        5. Validate at 12 weeks<\/strong>, if top cause reduced 30%, move to second; if not, RCA was incomplete<\/li>\n<\/ol>\n

          Plants applying Pareto-driven Six Big Losses reduction typically gain +6 to +12 OEE points within 12 months.<\/p>\n

          Watch: How TeepTrak Customers Transform OEE<\/h2>\n