# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 1 Introduction  
Forecasting demand is an essential part of managing the supply chain and has a considerable influence on planning, capacity, and inventory control choices. The significance of precise demand prediction becomes apparent in inventory control when incorrect forecasts lead to higher backlog and holding expenses [1, 2, 3]. The uncertainty of demand has a significant impact on order amplification, which affects all participants in the supply chain. In the broader field of supply chain management, accurate demand forecasting is crucial for making decisions, allocating resources, and improving operational efficiency [4, 5]. However, traditional methodologies face challenges in capturing the complex dynamics of modern supply chains as global markets evolve and become more interconnected [6]. Linear models and time series analyses, though foundational, struggle to predict the nonlinear and intricate relationships that characterize contemporary business environments. Traditional methods of demand forecasting are no longer effective in accurately predicting customer demands due to intense competition in various industries [7, 8, 9, 10, 11, 12]. In order to address this problem, companies are currently implementing advanced data science methods to predict customer demand. By treating customer demand as a series of data points over a period of time, the issue of demand forecasting can be seen as a challenge of predicting values in a time series [13, 14, 15]. However, predicting the demand for componentspresents its own difficulties, such as a lack of adequate information about downstream processes, sporadic occurrences of demand, and a limited comprehension of market trends. It is crucial to address these challenges in order to reduce inventory costs and make flexible decisions in agile supply chains. While there have been several studies on supply chain management, limited research has specifically focused on the issues related to forecasting demand for components [16; 17]. Conventional approaches like moving averages and the Croston method, which is commonly used, may not be effective in dealing with the growing volatility of the supply chain [11; 18; 19]. The demand patterns in the supply chain industry are constantly changing due to technological advancements, globalization, and changing consumer preferences. Traditional forecasting models struggle to keep up with these nonlinear and unpredictable patterns. Industries that rely on quick-to-market products and are heavily influenced by market trends face particular challenges in accurately predicting future demand [20]. This leads to inefficient inventory management and resource allocation [11; 21; 22; 23]. Linear models like linear regression oversimplify the relationships between input variables and demand, making it difficult to capture the complex nature of supply chain data [24]. Time series analysis, although effective in capturing patterns over time, often fails to account for sudden shifts and irregularities in demand, resulting in inaccurate forecasts [25]. In recent years, artificial neural networks have gained attention in the field of computational intelligence due to their adaptability and robustness [10; 26; 27; 28; 29; 30]. These networks excel in decision-making, offering the ability to adapt to environmental changes, handle nonlinear systems, and process data quickly [31]. Machine learning (ML) has been shown to be more effective than traditional methods in improving customer engagement and accuracy of demand forecasts, due to the application of predictive analytics. ML techniques are particularly skilled at managing intricate interdependencies and nonlinear relationships, ultimately leading to enhanced performance across the supply [8; 9; 32; 33]. Promising results for ML approaches are discovered after applying them to large-scale homogenous product sales data on Amazon. Additional examples include foundries and chocolate manufacture, where the time series are likewise homogeneous [34].