International Journal of Advanced Multidisciplinary Research and Studies
Volume 3, Issue 6, 2023
Artificial Intelligence in Demand Forecasting and Inventory Optimization
Author(s): Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho
DOI: https://doi.org/10.62225/2583049X.2023.3.6.4617
Abstract:
Artificial Intelligence (AI) is transforming traditional supply chain management by enabling more accurate demand forecasting and efficient inventory optimization. This paper explores how AI-powered technologies are reshaping the way businesses anticipate customer demand and manage inventory levels, especially in dynamic and uncertain markets. Conventional forecasting methods often rely on historical data and fail to adapt to real-time market fluctuations, leading to overstocking, stockouts, and lost revenue. AI algorithms, including machine learning and deep learning models, analyze vast datasets ranging from sales records and seasonal trends to social media sentiments and weather patterns to generate predictive insights with higher accuracy. These insights support proactive decision-making, allowing firms to align supply with anticipated demand and reduce operational costs. Furthermore, AI facilitates dynamic inventory optimization by continuously adjusting stock levels based on real-time demand signals, supplier reliability, lead times, and customer behavior. Advanced tools such as reinforcement learning and neural networks enhance adaptive inventory policies, enabling organizations to respond to changing conditions with agility. The paper also presents practical applications across industries such as retail, manufacturing, and e-commerce, highlighting measurable improvements in service levels, inventory turnover, and cost savings. Despite its advantages, AI integration poses challenges, including data quality issues, algorithm transparency, implementation complexity, and workforce readiness. The paper offers mitigation strategies and outlines best practices for successful AI adoption. It also discusses future trends, such as the convergence of AI with Internet of Things (IoT) and blockchain, which promise to further streamline forecasting and inventory control. In conclusion, the study emphasizes that AI-driven demand forecasting and inventory optimization are not just technological enhancements but strategic imperatives for achieving supply chain resilience, customer satisfaction, and competitive advantage in the digital era.
Keywords: Artificial Intelligence, Demand Forecasting, Inventory Optimization, Machine Learning, Supply Chain Management, Predictive Analytics, Reinforcement Learning, Neural Networks, Dynamic Inventory, Data-Driven Decision-Making
Pages: 1930-1948
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