<![CDATA[The ongoing digital transformation across business sectors has encouraged micro and small enterprises to adopt information systems that enable accurate data processing and more strategic decision-making. CV. Ragam Jaya is one such business that still depends on manual processes for recording sales transactions and monitoring inventory, resulting in inconsistent stock data, delayed reporting, and limited capability to analyze demand patterns. To address these challenges, this study develops a web-based forecasting and inventory optimization system that integrates Least Square–based demand prediction with Safety Stock calculations. The Rapid Application Development (RAD) framework is utilized to accelerate system construction through iterative prototyping and continuous user involvement. Data were collected through interviews and direct observations to capture operational issues in the existing workflow. The system provides automated forecasting, inventory management, and stock buffer recommendations, enabling users to interpret demand trends more effectively. Experimental evaluation shows that the forecasting module achieves stable trend estimation with an average deviation of less than 8% from historical sales data, indicating strong alignment with actual demand behavior. Blackbox testing was conducted on core modules transactions, forecasting, reporting, and stock optimization and all tests achieved a 100% pass rate, demonstrating consistent system reliability and robustness. The integration of Least Square forecasting and Safety Stock significantly improves inventory planning accuracy by reducing manual discrepancies and supporting timely replenishment decisions. Overall, the developed system is effective in enhancing operational efficiency, minimizing human error, and improving stock control for small distribution businesses seeking to transition toward digitalized management practices.]]>
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