Abstract: Effective inventory management and accurate demand forecasting remain central to the success of modern retail and supply chain systems. Industries must continually put up a delicate balance between holding enough stock to meet customer needs while avoiding excess that leads to high costs and waste. This paper provides a comprehensive review of the methods and models developed to address this challenge. It explores traditional approaches such as statistical and econometric models, as well as newer techniques based on machine learning and also hybrid frameworks that combine the strengths of different models to achieve greater accuracy and adaptability. Recent research has explored a wide range of approaches ranging from classical deterministic models such as Economic Order Quantity (EOQ) [2], to statistical methods like ARIMA [5], and modern machine learning and deep learning approaches including LSTM and CNN-LSTM [15]. This survey provides insights from thirty scholarly works, examining methodological advancements and applications across retail, manufacturing, and food industries. The review highlights key contributions, compares model performances, and discusses practical challenges in data preprocessing, model selection, and performance evaluation. Findings indicate that while traditional models remain useful for structured environments, data-driven and hybrid models offer superior adaptability in uncertain, dynamic markets. Future work emphasizes integrating explainable AI with real-time optimization to bridge the gap between theoretical models and industrial practice.

Keywords: Inventory Management; Demand Forecasting; Supply Chain Systems; Machine Learning; Deep Learning; Hybrid Models; ARIMA; LSTM; Explainable AI; Real-time Optimization.

Downloads: | DOI: 10.17148/IARJSET.2025.12823