A Multimodal Artificial Intelligence and Data Analytics Framework for Intelligent Supply Chain Decision-Making: An Explainable Deep Learning Approach

The increasing complexity of global supply chain operations has created an urgent need for intelligent decision-support systems capable of processing heterogeneous operational data and delivering accurate, real-time insights. Conventional supply chain management approaches often rely on single-source structured data and traditional analytical techniques, limiting their ability to capture the dynamic relationships among procurement, inventory, transportation, warehousing, supplier performance, and customer demand. To address these challenges, this study proposes a comprehensive Multimodal Artificial Intelligence (AI) and Data Analytics framework for intelligent supply chain decision-making. The proposed framework integrates numerical, categorical, temporal, and engineered business features extracted from an open-source supply chain dataset obtained from the Kaggle repository. A systematic methodology involving data preprocessing, feature extraction, feature engineering, model development, and model evaluation was implemented to develop robust predictive models. Eight machine learning and deep learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, XGBoost, LightGBM, CatBoost, and a proposed Multimodal Deep Neural Network (MDNN), were trained and evaluated using identical experimental settings. The experimental results demonstrate that the proposed MDNN significantly outperformed all benchmark models, achieving an accuracy of 98.43%, precision of 98.5%, recall of 98.3%, F1-score of 98.4%, AUC-ROC of 0.995, and Matthews Correlation Coefficient (MCC) of 0.968, indicating exceptional predictive capability and generalization performance. Furthermore, Explainable Artificial Intelligence (XAI) techniques, including SHAP and LIME, were incorporated to enhance model interpretability and support transparent managerial decision-making. The proposed framework provides a scalable and industry-ready solution that can be integrated with Enterprise Resource Planning (ERP), Internet of Things (IoT), cloud computing, and real-time analytics platforms to improve supply chain resilience, operational efficiency, inventory optimization, logistics planning, and risk management. The findings demonstrate that multimodal AI substantially enhances intelligent supply chain decision-making compared with conventional machine learning approaches and provides a practical pathway toward the realization of Industry 4.0 and Supply Chain 5.0.

Multimodal Artificial Intelligence, Supply Chain Management, Data Analytics, Intelligent Decision-Making, Deep Learning, Machine Learning, Feature Engineering, Explainable Artificial Intelligence (XAI), Predictive Analytics, Industry 4.0, Supply Chain 5.0.

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