{"id":91986,"date":"2026-05-02T07:09:44","date_gmt":"2026-05-02T07:09:44","guid":{"rendered":"https:\/\/teeptrak.com\/oee-formula-worked-examples-2026\/"},"modified":"2026-05-02T07:09:45","modified_gmt":"2026-05-02T07:09:45","slug":"oee-formula-worked-examples-2026","status":"publish","type":"post","link":"https:\/\/teeptrak.com\/en\/oee-formula-worked-examples-2026\/","title":{"rendered":"OEE Formula \u2014 5 Worked Examples from Real Plants (2026)"},"content":{"rendered":"

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OEE Formula \u2014 5 Worked Examples from Real Manufacturing Plants<\/h1>\n
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TL;DR<\/strong><\/p>\n

OEE is calculated as Availability \u00d7 Performance \u00d7 Quality. This guide walks through 5 fully-worked examples from real plant data: a packaging line at 79.5% OEE, an automotive Tier-2 at 71.3%, a pharma packaging line at 56%, a food and beverage filler at 67%, and a plastics extruder at 73%. Each example shows the calculation step-by-step and identifies the common pitfalls that cause inflated reported OEE.<\/p>\n<\/div>\n

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The OEE formula is OEE = Availability \u00d7 Performance \u00d7 Quality, where each factor is a percentage between 0% and 100%, calculated independently from production data.<\/strong><\/p>\n<\/div>\n

OEE (Overall Equipment Effectiveness) is calculated as Availability \u00d7 Performance \u00d7 Quality.<\/strong> The formula is simple, but most plants get it wrong on small details that change the final result by 10+ percentage points.<\/p>\n

This guide presents 5 fully worked examples from real plant data, anonymized but otherwise unchanged. Each example shows the math step-by-step and flags the pitfalls that cause inflated reported OEE.<\/p>\n

The OEE formula (1-sentence answer)<\/h2>\n

OEE = Availability \u00d7 Performance \u00d7 Quality.<\/strong><\/p>\n

Where:<\/p>\n

    \n
  • Availability<\/strong> = Run Time \u00f7 Planned Production Time<\/li>\n
  • Performance<\/strong> = (Total Count \u00d7 Ideal Cycle Time) \u00f7 Run Time<\/li>\n
  • Quality<\/strong> = Good Count \u00f7 Total Count<\/li>\n<\/ul>\n

    Example 1 \u2014 Packaging line (79.5% OEE)<\/h2>\n

    5-line beverage packaging plant. Bottle filler, 8-hour shift.<\/strong><\/p>\n

      \n
    • Shift: 480 min \/ Breaks: 30 min \/ Planned Production Time = 450 min<\/strong><\/li>\n
    • Stops: breakdown 22 min + material 18 min + minor stops 7 min = 47 min<\/strong><\/li>\n
    • Run Time<\/strong> = 450 – 47 = 403 min<\/strong><\/li>\n
    • Availability<\/strong> = 403\/450 = 89.6%<\/strong><\/li>\n
    • Total parts: 18,135 \/ Ideal cycle: 1.2 sec \/ Theoretical max: 20,150<\/li>\n
    • Performance<\/strong> = 18,135\/20,150 = 90.0%<\/strong><\/li>\n
    • Good parts: 17,881 \/ Rejects: 254<\/li>\n
    • Quality<\/strong> = 17,881\/18,135 = 98.6%<\/strong><\/li>\n<\/ul>\n

      OEE = 0.896 \u00d7 0.900 \u00d7 0.986 = 79.5%<\/strong><\/p>\n

      Example 2 \u2014 Automotive Tier-2 stamping (71.3% OEE)<\/h2>\n

      Hydraulic press, automotive structural parts. 8-hour shift.<\/strong><\/p>\n

        \n
      • Planned Production Time = 480 min (continuous shift, breaks counted as availability loss per Nakajima method)<\/li>\n
      • Stops: 96 min total (breakdowns 38 + die changeovers 42 + safety 16)<\/li>\n
      • Run Time = 384 min, Availability = 80.0%<\/strong><\/li>\n
      • Ideal cycle: 30 sec \/ Total parts: 700 \/ Theoretical: 768<\/li>\n
      • Performance = 700\/768 = 91.1%<\/strong><\/li>\n
      • Good parts: 685 \/ Rejects: 15<\/li>\n
      • Quality = 685\/700 = 97.9%<\/strong><\/li>\n<\/ul>\n

        OEE = 0.800 \u00d7 0.911 \u00d7 0.979 = 71.3%<\/strong><\/p>\n

        Example 3 \u2014 Pharma blister packaging (56% OEE)<\/h2>\n

        Blister packaging line, regulated GMP environment.<\/strong><\/p>\n

          \n
        • Planned Production Time = 420 min (60-min cleaning excluded)<\/li>\n
        • Stops: 92 min (changeover 35 + sensor 22 + microstops 35)<\/li>\n
        • Availability = 328\/420 = 78.1%<\/strong><\/li>\n
        • Performance = 73.6% (significant micro-stop losses)<\/li>\n
        • Quality = 97.4%<\/li>\n<\/ul>\n

          OEE = 0.781 \u00d7 0.736 \u00d7 0.974 = 56.0%<\/strong><\/p>\n

          Pitfall flagged:<\/strong> the plant’s reported OEE was 72%. Direct-sensor measurement revealed 16 percentage points of “invisible” Performance losses, mostly micro-stops under 5 minutes that operators did not log.<\/p>\n

          Example 4 \u2014 F&B filler line (67% OEE)<\/h2>\n

          Liquid filler, multi-SKU, 12-hour shift with high changeover frequency.<\/strong><\/p>\n

            \n
          • Planned Production Time = 660 min<\/li>\n
          • Stops: 178 min (5 changeovers averaging 22 min + breakdowns 38 + jams 30)<\/li>\n
          • Availability = 73.0%<\/strong><\/li>\n
          • Performance = 94.2% (high speed when running)<\/li>\n
          • Quality = 97.3%<\/li>\n<\/ul>\n

            OEE = 0.730 \u00d7 0.942 \u00d7 0.973 = 66.9%<\/strong><\/p>\n

            Pitfall flagged:<\/strong> changeovers were initially treated as “planned” and excluded from Availability \u2014 the inflated OEE was 81%. The Nakajima method includes them.<\/p>\n

            Example 5 \u2014 Plastics extruder (73% OEE)<\/h2>\n

            Continuous-flow extrusion line, single product, 24-hour operation.<\/strong><\/p>\n

              \n
            • Planned Production Time = 1,440 min<\/li>\n
            • Stops: 130 min (1 unplanned stop 45 + planned PM 60 + start-up 25)<\/li>\n
            • Availability = 91.0%<\/strong><\/li>\n
            • Performance = 84.6% (sustained underspeed)<\/li>\n
            • Quality = 95.1%<\/li>\n<\/ul>\n

              OEE = 0.910 \u00d7 0.846 \u00d7 0.951 = 73.2%<\/strong><\/p>\n

              Pitfall flagged:<\/strong> ideal cycle time set to nameplate value, not demonstrated best. Recalibrating revealed Performance was actually 76%, lowering OEE to 65.9% \u2014 a 7-point correction.<\/p>\n

              Common pitfalls across the 5 examples<\/h2>\n

              Three errors caused 8-16 points of OEE inflation across these plants:<\/strong><\/p>\n

                \n
              1. Wrong ideal cycle time (Plant 5)<\/strong> \u2014 using nameplate instead of demonstrated best<\/li>\n
              2. Excluding changeovers (Plant 4)<\/strong> \u2014 treating them as planned not Availability loss<\/li>\n
              3. Missing micro-stops (Plant 3)<\/strong> \u2014 manual logs don’t capture stops under 5 min<\/li>\n<\/ol>\n

                If your reported OEE seems much higher than peer plants in your sector, audit these three potential sources first.<\/p>\n

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