Citation:

Anirudh Parupalli, Honie Kali, 2024. "Leveraging ML for Business Forecasting in ERP-Enabled E-commerce Environments", ESP Journal of Engineering & Technology Advancements  4(3): 189-199.

Abstract:

E-commerce platforms depend more and more on enterprise resource planning (ERP) tools to enhance decision-making and expedite processes. Accurate business forecasting in such ERP-enabled environments is critical for demand prediction and optimized inventory management. This paper presents a robust machine learning framework for business forecasting using the Brazilian Olist e-commerce dataset. The methodology incorporates comprehensive data preprocessing merging relational tables, removing irrelevant attributes, handling missing values, encoding categorical variables, min–max normalization, and temporal feature extraction related to delivery. Domain-specific feature engineering generates delivery accuracy metrics, product rating statistics. Class imbalance is addressed via SMOTE, and a stratified split is used for training and testing. Among four evaluated models Cat Boost, Convolutional neural network (CNN), Decision Tree, and K-Nearest Neighbors the Cat Boost classifier achieved superior accuracy of 97.62%, outperforming CNN (91.7%), Decision Tree (87.2%), and K-Nearest Neighbors (75%). These results demonstrate Cat Boost's strength in modelling heterogeneous data and complex feature interactions, confirming its scalability and practical effectiveness for accurate forecasting in dynamic ERP-integrated e-commerce platforms.

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Keywords:

E-commerce, Enterprise Resource Planning (ERP), Business Forecasting, Cat Boost, Machine Learning.