Implementation of a Decision Support System for Selecting the Best Supplier Using the SAW Method
DOI:
https://doi.org/10.32877/bt.v7i3.2255
Keywords:
Decision Support System (SPK) , Micro, Small, and Medium Enterprises (MSMEs), Multi-Criteria Decision Making (MCDM), Simple Additive Weighting (SAW), Supplier Selection
Abstract
This study aims to design and implement a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method in the process of selecting the best supplier within the Micro, Small, and Medium Enterprises (MSMEs) environment, with a case study on Thaiyo Thaitea. In the face of increasingly competitive business environments, choosing the right supplier is a crucial factor in maintaining operational efficiency and service quality. However, the supplier selection process is often subjective and unstructured. Therefore, the SAW method is implemented to provide an objective and systematic approach to evaluating multiple supplier alternatives based on various criteria. This study uses five main criteria in supplier evaluation: price, product quality, completeness, delivery timeliness, and service. The SAW method allows for weighting and normalizing these criteria, resulting in a quantitative ranking of suppliers. The research findings indicate that supplier H. Slamet (A1) achieved the highest score (12.01), making it the best choice for Thaiyo Thaitea. System validation was conducted using the Black Box method to test the application's reliability and functionality. The test results demonstrate that all core features, including criteria data management, assessment processes, and user authentication, function as expected. This study provides practical contributions to improving efficiency and objectivity in the decision-making process for MSMEs. The study's implications indicate that the implementation of the SAW method in DSS can produce more transparent, accurate, and structured decisions. This research opens opportunities for further development through real-time data integration or comparisons with other Multi-Criteria Decision Making (MCDM) methods to enhance decision-making flexibility and accuracy in the future.
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