Illustrative example: a representative scenario, not a customer story
Crop Selection Under Climate Change
Determine optimal crop varieties for climate-vulnerable regions considering yield stability, water requirements, and market value. Fuzzy modeling captures agronomic uncertainty while best-worst weighting reflects farmer preferences.
The decision problem
Eight crop varieties are compared for a region where the water budget is shrinking. Climate projections give ranges, not values, and a variety that yields well in an average year can fail in a dry one. Farmers and agronomists can say which variety is best and which is worst on each criterion, but they cannot assign percentages.
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.
8
Alternatives
5
Criteria
Climate projection uncertainty
Uncertainty type
| # | Criterion |
|---|---|
| C1 | Yield stability |
| C2 | Water requirement |
| C3 | Heat tolerance |
| C4 | Input cost |
| C5 | Market price |
Which method, and why
Fuzzy VIKOR keeps the projection ranges as fuzzy numbers and still returns a compromise ranking, so yield stability is not reduced to a single expected value. BWM asks only for the best and the worst reference on each criterion and derives consistent weights from those, which is a realistic amount of work for a field panel. CODAS cross-checks the order with a combined distance measure.
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.