Forecasting models of demand in supply chain with high product diversification using gradient boosting machine learning methods | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از 08 تیر 1405 اصل مقاله (394.31 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.34189.5103 | ||
| نویسندگان | ||
| Mohammad Reza Fahimi1؛ Ali Rajabzadeh Ghatari* 2؛ Maryam Shoar1؛ Maryam Khademi3 | ||
| 1Department of Industrial Management, Islamic Azad University, North Tehran Branch, Tehran, Iran | ||
| 2Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran | ||
| 3Department of Applied Mathematics, Islamic Azad University, South Tehran Branch, Tehran, Iran | ||
| چکیده | ||
| Forecasting demand in the supply chain is an essential and challenging issue for determining effective strategies and informed decisions. The related challenges also increase by raising the variety and number of products. Providing frameworks and methods with the flexibility, accuracy, and advantages necessary to forecast all product categories is crucial for managers, despite product diversity, differences in applications and features, and varying data volumes. In this regard, two supervised learning models, XGBoost Regressor (XGBR) and Gradient Boosting Regressor (GBR), have been implemented on the Global Superstore dataset on the Kaggle site. This dataset contains 3788 products in three diverse product categories, seventeen subcategories, and 51,290 orders. The limited data volume of the products prevented the possible and helpful forecasting of many products and the obtaining of appropriate results from the methods. In this experimental research, demand forecasting is used in strategic decisions. Therefore, a product aggregation approach has been proposed for this problem, which can be predicted separately according to the similarity in the subcategory products. The data of the dataset was increased using the Data Augmentation technique to investigate the effect of the amount of data on the performance of the models, and the forecasting results of the two models were compared by re-running the models. Based on the forecasting results with increased data with MSE and MAE metrics, the XGBR model achieved the lowest values of 0.12 and 0.10, respectively, and the GBR model achieved values of 0.13 and 0.15. Also, the result of the $D^{2}$ Score Metric was 0.97 in the XGBR model and 0.96 in the GBR model. The values of the error metrics decreased dramatically and by more than 80% as the data increased, and XGBR had a relative advantage in the larger data. The proposed framework and models can be used in industries with similar issues at the strategy level. | ||
| کلیدواژهها | ||
| demand forecasting؛ high product diversification؛ supply chain؛ machine learning؛ gradient boosting | ||
| مراجع | ||
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