How to read MOT TQI

The basics

TQI shows how your station's initial MOT test results compare to national patterns. The data is broken down by:

• Site level — your whole station vs the national average
• Tester level — each tester at your site vs site and national averages
• Time period — last one calendar month or last three calendar months

Only initial tests are counted. Retests and partial retests are excluded.

Key metrics explained

Number of tests

The total initial MOT tests conducted in the selected period. This is critical context — percentage-based metrics are unreliable at very low volumes. A tester who has done 5 tests in a month can show wild percentage swings that mean nothing.

Rule of thumb: be cautious interpreting percentages from fewer than ~30–50 tests.

Average vehicle age

The mean age of vehicles tested. This matters because older vehicles are more likely to fail. A site testing predominantly older vehicles will naturally have a higher failure rate than one testing newer fleet vehicles.

If your tester's average vehicle age is significantly higher or lower than the site average, that context should be considered before interpreting their failure rate.

Failure rate

The percentage of initial tests resulting in a fail. Shown for:

• the individual tester
• the site overall
• the national average

How to use it:

Look at the difference between your tester/site and the national average. Then ask:

• Is the difference large enough to be meaningful given the test volume?
• Could vehicle age/type explain the difference?
• Is the pattern consistent over multiple months?

A difference does NOT automatically indicate a problem. It indicates something that may be worth investigating.

Average test duration

How long tests take on average. Very short tests are sometimes flagged because they may suggest insufficient inspection time — but context matters. A straightforward Class 4 test on a newer vehicle will legitimately take less time than a complex test on an older vehicle.

Very long durations may indicate a tester being thorough, unfamiliar with equipment, or spending time on unrelated work during the test window.

Component failure rates

Failure percentages broken down by test component areas (brakes, lighting, suspension, steering, tyres, etc.). Useful for:

• Identifying whether a tester has an unusually high or low failure rate in a specific area
• Comparing against national patterns to spot potential over- or under-reporting in specific categories

One-month vs three-month data

| Period | Best for | |--------|----------| | One month | Spotting recent changes, investigating specific concerns, checking recent tester behaviour | | Three months | More reliable patterns (larger sample), seasonal smoothing, strategic review |

Practical approach: use one-month data to identify potential issues, then check three-month data to see whether it is a pattern or an anomaly.

Reading the comparison structure

Conceptually, TQI gives you a hierarchy:

National average
└── Your site average
    ├── Tester A
    ├── Tester B
    └── Tester C

At each level, you can compare:

• Failure rate
• Duration
• Vehicle age
• Component breakdown

Start broad, then narrow: begin at site vs national, then drill into individual testers only if the site-level comparison raises questions.

What "significant" differences look like

There is no official threshold. However:

• A 5% difference in failure rate on 200 tests is more notable than a 20% difference on 8 tests.
• Consistent differences over three months are more informative than a single-month spike.
• A tester whose vehicle age is 2+ years different from the site average may legitimately have a different failure rate.

Common mistakes when reading TQI

1. Ignoring volume. Comparing percentages without checking how many tests were done.
2. Ignoring vehicle age. A tester working Saturday shifts on older vehicles will look different.
3. Comparing across different vehicle groups. Class 4 vs Class 7 averages are not comparable.
4. Assuming national average = "correct" rate. The national average is just the mean. It includes both good and poor practice.
5. Reacting to a single month. One-month data can fluctuate. Look for sustained patterns.
6. Treating TQI as a pass/fail system. TQI is comparison evidence, not a compliance score.

Practical checklist for reading TQI

• [ ] Check the reporting period and vehicle group
• [ ] Note total test volumes (site and per tester)
• [ ] Review site failure rate vs national
• [ ] Compare tester failure rates to each other and the site average
• [ ] Check average vehicle age for context
• [ ] Review average test durations
• [ ] Look at component breakdown for unusual patterns
• [ ] Consider whether differences are large and sustained enough to warrant action
• [ ] Record your findings

How MOT Lens helps

MOT Lens imports your Manager Report and presents TQI data as structured comparison evidence:

• Side-by-side site vs national and tester vs site views
• One-month and three-month data together
• Component breakdowns
• Historical snapshots across multiple imports
• A review trail to record what you found

It does not assign scores or make compliance judgements.

FAQ

Can a high failure rate mean a tester is doing something wrong?

Not automatically. A high failure rate might indicate a tester is applying the inspection manual correctly while others are being too lenient. Context (vehicle age, volume, component patterns) is essential.

What is a "normal" MOT failure rate?

The national average varies by vehicle class and time of year, but for Class 4 vehicles it is typically in the low-to-mid 30% range for initial tests. Your site may legitimately differ based on location and vehicle mix.

How far back does TQI data go?

TQI in the MOT Testing Service shows the last one or three full calendar months. For longer-term tracking, you need to retain your own historical records (or use a tool like MOT Lens).

Should I check TQI monthly?

There is no mandated frequency, but regular review is part of good quality management. Monthly review gives you the earliest indication of emerging patterns.