The emerging programs of e-commerce have changed the international retail environment providing new opportunities and challenge of supply chain management (SCM) in an unprecedented manner. Efficient SCM has taken center stage in the e-commerce firms to handle increased customer demands of delivery speed, perfectly matched order deliveries, and cost effective operations. The typical supply chain tactics are typically ineffective dealing with the dynamic alternative of online retail such as variable demand, trends of seasons and intricate logistics networks [1]. The obstacles require the new innovative solutions and advancements that would lead to better decision-making, streamlined operations, and decreased expenses. Machine learning (ML) is an artificial intelligence branch that has turned out to be a transformative technology that can tackle the intricacies of the present-day supply chains [2]. ML algorithms may reveal patterns, predict changes in demand and optimize inventory quantities, as well as enhance route planning, using huge amounts of historical and real-time data. More importantly, within the framework of e-commerce, ML has noteworthy possibilities to support the improvement of demand prediction, decreased stockouts and overstocking, and efficient warehouse logistics [3]. In addition, it facilitates proactive decision-making whereby the businesses are able to quickly respond to changes and customer tastes in the market. A combination of ML in SCM does not only enhance the efficiency of the operations but enhances the competitive advantage, as customer satisfaction is improved, and cost of operations is reduced. This study will investigate what ML can be used to optimize SCM processes in the e-commerce environment and what methods, real-life uses, and quantifiable advantages are the most prominent topics in regard to this issue. Analysis of existing trends and case studies helps the study discover the insights on how solutions based on the four aspects of ML can convert Joe-average systems of traditional supply chains into smart, flexible, and super-efficient systems that would satisfy the growing needs presented by the digital marketplace.

In the past few years, applying the concept of artificial intelligence (AI) and machine learning (ML) to supply chain management (SCM) has received much interest, especially in the world of e-commerce. The application of AI in optimizing the design of packing was investigated by Hayder and Konathala [15], and it was demonstrated that there is a significant savings of costs and a better opportunity to manage any business. They focused on the role of AI-supported packaging solution in reducing the materials used but keeping the products safe: this dimension of the SCM optimization encompasses more than inventory and logistics management. When applied in a general framework of industry usage, the role of AI in the motor industry was considered transformational by Hossain et al. [16], along with the focus on the predictive analytics, the automated decision-making, and the interconnection with other digital technologies. The results they achieve demonstrate the significance of AI in improving the resiliency and efficiency of the operations within any supply chain that can be transformed into the e-commerce logistics. Equally, Huang [17] suggested using metaheuristic optimization algorithms, such as, the Ant Colony Optimization, algorithm, to ensure better supply chain network design and control of inventory. This research paper has shown that the hybrid optimization method can ensure that operational costs are lowered significantly, and responsiveness to the changing demand is enhanced. Computer vision and deep learning have likewise been used in the logistics processes. Huang [18] had identified a machine device that caused defects during packing, that was an automated system that relied on the two-dimensional flow model to detect the defects hence had an amplified consistency and quality control. This strategy captures the increasing trend in adoption of AI, in the operation of the warehouse, with a view to the improvement of operational precision. Similarly, Huayu et al. [19] evaluated the impact of reorganizing resources on supply chain resiliency during the digital age with a strong focus on designing strategy-based digital integration processes, which enhances supply chain flexibility and lowers susceptibility to disruption.

Due to the usage of the AI and Internet of Things (IoT) technologies, the supply chain has become transformable even further. Jerifa et al. [20] suggested a combo methodology of bibliometricity and topic modelling to access trends in the AI-IoT-enabled supply chains and reported to observe the advancement of decision-making, real-time monitoring, and prediction. There are also investigated platforms using blockchains, as the case proposed by Ju et al. [21], which aimed to streamline reserve decisions during adoption in relief supply chains through blockchain in the second-hand sale of products and exerted a better degree of transparency, traceability, and efficiency. AI-driven SCM benefits have also been used by small and medium enterprises (SMEs). As pointed out by Le et al. [22], AI improves business operations, such as procurement, inventory management and logistics planning allowing SMEs to act more competitively. Moreover, Lin and Karia [23] revised the effects of digitalization on manufacturing supply chains, and they effectively outline that the improved performance metrics through integrated digital solutions included lead times, inventory turnover, cost-effectiveness. There has been the utilization of advanced predictive models based on a combination of ML and deep learning in e-commerce SCM. Vakharia [24] and Liu proposed a hybrid BO-CNN-LSTM model to use in demand forecasting and inventory optimization and showed considerable improvements regarding the quality of the forecast and the control of stock. Strategies of SCM have also been developed based on a financial requirement; Mai et al. [25] have proposed data-driven solutions to maximize the working capital in e-commerce supply chain to incorporate a relationship between working efficiency and monetary performance. In the same vein, Md [26] also showed that the business performance regionally could be improved by using data analytics in logistic and SCM with the focus on the idea of sustainability and development by using the concept of intelligent supply chain decisions.

The paper examines how machine learning (ML) can be used in optimization of supply chain management (SCM) of e-commerce. The approach deals with the analysis of the historical e-commerce data, demand forecasting, inventory management, and optimization of logistics based on the advanced ML algorithms and the evaluation of the algorithms [4].

Data Collection and Description

The data to be used in this research is gathered on a mid-size e-commerce platform between two years (20232024). Data in the dataset comprises the details of the orders, product type, its sales volume, customer whereabouts, the times they are shipped and the stock level. It has 50,000 records of transactions, that embrace 500 products in 20 groups. Characteristics such as OrderID, ProductID, Category, Order Date, Quantity, Price, CustomerLocation, Delivery time and StockLevel are adopted. The preprocessing of the data included the treatment of missing data, the encoding of the categorical variables, normalization of the numerical data, and division of the dataset into testing (30% and training 70% parts [5].

Algorithms for SCM Optimization

1. Linear Regression (LR)

The demand is projected through the help of Linear Regression, which relies upon historical sales information. It presupposes that there is a linear relationship between independent factors (e.g., time, promotions, seasonality) and dependent one (demand). With the use of the least-squares-based method of coefficients fitting into a linear model, LR estimates line coefficients that produce a minimum error between the actual and predicted demand [6]. This assists in predicting future sales, efficient inventory, and in preventing overstock or situation of stockout. LR can also be used to predict demands of products whose sales trends are stable in e-commerce SCM.

Table 1: Sample Predicted Demand Using Linear Regression

Pseudocode: Linear Regression

2. Random Forest (RF)

Random Forest is a classification and regression algorithm algorithm employed in ensemble learning. It builds up several decision trees of randomly selected subsets of the dataset and then averages the prediction of the tree to enhance accuracy and decrease overfitting. RF can also forecast the demand of products, decide on products by their characteristics of sales availability, and perform cost-effective inventory replenishment in SCM [7]. It is best suited to complex environments of e-commerce with many influencing factors because of its capacity to deal with non-linear relationship and interactions between features.

Table 2: Sample Inventory Prediction Using Random Forest

3. Long Short-Term Memory (LSTM) Networks

LSTM is a form of recurrent neural network (RNN), which aims at understanding the long-term dependencies of sequential data. LSTM has greatly been used in SCM regarding time-series demand forecasting, since it takes into account the trend and the seasons as well as previous sale behavior. In contrast with classic RNNs LSTM can address the problem of vanishing gradients with memory cells that store information retrieved and updated selectively over time. LSTM foresights future demand more efficiently by computer learning through historic sequences of demand, contributes to active inventory control and minimizes operational expenses [8].

4. K-Means Clustering

An example of unsupervised learning algorithm K-Means is applied to divide the products or the customers into groups that are similar. It has proven useful in e-commerce SCM in around-the-world inventory allocation by product segments (e.g., demand patterns or customer buying behavior); it can also be used to distribute inventory in specific regions to optimize logistics in the region. The algorithm operates by repeated assignment of data points to clusters using a minimum of the Euclidean distance involving the points and the cluster centres and, in the process, adjusting the centres until convergence [9]. This strategy will also optimize placement in the warehouses and minimize the delivery time and increase the efficiency of the supply chain.

This paper undertook a set of experiments to determine the quality of machine learning algorithm in optimization of supply chain management (SCM) under an e-commerce situation. The experiments were written to attend to three main objectives based on these three issues: (1) better correct prediction of demand, (2) better inventory management, (3) better logistics and delivery. The four algorithms discussed in the Materials and Methods section, including a Linear Regression (LR), Random Forest (RF), Long Short-term Memory (LSTM) Networks, and K-Means Clustering were applied on historical e-commerce datasets comprising 50,000 transactions of 500 products of 20 different categories.

Figure 1: “Artificial Intelligence in Supply Chain”

1. Demand Forecasting

Demand forecasting plays an essential role in efficient SCM because incorrect forecasts may lead to excessive stocks or stockout that may cause a financial loss and dissatisfaction with customers. This paper employed the LR and LSTM in predicting the demand [10]. LR was used when the sales pattern of products was relatively the same, and LSTM was utilized when product sales have strong temporal dependence and seasonal variations.

Table 1: Comparison of Predicted Demand vs Actual Demand

It shows that LSTM has performed better in predicting products with variable demand patterns than LR as it showed less prediction errors in all the test products. Individually, the RMSE of 10 corresponds to the study presented by Raj et al. (2020) who found an RMSE of between 25 and 30 when demand forecasting was performed using the conventional methods of regression [11].

2. Inventory Optimization

Inventory control will be effective, so that there will be no surplus levels of stocks locally without spending in excess stores. Random Forest were used in predicting the optimal inventory levels and whether a replenishment was taken or not [12]. The model applied includes previous sales, seasonality, and category of products to be sorted into gangs of the so-called Replenish and Do Not Replenish.

Table 2: Inventory Replenishment Prediction

The overall inventory prediction accuracy of the Random Forest model was 94.25%. In comparison to the results of Puttagunta and Ravi (2021), who identified the average accuracy of 88% when decision tree models used, it is obvious that our multi-model model demonstrates the improvement of the level of accuracy and reliability, especially with products that have varying demand [13].

Figure 2: “Artificial Intelligence in Supply Chain”

3. Logistics and Delivery Optimization

The use of efficient logistics and delivery is the key to satisfying customers in e-commerce. The K-means clustering was used to cluster the customers and products by geography in which an optimum allocation of the warehouse could be achieved and finally the routes could be optimized [14]. Orders and addresses to customers were organized in 4 groups according to frequent purchases and distance of delivery.

Table 3: Cluster Analysis for Logistics Optimization

The clustering approach reduced average delivery distance by 15% compared to non-clustered allocation, which is consistent with similar findings in the literature (e.g., Liu et al., 2021) that reported 10–12% improvement using ML-based segmentation [27].

4. Comparative Performance Analysis of Algorithms

A comparative evaluation was conducted to assess the performance of the four algorithms across multiple SCM tasks, including prediction accuracy, RMSE, execution time, and overall operational benefit.

Table 4: Algorithm Performance Comparison

The results demonstrate that LSTM provides the highest accuracy in demand forecasting, while Random Forest excels in inventory management. K-Means clustering effectively optimizes logistics and delivery routes. Linear Regression, although simpler and faster, exhibits lower accuracy, particularly for products with variable demand [28].

Figure 3: “Applications of deep learning into supply chain management”

5. Comparative Analysis with Related Work

The proposed ML-based SCM framework outperformed traditional SCM models reported in the literature. For instance, Li et al. (2022) reported average forecasting errors of 7–9% using conventional regression and decision tree models, while our LSTM model achieved an error below 1% for high-demand products. Random Forest outperformed single decision tree models in inventory accuracy (94.25% vs 88%), and K-Means clustering reduced average delivery distance more efficiently than standard heuristics reported by Chan et al. (2020).

Table 5: Comparison with Related Work

The experimental results confirm the hypothesis that machine learning can significantly enhance SCM efficiency in e-commerce. LSSTM demand forecasting was found to be more accurate than the linear models especially when it comes to products whose brand cycle is complicated in nature. Random Forest came up with strong access to inventory analyses, which heightened the likelihood of avoiding an overstocking threat and stockings [29]. K-Means cluster allowed the optimization of warehouses location and shortening delivery routes to make customers happier. In comparison to previous research, it is clear that this research shows measurable improvement in areas that are important in SCM with the distribution of demand prediction error being decreased by 50 70, Logistics being improved by 15, and Inventory being improved by 6. Based on these findings, a set of ML algorithms, specific to the requirements of particular SCM, has the potential to develop an adaptive and intelligent supply chain which will meet the dynamic needs of the e-commerce processes. Additionally, a combination of these ML methods will allow making an overview of real-time decisions [30]. Indicatively, LSTM models could be used to continually update the demand predictions using the sales available and the Random Forest models to respond adjusting the inventory average. K-Means clustering provides the possibility of reorganizing the operations of the warehouse periodically depending on customer location and change of demand density. An amalgamation of these follows into reduced costs, better rates of order fulfillment and customer experience.

Figure 4: “Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks”

This study shows that machine learning (ML) is an effective and potent method of optimizing the supply chain management (SCM) throughout the e-commerce industry. This study revealed the solutions to some of the most important problems of SCM, such as forecasting the demand, managing inventories, and optimization of logistics by analyzing historical data of the transaction and proposing a combination of algorithms: Linear Regression, Random Forest, Long Short-Term Memory, and K-Means clustering. Through the experimental process, LSTM networks were found to be especially accurate when forecasting product demand, especially when the demand pattern of an item is seasonal or varying and ultimately caused a reduction in the error margin of the forecast of up to 70 percent compared to that of a traditional regression model. Random Forest was also very successful in terms of trying to predict inventory and was shown to have a high accuracy of 94.25 out of which it was able to make proactive decisions to replenish stocks before they went out of stock or overstocked the inventory. K-Means clustering supported efficient logistical planning because the clustering of customers and products ensured by the algorithm based on geographic and demand patterns enabled profitable logistics planning, which decreased average delivery distances by 15 percent as well as increasing in general efficiency in operations. The integrated ML-based framework compared to the earlier research [1526] showed large scale enhancement in the most crucial performance indicators of the SCM and including the forecasting precision, inventory control, and delivery efficiency. These results suggest that an intelligent, highly responsive, and data-driven, ML-enabled conceptualization can convert conventional supply chains into an intelligent network, changes in the market development and responsive to customer demand. As a whole, the current study is very effective in promoting the use of ML within e-commerce SCM that may improve the efficiency and sustainability of operations in addition to competitive advantage to foster the use of smart, sustainable, and robust supply chain strategies in the virtual market.

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