Fishbone Cause Weighting Calculator

Work out fishbone cause weighting instantly with clear inputs, formula shown and shareable results.

About Fishbone Cause Weighting Calculator

A fishbone diagram organises candidate causes into categories — machine, method, material and manpower being the classic four. Weighting each category by team assessment or by evidence turns a qualitative diagram into a ranked list, showing where to direct data collection and verification effort next.

Formula

Cause weighting

Category share % = category weight x 100 / sum of all weights

Worked example

With the default values below, Fishbone Cause Weighting Calculator returns Dominant Cause Share of 34.78%. Change any input above and every figure here is recalculated in your browser.

Inputs used

Machine Cause Weightaccepts 0–10: 8
Method Cause Weightaccepts 0–10: 6
Material Cause Weightaccepts 0–10: 4
Manpower Cause Weightaccepts 0–10: 5

Results

Dominant Cause Share: 34.78%
Dominant Cause Category: Machine
Second Cause Category: Method
Total Assigned Weight: 23.0

Step by step

1Values used
2Cause weighting
3Dominant Cause Share= 34.78
4Dominant Cause Category= Machine
5Second Cause Category= Method
6Total Assigned Weight= 23.0

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Is a weighted fishbone a substitute for data?

No. Weighting captures team judgement and prioritises investigation. The dominant category must then be verified with measurements before countermeasures are designed.

Should more categories be used?

The 6M version adds measurement and environment (mother nature), which is worth doing when gauge error or ambient conditions are plausible contributors.