Illustrative example: a representative scenario, not a customer story
Supplier Selection in Manufacturing
A manufacturing company evaluates 12 suppliers across quality, cost, delivery, and sustainability criteria. TOPSIS + AHP weighting identifies optimal long-term partners.
The decision problem
A manufacturer buys the same component from a long supplier list and wants to reduce it to a shortlist of long term partners. Price is not the only thing at stake: a cheap supplier that misses delivery windows stops the line, and a supplier with weak finances is a risk that surfaces years later. The buying team can order the criteria by importance but cannot put a number on them directly. The question is who stays on the list, and how much the answer depends on how heavily quality is weighted.
What the decision matrix looks like
The typical shape of a study like this. Your own matrix can be larger or smaller; nothing here is fixed.
12
Alternatives
5
Criteria
Subjective expert judgement
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Unit cost |
| C2 | Defect rate |
| C3 | Lead time |
| C4 | Financial strength |
| C5 | Carbon intensity |
Which method, and why
AHP turns the team's pairwise judgements into weights and reports a consistency ratio, so an incoherent set of judgements is caught before it reaches the ranking. TOPSIS is compensatory: a strong score on one criterion can offset a weaker one, which is how procurement actually trades price against lead time. VIKOR runs on the same matrix as a compromise cross-check, and disagreement between the two orders is itself a finding.
What you get out
Ranking and scores
Every alternative with its score, its rank and the intermediate matrices that produced them.
Weight sensitivity
One criterion weight moves at a time, so you see exactly where the leader changes.
Method agreement
When more than one ranking method is run, the orders are compared with Spearman rho and Kendall W.
Report and citation
PDF, DOCX and XLSX output carrying the seminal source of every method used.
Run this flow on your own data
Load the matrix, choose the weighting and ranking methods, read the sensitivity.