## 1 Introduction  
As enterprises grow larger, the role of inventory as a necessary buffer to maintain production and adapt to fluctuations in demand becomes more significant. As the company expands, there is a greater requirement for a range of raw materials, partially processed goods, and purchased components. As a result, there is a greater need for capital to handle these items. A high forecast for inventory demand resulting in a backlog can hinder company growth and potentially lead to bankruptcy [1, 2, 3]. Therefore, we cannot overstate the importance of inventory management in the manufacturing sector. In China, the actual cost of producing a product represents only 10% of the overall cost, while logistic expenses make up around 40%. Among these logistic expenses, inventory costs make up a substantial portion, ranging from 80 to 90%, which translates to 32-36% of the total product cost [4]. This is significantly higher than the cost of directly manufacturing products. Additionally, a low forecasted demand for inventory could lead to losses from stock-outs, impacting not just one company but the entire supply chain. Companies such as a global manufacturing corporation-owned commercial logistics company, Boeing, and Airbus, with global subsidiaries, exemplify this phenomenon. Many of their main products consist of numerous parts, ranging from hundreds to tens of thousands [5, 6, 7, 8]. Furthermore, a single supplier involves numerous companies worldwide, ranging from dozens to hundreds. Inadequate or excessive inventory levels can impact thousands of companies in the supply chain, affecting the overall health of these large enterprises [9; 10]. This underscores the importance of a logical and scientific forecast for inventory demand. As companies expand, the number and variety of products they produce also increase, making inventory demand forecasting more challenging. For instance, as companies grow, their product range becomes larger and more complex [11; 12]. A subsidiary of a major global manufacturing company focused on commercial logistics brought attention to the difficulties of predicting inventory needs for large items such as Boeing and Airbus aircraft, which consist of numerous parts. The complexity of forecasting inventory demands for these huge companies is apparent. Previous studies in accounting and inventory management have mainly concentrated on either controlling inventory or planning for inventory needs [13; 14]. Taking into consideration the different systematic control solutions available, prior studies have aimed to determine the optimal timing and quantity for ordering inventory [15]. Furthermore, experts recommend different strategies for material planning based on the duration of product use. Most suggested analytical methods approach inventory management problems as a multi-faceted optimization challenge, aiming to minimize expenses related to ordering and storing inventory while simultaneously maximizing profit or overall usefulness [16]. Prior research has typically used stochastic approximations to predict expected backorders based on the base stock level. Researchers have recently introduced a model that analyzes inventory levels across multiple levels of distribution to predict stock-outs. Another method for forecasting inventory levels is time series forecasting. However, there is a wealth of information in inventory databases that are not typically utilized, such as previous stock levels, amounts of overdue stock, and flags indicating operational issues [17; 18]. Despite there being numerous studies conducted on anticipating inventory backorders, there is a current lack of research in the field when it comes to a profit-driven model based on big data [19; 20]. The primary goal of this research is to fill the gap by examining how a model can provide valuable insights into the financial benefits of utilizing optimal backorder strategies in the age of big data [21]. Moreover, this study underscores a specific challenge in utilizing machine learning classifiers for this purpose.