Applying the Fuzzy Delphi Method in the Cement Industry: Enhancing Decision-Making and Operational Efficiency

Introduction: The cement industry plays a critical role in economic growth but faces challenges such as high energy consumption, environmental pollution, and rising production costs. Increasing global competition and the need for sustainability demand advanced decision-making approaches. The Fuzzy Delphi Method (FDM), combining expert consensus with fuzzy logic, enhances decision- making under uncertainty. This study explores FDM’s applications in optimizing managerial strategies and industrial performance.

Methods: This research follows a narrative review of studies from 2015 to 2024 on FDM in the cement industry, analyzing data from peer-reviewed articles, industrial reports, and case studies. Key applications include assessing environmental impacts, reducing pollutants, optimizing energy use, prioritizing performance indicators, managing risks, and enhancing competitiveness. A qualitative analysis was conducted to evaluate FDM’s effectiveness and limitations.

Results: Findings indicate that FDM converts expert opinions into quantifiable data, reducing uncertainty in strategic decisions and improving efficiency. The method has significantly contributed to higher product quality, reduced waste, enhanced energy efficiency, and innovative environmental solutions. Additionally, it fosters expert collaboration, aiding informed policy-making and sustainable development.

Conclusion: FDM is a valuable tool for improving decision-making, reducing costs, and promoting sustainability in the cement industry. Given ongoing challenges in energy management and environmental concerns, future research should explore hybrid models integrating FDM with other decision-making techniques. The method’s application can drive greater efficiency, competitiveness, and long-term industrial sustainability. Support: This study received ethical approval from the Isfahan University of Medical Sciences, Isfahan, Iran (Ethical approval number: IR.MUI.REC.1401–063). The research was funded by the Isfahan University of Medical Sciences (Grant number: 3401657).

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Artikel online veröffentlicht:
08. Oktober 2025