ArticleScientific reports2025
A multi-dimensional evaluation model for power enterprise procurement performance based on fuzzy analytic hierarchy process and TOPSIS integration.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Power enterprises face unique procurement challenges, including grid reliability requirements, regulatory complexities, and long-term asset investment considerations that existing evaluation methods inadequately address. Traditional approaches suffer from three critical limitations: inability to handle linguistic uncertainty (consistency ratios often exceeding 0.15), failure to integrate power-specific criteria simultaneously, and lack of systematic frameworks for aggregating conflicting stakeholder objectives. This study develops a comprehensive multi-dimensional evaluation model by integrating Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS methodologies specifically tailored for power enterprise contexts. The proposed framework achieves 23% improved consistency ratios compared to traditional AHP methods while systematically addressing uncertainty in expert judgments. The model constructs a hierarchical evaluation system encompassing four primary dimensions: cost-benefit performance, quality management, supplier relationship management, and risk control. FAHP handles subjective expert judgments and linguistic uncertainties in weight determination, while TOPSIS provides robust ranking capabilities through distance-based proximity measures. Empirical validation through a large regional power enterprise case study demonstrates model effectiveness. Results show a comprehensive closeness coefficient of 0.6651, positioning the enterprise at the 73rd industry percentile with strengths in quality management and improvement opportunities in supplier relationship development. Multi-dimensional sensitivity analysis across five uncertainty sources confirms model robustness, with coefficient variations within ± 5.7% and 96.3% ranking stability. Cross-validation with three peer enterprises (closeness coefficients ranging 0.5834-0.7245) demonstrates 91.3% performance gap identification accuracy. The integrated framework provides enhanced analytical capabilities for strategic procurement decision-making and continuous improvement initiatives in power enterprises.
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