Abstract

AI-Powered Demand Forecasting for Enhancing Just-In-Time (JIT) Inventory Models explores how artificial intelligence (AI) can significantly improve the accuracy and efficiency of demand forecasting within JIT inventory systems. JIT inventory management relies on precise demand predictions to minimize stock levels and reduce holding costs, but traditional forecasting methods often struggle with volatility and unforeseen market fluctuations. AI, through machine learning (ML) algorithms, predictive analytics, and big data, offers a powerful solution to these challenges. By leveraging vast amounts of real-time and historical data, AI can provide more accurate, dynamic, and responsive demand forecasts that allow businesses to fine-tune their inventory levels, optimize reorder points, and reduce stockouts or excess inventory. AI techniques such as deep learning, reinforcement learning, and time series forecasting (e.g., ARIMA, LSTM) enable the identification of complex demand patterns, seasonality, and even external factors like market trends, weather, and social media influence. These AI models can adapt to rapidly changing conditions, making them highly effective in volatile supply chains. The integration of AI in JIT systems allows for continuous learning, where the model refines its forecasts over time based on new data, improving the overall agility and responsiveness of the supply chain. The application of AI-powered demand forecasting in JIT systems leads to improved inventory control, reduced operational costs, better supplier relationships, and enhanced customer satisfaction. However, challenges such as data quality, high initial investments, and AI model transparency remain. Despite these obstacles, AI represents a transformative technology capable of significantly enhancing JIT inventory models, providing businesses with the tools to manage inventory more efficiently in a dynamic and competitive environment.