Overall Equipment Effectiveness (OEE) measures manufacturing productivity as the product of Availability (A), Performance (P), and Quality (Q). Despite its widespread adoption, the classical OEE formula embeds a structural limitation, i.e., the three components are treated as equally important regardless of operational context. This fixed-weight assumption distorts maintenance prioritisation in environments where one component dominates operational losses. To the best of the authors’ knowledge, no published framework has formally addressed this limitation through a structured, auditable multi-criteria weighting model. This paper proposes Adaptive OEE, a FUCOM–TOPSIS framework that replaces the fixed A × P × Q product with a context-driven weighting model. FUCOM derives context-specific weights for A, P, and Q from expert judgement with minimum elicitation effort and mathematically guaranteed consistency. TOPSIS is adapted from its classical formulation by replacing data-derived ideal solutions with fixed reference poles defined independently of the observed data, ensuring that the effectiveness score of each asset is not influenced by the performance of other assets in the dataset. Three illustrative case studies covering availability-dominant, performance-dominant, and quality-dominant industrial scenarios suggest that classical OEE rankings are not preserved under asymmetric weight configurations, with ranking divergence being most severe when one component carries strongly asymmetric weight, precisely the condition that equal weighting cannot accommodate. The principal contributions are the formalisation of the equal-weight assumption as a formal methodological limitation, the replacement of multiplicative aggregation with a weighted distance measure, and the adaptation of TOPSIS with fixed reference poles for context-independent asset scoring. The framework is directly applicable by maintenance managers and industrial engineers seeking operationally justified equipment rankings without specialised analytical expertise.
Anes et al. (Wed,) studied this question.