Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP

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 delivery. The R² was found to be 0.92, Root Mean Squared Error (RMSE) 0.936, and Mean Absolute Error (MAE) 0.8459 of the SOM+ANN model estimated the shipping duration. In the classification, the accuracy was 96% and the F1-score was 96.22%. Furthermore, the research also employed another dataset, making it more accurate, better generalized, and robust. The proposed model achieved 89.65% accuracy in dataset 2. The model's outcomes are also interpretable and assisted by SHAP (Shapley Additive exPlanations). The interpretability methods enabled end users to comprehend how the model makes classification decisions. The proposed framework promotes SCM operations, resilience, and decision-making by incorporating transparent AI methodologies.