Enhancing energy resilience in enterprises: a multi-criteria approach

Abstract

Unreliable energy supply disrupts productivity and escalates costs in manufacturing enterprises, highlighting the need for robust energy resilience strategies. Existing multi-criteria decision-making (MCDM) methods, such as the analytic hierarchy process (AHP), best–worst method (BWM), and unaugmented TOPSIS, often lack advanced risk prioritization and objective criteria weighting, limiting their effectiveness in addressing complex, multidimensional energy challenges. This study proposes an integrated framework that combines Failure Mode and Effects Analysis (FMEA) for risk prioritization, the CRITIC-method for objective criteria weighting based on the 4A’s energy resilience dimensions—availability, accessibility, affordability, and acceptability—and fuzzy-TOPSIS for strategy ranking. The framework bridges theoretical rigor with operational applicability, addressing the unique challenges faced by manufacturing enterprises. Validated through a real-world case study and sensitivity analysis, it identifies actionable strategies, such as flexible scheduling, demonstrating consistent rankings across comparative analyses with fuzzy VIKOR, WASPAS, and MARCOS. This sector-specific approach advances both academic understanding and industrial practice by providing a scalable, multidimensional tool for enhancing energy resilience. The study presents a robust framework integrating FMEA, the CRITIC-method, and fuzzy-TOPSIS within the 4A’s energy resilience framework to address challenges in manufacturing enterprises. It prioritizes risk, provides objective criteria weighting, and ranks strategies under uncertainty. Validated through a manufacturing case study, the framework identifies effective strategies and demonstrates adaptability, confirming robustness via sensitivity and comparative analyses. Demonstrating adaptability under varying criteria weights (± 20%) and reliability across MCDM methods, it offers a multidimensional tool advancing theoretical understanding and practical decision-making in manufacturing.