<![CDATA[The manufacturing industry faces major challenges in maintaining consistent product quality amidst the dynamics of technology and global competition. This study aims to develop an effective Business Intelligence (BI) implementation model to support data-based quality control. The method used is a conceptual design approach through integrated system simulation, including MySQL database, PHP backend, Power BI visualization, Google Cloud AutoML predictive analytics, and initial processing using Microsoft Excel. Historical production data for 12 months is used for model training and defect trend visualization. The simulation results show that the implementation of BI can reduce product defect rates, accelerate system response, and increase inspection process efficiency. Technical validation proves the model's prediction accuracy is above 90%, while field validation shows positive acceptance from users regarding the ease of use of the dashboard. This system not only supports early detection of quality deviations but also contributes to real-time strategic decision making. With an integrated technology approach, BI enables medium-sized manufacturing companies to adopt an adaptive and sustainable digital quality system, in line with the concept of Quality 4.0.]]>
Copyrights © 2025
