Enhancing Demand Forecasting in Supply Chains Through Deep Learning Techniques

Abstract

Demand forecasting is critical in supply chain management, particularly for perishable goods, where inaccurate predictions can lead to significant financial losses and wastage. This paper explores the application of advanced deep learning techniques for food demand forecasting to optimize inventory management, reduce stockouts and overstock situations, and improve overall supply chain efficiency. By employing architectures such as LSTM, Bi-LSTM, Stacked-LSTM, and Transformer-based models, this research demonstrates the transformative potential of these methods in capturing complex temporal relationships, seasonal trends, and external factors influencing demand. Additionally, hybrid approaches integrating wavelet transforms and LSTM are examined to address unique challenges such as seasonality and multivariate dependencies. The outcomes highlight deep learning’s capacity to enhance decision-making, reduce costs, and increase customer satisfaction while paving the way for future innovations in supply chain forecasting. The results indicate that deep learning models greatly surpass conventional forecasting methods in terms of accuracy, flexibility, and scalability. These technological improvements contribute to more informed decision-making processes, decreased operational expenses, minimized food wastage, and higher levels of customer satisfaction. The study underscores the critical role of incorporating AI-based forecasting solutions into contemporary supply chain operations and highlights their potential to drive ongoing advancements and research in the field of predictive analytics.