Analysis of Multi-Objective Decision-Making Indicators for Evaluating Smart Supply Chain Risks in the Electricity Industry Using the Attride-Stirling Method

Document Type : Original Article

Authors

1 Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 PhD, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Phd, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract
This study analyzes multi-objective decision-making indicators for evaluating non-dominated solutions to smart supply chain risks in the electricity industry using the Attride-Stirling thematic analysis method. The research is applied in purpose and adopts a descriptive-analytical, qualitative survey design. The expert panel consisted of 18 electricity industry professionals and academic specialists with expertise in smart supply chains and decision-making, whose insights were employed to identify, refine, and validate the emerging themes.
Data were collected through an extensive review of the relevant literature and semi-structured expert interviews. Thematic analysis was conducted following Braun and Clarke’s six-phase framework, including data familiarization, initial coding, theme generation, theme review, theme definition and naming, and report preparation. The analysis yielded 45 initial codes organized around the overarching theme of developing a multi-objective framework for evaluating non-dominated solutions to smart electricity supply chain risks. Three second-level organizing themes were identified: risk and reliability criteria, economic and cost criteria, and operational and resilience criteria. To ensure methodological rigor, Holsti’s coefficient, Scott’s pi, Cohen’s kappa, and Krippendorff’s alpha were employed to evaluate credibility and reliability, yielding values of 0.811, 0.835, 0.850, and 0.909, respectively, indicating substantial inter-coder agreement. The proposed framework provides a conceptually coherent and reliable basis for supporting multi-objective decision-making, selecting Pareto-efficient solutions, strengthening intelligent risk management, and enhancing digitally enabled supply chain resilience in the electricity industry.

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Subjects

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  • Receive Date 09 December 2025
  • Revise Date 10 January 2026
  • Accept Date 25 February 2026