Machine Learning-Based Retail Supply Chain Management Using ERP and Sales Data Analytics
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P115Keywords:
Enterprise Resource Planning (ERP), Retail Supply Chain Management, Machine Learning, Demand Forecasting, Sales Data AnalyticsAbstract
Enterprise resource planning (ERP) solutions provide direct support for supply chain management and logistics operations, which are essential components of modern business operations that determine international market competitiveness. The research presents a machine learning (ML) approach to developing a retail supply chain management system that utilizes sales and ERP data for its operations. The team used Kaggle Superstore sales data to conduct exploratory data analysis and evaluate sales performance across regions and features. The model development process starts after completion of multiple preprocessing steps, which include handling missing values and outlier detection, categorical data encoding, data normalization and data partitioning. The proposed model performance evaluation uses Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R2 as performance metrics. The experimental results demonstrate that the DNN model can predict outcomes effectively, with an MAE of 2.277, an RMSE of 2.814, an MAPE of 13.72%, and an R2 of 92.0%. The DNN approach demonstrates higher forecasting accuracy than traditional Decision Tree and Random Forest models, according to the comparative analysis. The research results demonstrate how deep learning methods can enhance demand-forecasting accuracy and support decision-making in ERP-based retail supply chain management systems.
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