AI-Driven Demand Forecasting in Supply Chains: A Qualitative Analysis of Adoption and Impact

This study investigates the adoption and impact of AI-driven demand forecasting in supply chain management, emphasizing the transformative potential of artificial intelligence (AI) in improving operational efficiency, forecast accuracy, and inventory management. With the increasing complexity of modern supply chains and the need for more accurate predictions, organizations are turning to AI technologies to address challenges such as market volatility, fluctuating consumer demands, and supply chain inefficiencies. The research highlights the factors driving AI adoption, including the desire for enhanced accuracy, proactive decision-making, and competitive advantage. It also explores the significant barriers organizations face during AI implementation, such as high initial costs, data quality issues, and a shortage of skilled professionals. Through thematic analysis, the study identifies key themes related to the drivers, challenges, and long-term benefits of AI-driven demand forecasting. The findings indicate that while AI offers substantial benefits, such as improved forecasting and optimized inventory levels, organizations must navigate complex challenges to successfully implement AI systems. These include overcoming data inconsistencies, addressing resistance to change, and ensuring the availability of necessary expertise. The study concludes by emphasizing the long-term advantages of AI adoption, including cost reductions, improved agility, and enhanced customer satisfaction, while highlighting the importance of strategic planning and investment in data infrastructure and talent development for successful AI integration in supply chains.