Comparative study of AHP and Fuzzy AHP Decision-Making Methods in the Selection of Low-Code Platforms in Automotive Startup Company - Bahari, Buyung; Mauritsius, Tuga (International Journal of Intelligent Systems and Applications in Engineering, 2024)
During the "Tech Winter" that struck the tech industry in late 2022, startups faced significant budget cuts, necessitating high efficiency in software development. Automotive Startup Company serves as an exemplar, adopting a strategic approach to address this situation by integrating Low-Code Platforms (LCP) into their product development operations. This study aims to assess the effectiveness of various LCPs to find the most suitable platform for the company’s specific needs. Employing the Analytic Hierarchy Process (AHP) and Fuzzy AHP methods, this research evaluates LCP alternatives based on the Low-Code Platform Attractiveness Measurement Model (LCPAMM) across five main criteria: Usability, Functional Suitability, Control, Maintainability, and Perceived Cost, gathering input from 18 experts consisted of software developers and management in IT department of Automotive Startup Company. The results reveal that OutSystems, scoring 0.478 in AHP and 0.475 in Fuzzy AHP, performs best across all criteria, followed by Mendix and Microsoft Power Apps. Furthermore, Fuzzy AHP proved advantageous in managing the ambiguities and uncertainties often present in subjective assessments. From this analysis, the study concludes that the utilization of LCPs can be a solution to enhance development efficiency and reduce operational costs. Moreover, the proper use of LCPs can potentially offer an alternative solution in the face of workforce reductions, allowing companies to remain competitive in a dynamic market.