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Inventory management is a critical business process that affects the operational efficiency and competitiveness of manufacturing companies. Inaccurate inventory decisions can result in significant financial losses for companies. Demand variability poses a challenge in determining inventory levels, requiring more sophisticated, flexible forecasting methods. This study was conducted to examine the roles of statistical methods and Artificial Intelligence (AI) in inventory decision-making in the manufacturing industry, analyze the conditions under which each method is suitable, and evaluate the potential of a hybrid approach integrating statistical methods and AI. This study uses the Systematic Literature Review method with the PRISMA 2020 framework to ensure research transparency and accuracy. This study identifies articles from reputable databases indexed in Scopus. The findings show a significant shift in inventory management research. In the last decade, AI technology has dominated the literature at 62.5%, while statistical methods account for 25%, and hybrid methods have begun to emerge but remain limited to 12.5%. Based on the review of selected papers, statistical methods have proven to remain effective for consistent historical data and stable demand patterns. Conversely, in dynamic operational environments with large-scale data and complex nonlinear patterns, AI technology is superior. This study also found that the hybrid approach has great potential to balance accuracy, interpretability, and decision support, although the relevant literature remains limited. The implementation of technology in the manufacturing industry faces several obstacles, including limited data quality, a skills gap in technology, and the black-box nature of complex AI. This review provides a systematic and critical synthesis of methodological patterns and operational fit in the use of statistical, AI, and hybrid methods for manufacturing inventory management. Future research is recommended to focus on the development of interpretable AI, modular hybrid frameworks, and the use of real industry data to ensure that academic innovations can be applied in the manufacturing industry.
Royani et al. (Fri,) studied this question.