<![CDATA[This study investigates the impact of machine learning algorithms, demand prediction, and automation systems on responsive inventory management in the retail industry of Central Java. Using a quantitative approach, data were collected from 160 respondents through a structured questionnaire employing a Likert scale (1–5) and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS 3). The findings reveal that automation systems and demand prediction significantly and positively influence responsive inventory management, while machine learning algorithms exhibit a significant but negative relationship. Automation systems streamline processes and improve efficiency, and demand prediction enhances inventory alignment with market needs. However, challenges such as limited technical expertise and integration issues hinder the effective use of machine learning. These results underscore the importance of strategic technology adoption and provide practical insights for improving inventory management practices in the retail sector of developing regions.]]>
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