Application of Fuzzy Multi-Criteria Decision Making in Entrepreneurial Venture Evaluation

Abstract

This study investigates vendor selection criteria within the framework of supply chain management using the Fuzzy Analytic Hierarchy Process (FAHP). To ensure reliability, expert insights were obtained from two industry professionals, each with over 30 years of experience. Six key criteria were evaluated: Quality (C1), Cost and Value (C2), Delivery and Logistics (C3), Customer Service and Support (C4), Security and Risk Management (C5), and Technical Expertise and Capabilities (C6). The FAHP approach was applied to determine the relative importance of these factors. The analysis showed that Cost and Value (C2) emerged as the most critical consideration, while Quality (C1) ranked lowest in priority. These results offer valuable guidance for organizations, highlighting the need to prioritize cost efficiency and value creation when formulating vendor selection strategies.

Introduction

In today’s competitive business landscape, effective supply chain management (SCM) is vital, with vendor selection being a key strategic decision. Vendors act as crucial partners influencing costs, quality, delivery, and overall supply chain resilience. While traditional vendor selection focused on cost and quality, modern complexities demand evaluation across multiple criteria including delivery, service, risk, and technical capabilities. This complexity makes vendor selection a multi-criteria decision-making (MCDM) problem, complicated further by uncertainty and subjective human judgments.

Fuzzy set theory, particularly the Fuzzy Analytic Hierarchy Process (FAHP), addresses these challenges by allowing decision-makers to incorporate vague and qualitative assessments naturally into the decision process. FAHP enhances traditional AHP by using fuzzy numbers to better capture uncertainty in expert judgments and has been successfully applied in various industries for vendor evaluation.

However, most existing models view vendor selection as a static, one-time decision, ignoring how vendor performance and organizational needs change over time. This study proposes a dynamic FAHP framework that integrates time-based evaluation to reflect evolving vendor performance and strategic priorities. The model aims to improve decision accuracy, flexibility, and transparency for supply chain managers.

The literature review traces the evolution of supplier selection methodologies from traditional cost-quality focus to advanced fuzzy and hybrid models, highlighting significant contributions in multi-criteria methods, fuzzy logic, and integrated decision-making approaches. The Fuzzy AHP method, combining AHP with fuzzy logic via triangular fuzzy numbers, provides a robust tool for managing uncertainty in complex supplier evaluation.

Conclusion

This study examined the evaluation of vendor selection criteria in supply chain management using the Fuzzy Analytic Hierarchy Process (FAHP). By consulting two highly experienced industry experts, six key criteria—Quality, Cost and Value, Delivery and Logistics, Customer Service and Support, Security and Risk Management, and Technical Expertise and Capabilities—were assessed for their relative importance. The findings indicate that Cost and Value is the most significant factor in vendor selection, whereas Quality holds the lowest priority among the evaluated criteria. These results emphasize the importance of aligning vendor selection strategies with practical organizational priorities, particularly focusing on cost efficiency and value creation. The study demonstrates that FAHP provides a systematic and reliable approach for quantifying expert judgments under uncertainty, offering actionable insights for supply chain decision-makers. Future research may extend this framework to include dynamic, time-based evaluations or integrate additional sustainability and risk-related criteria to further enhance decision-making accuracy and strategic alignment.

References

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Copyright

Copyright © 2025 Sunny Khanna, E. Balaji, Harpreet Kaur, Charanjit Singh, Dr. K. Akila, Prerna Chanana. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.