Explainable Graph Transformer Networks for Intelligent Enterprise Analytics and Autonomous Business Process Optimization

Abstract

Modern enterprises generate massive volumes of heterogeneous and interconnected data from Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), Internet of Things (IoT), financial systems, and external business sources. Traditional machine learning approaches often fail to capture complex relational dependencies and provide limited explainability for enterprise decision-making. This paper proposes an Explainable Graph Transformer Network (XGTN) for intelligent enterprise analytics and autonomous business process optimization. The proposed framework integrates Enterprise Knowledge Graphs with Graph Transformer Networks to model local and global dependencies using multihead self-attention. An explainability module combining attention visualization, SHAP, GNNExplainer, and counterfactual reasoning enhances transparency and trust in AIdriven predictions. Furthermore, an autonomous optimization engine supports workflow optimization, anomaly detection, resource allocation, and strategic decision-making. The framework comprises enterprise data integration, knowledge graph construction, graph transformer representation learning, explainability, autonomous optimization, and continuous feedback learning. Experimental results on benchmark enterprise datasets demonstrate that XGTN outperforms existing graph learning models in predictive accuracy, explainability, and scalability, providing a trustworthy and efficient solution for next-generation intelligent enterprise management systems.