How are raw values normalized?

By default with the ratio method: value ÷ maximum for benefit criteria and minimum ÷ value for cost criteria, so the best option gets 1 and the rest scale proportionally. Min–max (worst 0, best 1) is also available and stretches the differences; it’s used automatically when values include zero or negatives. Scale scores are simply divided by the top of the scale. Normalized scores times weights, summed, give the 0–100 total.

What is the AHP consistency ratio (CR)?

If A is 3× as important as B and B is 3× as important as C, A should be about 9× as important as C; the more your judgments contradict each other, the less consistent they are. The tool computes CI = (λmax − n)/(n − 1) from the matrix’s largest eigenvalue and divides it by Saaty’s random index RI to get CR. Below 0.1 is generally acceptable; otherwise the comparison that deviates most is shown so you can revisit it.

How do I read the sensitivity analysis?

For each criterion, the tool changes only that weight, rescales the others proportionally and finds the weight at which another option overtakes the leader — for example, “25% → 47.5%: Gaming Y takes the lead”. The farther that point is from the current weight, the more robust the decision. “Winner stays the same anywhere from 0–100%” means that criterion can’t change the outcome. The sweep chart shows where the lines cross.

Where is my data stored?

All calculations run in your browser, and the matrix, weights and notes autosave only to this browser’s local storage — nothing is uploaded. To share or archive a decision, export a PNG image, a CSV you can open in Excel, or Markdown text.

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🤔Weighted Decision Matrix Calculator with AHP

Torn between job offers, laptops or vendors? Turn gut feeling into comparable numbers. List your options down the side and the criteria you care about across the top, each with a weight. Score each criterion on a 1–5 or 1–10 scale, or enter raw values such as price, weight or commute minutes and mark whether higher or lower is better (benefit or cost); raw values are normalized before weighting, giving every option a 0–100 total and a rank. Enter weights directly (they’re normalized to 100%) or use the Analytic Hierarchy Process: compare criteria two at a time on Saaty’s 1–9 scale, and the weights come from the principal eigenvector of the judgment matrix, along with λmax, CI and the consistency ratio CR — if CR is 0.1 or more, the least consistent comparison is pointed out. Results show a bar for each option split by each criterion’s contribution. Sensitivity analysis finds how far each criterion’s weight would have to move before the winner changes; what-if sliders update the ranking live, and a sweep chart plots every option’s total as one weight goes from 0% to 100%. Jot down pros and cons for each option, start from templates for choosing a job, buying a laptop, picking a vendor or choosing a city, and export a PNG, CSV or Markdown. Your matrix autosaves in this browser.

Free · No sign-up · Runs in your browserLast updated: 2026-10-26

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How to use Decision Matrix

1Pick a template or start blank, then list your options and criteria
2Score each option or enter raw values, and set weights directly or by AHP pairwise comparison
3Review the ranking and sensitivity analysis, try what-if weights, then export PNG, CSV or Markdown

FAQ

How are raw values normalized?: By default with the ratio method: value ÷ maximum for benefit criteria and minimum ÷ value for cost criteria, so the best option gets 1 and the rest scale proportionally. Min–max (worst 0, best 1) is also available and stretches the differences; it’s used automatically when values include zero or negatives. Scale scores are simply divided by the top of the scale. Normalized scores times weights, summed, give the 0–100 total.
What is the AHP consistency ratio (CR)?: If A is 3× as important as B and B is 3× as important as C, A should be about 9× as important as C; the more your judgments contradict each other, the less consistent they are. The tool computes CI = (λmax − n)/(n − 1) from the matrix’s largest eigenvalue and divides it by Saaty’s random index RI to get CR. Below 0.1 is generally acceptable; otherwise the comparison that deviates most is shown so you can revisit it.
How do I read the sensitivity analysis?: For each criterion, the tool changes only that weight, rescales the others proportionally and finds the weight at which another option overtakes the leader — for example, “25% → 47.5%: Gaming Y takes the lead”. The farther that point is from the current weight, the more robust the decision. “Winner stays the same anywhere from 0–100%” means that criterion can’t change the outcome. The sweep chart shows where the lines cross.
Where is my data stored?: All calculations run in your browser, and the matrix, weights and notes autosave only to this browser’s local storage — nothing is uploaded. To share or archive a decision, export a PNG image, a CSV you can open in Excel, or Markdown text.