Decision Science (MCDA) Decision Frameworks

I need to compare options across many criteria — which MCDA method should I start with?

Start with AHP (Analytic Hierarchy Process) if you have fewer than 9 criteria and want to derive weights through pairwise comparisons. For more criteria or when you already know your weights, TOPSIS is faster — it ranks options by geometric distance from the ideal solution. SolveRight runs both simultaneously so you can compare their recommendations.

When should I use outranking methods like ELECTRE instead of scoring methods?

Outranking methods (ELECTRE, PROMETHEE) excel when criteria are measured on incomparable scales — for example, comparing cost in dollars against environmental impact in CO2 tons. Unlike scoring methods, outranking does not require converting everything to a common scale, which avoids the information loss of normalization.

How do I handle uncertainty in my criteria scores?

Use fuzzy MCDA variants (Fuzzy AHP, Fuzzy TOPSIS) when your input data has inherent uncertainty or imprecision. These frameworks represent scores as ranges rather than point values, propagating uncertainty through the entire calculation so your final ranking includes confidence intervals.

My team cannot agree on criteria weights — what should I do?

Run a sensitivity analysis. SolveRight lets you adjust weights in real time and see how rankings change. If the same option wins across a wide range of weight configurations, you have a robust choice regardless of weight disagreements. If rankings flip easily, the weight debate genuinely matters and warrants facilitated discussion.