A Fuzzy Multi-Criteria Approach for Selecting Open-Source ERP Systems in SMEs Using Fuzzy AHP and TOPSIS

Main Article Content

Dana Marsetiya Utama
Muhammad Faisal Ibrahim
Ahmed Nedal Abid Al Kareem Jabari

Keywords

ERP, fuzzy AHP, fuzzy TOPSIS, small and medium enterprises (SMEs)

Abstract

In a rapidly growing and competitive business era, selecting an open-source Enterprise Resource Planning (ERP) system is a critical step to support the efficiency and effectiveness of company operations. This research aims to propose an innovative methodology by integrating the fuzzy Analytical Hierarchy Process (fuzzy AHP) and fuzzy Technique for Order Preference by Similarity to the Ideal Solution (fuzzy TOPSIS) to improve the open-source ERP selection process. The method involves eight criteria and 26 sub-criteria to comprehensively evaluate 11 open-source ERP alternatives, specifically for SMEs in the transportation services sector in Indonesia. System quality has been identified as a critical factor in the selection of an open-source ERP system, with particular emphasis on aspects such as security and reliability. These sub-criteria are considered the most influential in determining the suitability of a system. The analysis further indicates that the 10th ERP alternative as the best choice, consistently outperforming others in meeting the defined criteria. Additionally, sensitivity analysis confirmed the robustness of this choice, demonstrating its stability and effectiveness despite changes in criteria weights. Beyond its practical implications for SMEs, this research contributes a versatile evaluation framework that can be adapted to other industries seeking effective ERP solutions. The findings emphasize the importance of structured decision-making in technology adoption, offering comprehensive and reliable guidance for organizations aiming to optimize their operations through open-source ERP systems. This study not only bridges a critical gap in ERP selection for SMEs but also establishes a methodological foundation for future research and applications across diverse industry sectors.

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