Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning
In today’s highly competitive and volatile environment, supply chains in emerging economies face ongoing challenges related to inventory management, demand forecasting, and distribution efficiency. This research proposes a predictive approach based on machine learning, specifically using ensemble st...
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| Formato: | Preprint |
| Lenguaje: | inglés |
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2025
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| _version_ | 1881700490190782464 |
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| author | Christian Ovalle Joel Martin Visurraga Agüero Frans Ramiro Cárdenas Palomino Waldyr Fredy Cerrón-Valverde Daniel Ramón Chirinos Armas Lenin Froy Arce-Huamán Omar Santillan Aching |
| author_browse | Christian Ovalle Daniel Ramón Chirinos Armas Frans Ramiro Cárdenas Palomino Joel Martin Visurraga Agüero Lenin Froy Arce-Huamán Omar Santillan Aching Waldyr Fredy Cerrón-Valverde |
| author_facet | Christian Ovalle Joel Martin Visurraga Agüero Frans Ramiro Cárdenas Palomino Waldyr Fredy Cerrón-Valverde Daniel Ramón Chirinos Armas Lenin Froy Arce-Huamán Omar Santillan Aching |
| author_sort | Christian Ovalle |
| collection | Producción científica |
| description | In today’s highly competitive and volatile environment, supply chains in emerging economies face ongoing challenges related to inventory management, demand forecasting, and distribution efficiency. This research proposes a predictive approach based on machine learning, specifically using ensemble stacking techniques, to optimize key logistics processes. Real-world data from a commercial company was used to develop a predictive framework that integrates various base algorithms Random Forest, CatBoost, XGBoost, Gradient Boosting, Decision Trees, and K-Nearest Neighbors combined through a Linear Regression meta-model. Performance evaluation using metrics such as MSE, RMSE, MAE, and R² revealed significant improvements in predictive accuracy compared to individual models, particularly in indicators such as material demand, purchase profitability, sales revenue, and inventory levels. The findings confirm that stacked models not only enhance forecasting capabilities but also offer a scalable, adaptable, and cost-effective solution to support logistics decision-making in resource-constrained contexts. This approach presents a strong alternative for boosting operational efficiency in supply chains across developing regions. |
| format | Preprint |
| id | openalex:W4413299999 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | openalex:W4413299999Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine LearningChristian OvalleJoel Martin Visurraga AgüeroFrans Ramiro Cárdenas PalominoWaldyr Fredy Cerrón-ValverdeDaniel Ramón Chirinos ArmasLenin Froy Arce-HuamánOmar Santillan Achingsupply chain optimizationstacking ensembledemand forecastinginventory managementlogistics decision-makingemerging marketsrandom forestCatBoostXGBoostgradient boostingk-nearest neighborsForecasting Techniques and ApplicationsSupply Chain and Inventory ManagementImpact of AI and Big Data on Business and SocietyIn today’s highly competitive and volatile environment, supply chains in emerging economies face ongoing challenges related to inventory management, demand forecasting, and distribution efficiency. This research proposes a predictive approach based on machine learning, specifically using ensemble stacking techniques, to optimize key logistics processes. Real-world data from a commercial company was used to develop a predictive framework that integrates various base algorithms Random Forest, CatBoost, XGBoost, Gradient Boosting, Decision Trees, and K-Nearest Neighbors combined through a Linear Regression meta-model. Performance evaluation using metrics such as MSE, RMSE, MAE, and R² revealed significant improvements in predictive accuracy compared to individual models, particularly in indicators such as material demand, purchase profitability, sales revenue, and inventory levels. The findings confirm that stacked models not only enhance forecasting capabilities but also offer a scalable, adaptable, and cost-effective solution to support logistics decision-making in resource-constrained contexts. This approach presents a strong alternative for boosting operational efficiency in supply chains across developing regions.Preprinthttps://doi.org/10.20944/preprints202508.1155.v1https://www.preprints.org/frontend/manuscript/6768e0cd3379ea23e99afd72b47d4ecb/download_pubW4413299999enPreprints.org2025-08-15 |
| spellingShingle | supply chain optimization stacking ensemble demand forecasting inventory management logistics decision-making emerging markets random forest CatBoost XGBoost gradient boosting k-nearest neighbors Forecasting Techniques and Applications Supply Chain and Inventory Management Impact of AI and Big Data on Business and Society Christian Ovalle Joel Martin Visurraga Agüero Frans Ramiro Cárdenas Palomino Waldyr Fredy Cerrón-Valverde Daniel Ramón Chirinos Armas Lenin Froy Arce-Huamán Omar Santillan Aching Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning |
| title | Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning |
| title_full | Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning |
| title_fullStr | Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning |
| title_full_unstemmed | Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning |
| title_short | Intelligent Supply Chain Optimization in Emerging Markets Using Ensemble Machine Learning |
| title_sort | intelligent supply chain optimization in emerging markets using ensemble machine learning |
| topic | supply chain optimization stacking ensemble demand forecasting inventory management logistics decision-making emerging markets random forest CatBoost XGBoost gradient boosting k-nearest neighbors Forecasting Techniques and Applications Supply Chain and Inventory Management Impact of AI and Big Data on Business and Society |
| topic_facet | supply chain optimization stacking ensemble demand forecasting inventory management logistics decision-making emerging markets random forest CatBoost XGBoost gradient boosting k-nearest neighbors Forecasting Techniques and Applications Supply Chain and Inventory Management Impact of AI and Big Data on Business and Society |
| url | https://doi.org/10.20944/preprints202508.1155.v1 https://www.preprints.org/frontend/manuscript/6768e0cd3379ea23e99afd72b47d4ecb/download_pub |
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