Inventory and Decision Support System for Entrepreneurs Using Computational Algorithms

This study presents the development of an inventory and decision support system designed to assist entrepreneurs managing small and medium-sized enterprises. Utilizing computational algorithms, the system incorporates ABC classification, rule association mining, and reorder point analysis to optimize inventory decision-making. The algorithms were evaluated using the F1 score, with rule association and reorder point analyses achieving a perfect score of 1.0, and ABC analysis recording a score of 0.71. The system was further validated through ISO/IEC 25010 standards and a user acceptability test involving 14 participants, including business owners and IT professionals. Evaluation results showed excellent ratings across software quality characteristics, demonstrating the system's reliability, usability, compatibility, and maintainability. These findings highlight the practical applicability of computational algorithms in enhancing smart inventory management for small businesses.