Contents I. Introduction
Machine learning algorithms, especially models based on large-scale data, are developing rapidly, providing possibilities for physical demand prediction[1], [2]. Demand forecasting in logistics plays a crucial role in the efficient management of warehouse operations, inventory control, and overall supply chain optimization. The accuracy of demand forecasts directly impacts various aspects of logistics, including space utilization, labor management, transportation planning, and inventory control [4]. However, existing forecasting methods often struggle to produce precise forecasts due to the influence of external factors such as holidays and seasonal fluctuations on warehouse demand [3]. To address this challenge, companies are increasingly turning to advanced technologies such as artificial intelligence (AI) and predictive analytics to enhance their demand forecasting capabilities [6], [11], [12]. Demand forecasting in logistics involves a series of business decisions that have a direct impact on profit margins [5]. Warehouse managers are tasked with making accurate demand forecasts to ensure optimal resource allocation and operational efficiency. By leveraging historical data analysis and advanced forecasting methods, companies can gain valuable insights into future demand patterns and make informed decisions regarding inventory levels, transportation planning, and warehousing operations [10]. Additionally, the integration of AI and predictive analytics enables companies to predict customer purchasing behavior with greater accuracy, leading to improved supply chain performance and customer satisfaction [12]. The logistics demand forecasting process typically includes data acquisition, storage, preprocessing, and standardization [7]. By effectively managing these steps, companies can ensure the quality and reliability of their demand forecasts, leading to better planning and decision-making. Furthermore, demand forecasting is closely linked to other supply chain functions such as sales and operations planning (S&OP), where accurate demand forecasts serve as the foundation for strategic decision-making and resource allocation [8]. In the context of modern supply chains, demand forecasting has become increasingly complex due to the dynamic nature of consumer behavior and market trends. Companies must adapt to changing demand patterns and evolving customer preferences to remain competitive in the global marketplace. Smart Warehousing solutions offer advanced tools and technologies to enhance demand forecasting accuracy and enable companies to collaborate effectively with partners and suppliers [9]. By leveraging predictive analytics and AI -driven insights, companies can gain a competitive edge in demand forecasting and supply chain management [11]. Overall, demand forecasting in logistics is a critical component of effective supply chain management. By leveraging advanced technologies and best practices, companies can improve their forecasting accuracy, optimize resource allocation, and enhance overall operational efficiency. As supply chain leaders increasingly turn to AI and predictive analytics to revolutionize their demand forecasting strategies, the future of logistics demand forecasting looks promising [12].
