Assessing the Effectiveness of AI-Driven Techniques for Demand Forecasting and Inventory Optimization in Smart Manufacturing

This study investigates the effectiveness of AI-driven techniques for demand forecasting and inventory optimization in smart manufacturing environments. The purpose was to explore how advanced machine learning algorithms, hybrid models, and adaptive forecasting methods contribute to operational efficiency, inventory control, and strategic decision-making. A qualitative research approach was employed, using purposive sampling to select supply chain managers, production planners, and industry experts. Data were collected through semi-structured interviews, observational site visits, and organizational documents, and analyzed using thematic analysis to identify key patterns, challenges, and benefits associated with AI adoption. The findings reveal that AI technologies enhance forecasting accuracy, enable adaptive inventory management, and support proactive decision-making, while reducing operational inefficiencies, stockouts, and excess inventory. Organizational readiness, skilled personnel, data quality, and robust technological infrastructure were identified as critical factors influencing AI effectiveness. The study further highlights that AI contributes to operational resilience, supply chain coordination, and sustainability initiatives, extending its impact beyond immediate cost and efficiency improvements. The implications suggest that firms adopting AI-driven forecasting and inventory strategies can achieve higher operational agility, align resources strategically, and maintain a competitive advantage in dynamic manufacturing contexts. These results underscore the importance of integrating technological, human, and organizational capabilities to maximize the benefits of AI in smart manufacturing.