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<p>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.</p>

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A multi-dimensional evaluation model for power enterprise procurement performance based on fuzzy analytic hierarchy process and TOPSIS integration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Haigang Zhao,
  • Shugui Song,
  • Xiaoqing Lv,
  • Yu Bao

摘要

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.