Six Big Losses in Manufacturing: The OEE Framework That Drives Real Improvement

Key Takeaways: The Six Big Losses framework turns OEE from a single number into six specific improvement targets. Each category requires different countermeasures, and different data to address.

See the calculation behind these losses.

Fabrico tracks these losses from PLC signals, the computer vision add-on and the causes operators tag, and its financial impact module puts a money value on them. A stoppage reason can be configured to open a maintenance task automatically in the CMMS, and people can also create tasks by hand. This transforms OEE monitoring into a continuous improvement engine.

Seiichi Nakajima defined the Six Big Losses as the six categories that prevent manufacturing equipment from reaching maximum effectiveness. Every production loss falls into one of them, making the framework exhaustive and the analysis complete.

The six losses map to OEE's three components:

Availability losses: Equipment failures (unplanned breakdowns) and Setup and adjustments (planned changeovers and material changes).

Performance losses: Idling and minor stoppages (events under 5 minutes) and Reduced speed (machine running below ideal cycle time).

Quality losses: Startup and yield rejects (defects during startup or after changeover) and Production defects and rework (rejects during steady-state production).

The value of the framework: it requires improvement teams to address each loss category with the countermeasure that fits that specific cause, not a generic "fix things" approach. Equipment failures need better preventive maintenance. Setup losses need SMED.

Minor stoppages need autonomous maintenance. Reduced speed needs condition monitoring and process optimization.

Quality losses need equipment condition management.

Capturing All Six Losses: What PLC Data Misses

Standard OEE monitoring from PLC signals captures equipment failures (large stoppages that trigger machine state changes), some setup and adjustment time (if changeover start/end signals are configured), and production counts for quality calculations.

What PLC monitoring alone can miss: the causes of very short stoppages, and losses from operator handling and manual process activities.

Fabrico's computer vision add-on, Inefficiencies Zoom-In, helps fill those gaps:

• Minor stoppages: Camera-based monitoring picks up short production interruptions, with video clips to review what happened
• Speed losses: Visual cycle time monitoring from camera observation detects when production rate differs from ideal, even when the machine is technically "running"
• Operator inefficiencies: Handling delays, manual inspection time, and rework activities at hybrid stations appear as performance rate losses that PLC sensors classify as normal operation

Computer vision can show OEE losses that PLC-only monitoring misses. These losses have names, root causes, and elimination paths, but only when they're visible.

The complete Six Big Losses picture requires both machine signals (for large stoppages, production counts, and quality rates) and computer vision (for the performance losses that happen in the gaps between sensor detections). Fabrico offers both in one platform, with cameras as an add-on.

Knowing all six loss categories doesn't tell you which to fix first. The prioritization that maximizes ROI from a Six Big Losses improvement program:

Step 1: Quantify each category in financial terms. Monthly production time lost to each category × production value per hour = financial impact per category. This converts percentages into dollars that finance can evaluate.

Worked example, with assumed figures: a 10-line plant at $4,000/hour:

• Equipment failures: 3% availability loss = 21.6 hours/month = $86,400/month
• Setup and adjustments: 8% availability loss = 57.6 hours/month = $230,400/month
• Minor stoppages: 6% performance loss = 43.2 hours/month = $172,800/month
• Reduced speed: 5% performance loss = 36 hours/month = $144,000/month
• Quality losses: 3% combined = 21.6 hours/month = $86,400/month

In this example: Setup and adjustments represent the largest financial loss, followed by minor stoppages and reduced speed. The improvement team should lead with SMED (changeover reduction), not with more aggressive PM scheduling for breakdowns.

Step 2: Assess improvement tractability. Some losses respond quickly to available interventions (PM interval adjustment, micro-stop root cause elimination, changeover technique standardization). Others require sustained programs (SMED training, autonomous maintenance culture development, engineering design changes for persistent failure modes). Prioritize fast wins that free resources for longer-horizon programs.

Step 3: Account for compounding effects. Reducing equipment failures typically improves PM compliance, because maintenance capacity freed from reactive work can be invested in preventive work. This compounding means Loss 1 improvement often delivers more total value than its direct financial impact alone.

Start the reliability flywheel and let it compound.

Fabrico supports this prioritization. Its downtime analytics rank stops by reason and machine, the financial impact module puts a money value on losses, and the AI assistant add-on answers questions about the data.

Your team still decides what to fix first.

Fabrico's OEE dashboard shows availability, performance and quality for each production line and for the plant, and dashboards are set up per client. A production or maintenance lead who reviews it weekly keeps the improvement priorities in view.

You can see downtime by reason and machine, the money value of losses with the financial impact module, and open CMMS tasks.

The improvement loop that Fabrico enables:

1. OEE and downtime data show the costliest loss category
2. Downtime analytics show the machines and stop reasons behind that category
3. Computer vision add-on clips show what happened in short stoppages
4. CMMS tasks are created for targeted corrective actions, by hand or automatically from a configured stoppage reason
5. Post-action OEE monitoring checks whether each improvement reduced the target loss category
6. The updated loss data shows the next target

This continuous loop, from loss identification through maintenance action through OEE recovery verification, is the operational mechanism that compounds OEE improvement over time. Each cycle targets the highest-value loss category, then moves to the next.

How much each cycle recovers depends on the plant and the losses it starts with.

Monitoring shows you the losses. Fixing them takes people and follow-up. Fabrico keeps OEE data and CMMS tasks in one platform to support that work.