Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP: KR Ahmed et al.

Authors : Khandakar Rabbi Ahmed, Md Eahia Ansari, Md Naimul Ahsan, Arafat Rohan, Md Borhan Uddin, Mir Araf Hossain Rivin

Publication date : 2025/7/20

Journal : Scientific Reports

Volume : 15

Issue : 1

Pages : 26355

Publisher : Nature Publishing Group UK

Description : Contemporary supply chain networks in the context of the era of Industry 4.0 are becoming more erratic and complex, and have an influx of structured and unstructured data. Conventional practices of supply chain management (SCM) cannot overcome real-time uncertainties, and it is time to orient the SCM toward AI-guided predictive modeling. This research contains a suggestion of a deep learning (DL) framework that combines Self-Organizing Maps (SOMs), Principal Component Analysis (PCA), and Artificial Neural Networks (ANNs) to predict more accurately the supply chain shipping timing and delivery risk. Applying the DataCo Smart Supply Chain dataset, the offered SOM+ANN model proved much more accurate than conventional Machine Learning (ML) procedures, e.g., Random Forest (RF), XGBoost, or Decision Tree (DT), to address the tasks of predicting the shipping time and categorizing the risk of late …

Total citations : Cited by 78