OCRA - Operational Competitiveness Rating

Overview

P_i ≥ 0 (the minimum always equals 0 by construction). Higher P means better overall operational competitiveness. OCRA naturally separates input (cost) and output (benefit) criteria, making it intuitive for production/efficiency evaluation where inputs are resources consumed and outputs are products/services delivered.

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

   Aggregated input (cost) preference Ī_i.

   $${\bar{I}}_i=\sum _{j\in J^{-}}{w}_j\frac{{\max }_k{x}_{kj}-x_{ij}}{{\min }_k{x}_{kj}}$$

   Parkan 1994, p.6 Eq.(1)

2. 2

   Aggregated output (benefit) preference Ō_i.

   $${\bar{O}}_i=\sum _{j\in J^{+}}{w}_j\frac{x_{ij}-{\min }_k{x}_{kj}}{{\min }_k{x}_{kj}}$$

   Parkan 1994, p.6 Eq.(2)

3. 3

   Overall preference P_i = (Ī_i − min Ī) + (Ō_i − min Ō) − min[(Ī_i − min Ī) + (Ō_i − min Ō)].

   $$P_i=({\bar{I}}_i-\underset{k}{\min }{\bar{I}}_k)+({\bar{O}}_i-\underset{k}{\min }{\bar{O}}_k)-\underset{i^{\prime }}{\min }[\cdot ]$$

   Parkan 1994, p.7 Eq.(3)

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 OCRA when there are no cost criteria (all benefit): Ī = 0 for all, P reduces to Ō ranking only.

Works with

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