SMART - Simple Multi-Attribute Rating Technique

Overview

Apply the F.steps in order; the final score determines the descending ranking.

Output

utility, higher is better

Data

Crisp, complete numeric matrix

Weights

Needs a weight source

Size

2+ alternatives, 3-12 criteria works best

Used for

Alternative selection, Supplier evaluation

How it works

1. 1

   Her kriter için en kötü ve en iyi değer ölçeği uçlarını belirle. Anchor: Edwards 1977 SMART Steps 4 ve 8.

2. 2

   Tek-kriter değerlerini 0=en kötü, 1=en iyi olacak biçimde yeniden ölçekle; varsayılan crisp uygulama yöne duyarlı lineer değer fonksiyonudur.

3. 3

   Oranları koruyan önem puanları q_j'yi w_j=q_j/Σq olarak normalize et. Anchor: Edwards 1977 SMART Steps 5-7.

4. 4

   U(A_i)=Σ_j w_j u_j(x_ij) toplam değerini hesapla ve azalan sırala. Anchor: Edwards 1977 SMART Steps 9-10.

Look elsewhere when

Assumptions to verify

• Criteria preferences are independent (no synergistic interactions)
• Compensation is acceptable: high score on one criterion can offset low on another
• Decision matrix is complete (no missing values)

Edge cases and pitfalls

• Applying SMART without verifying this assumption.

  Requirement: Criteria preferences are independent (no synergistic interactions)

• Applying SMART without verifying this assumption.

  Requirement: Compensation is acceptable: high score on one criterion can offset low on another

• Applying SMART without verifying this assumption.

  Requirement: Decision matrix is complete (no missing values)

• Using SMART when: Criteria strongly correlated → consider DEMATEL/ANP for interdependence.

  An alternative method is recommended in this situation.

• Using SMART when: Non-compensatory preferences → consider outranking (ELECTRE/PROMETHEE).

  An alternative method is recommended in this situation.

Works with

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