Abstract
This work addresses the limited transparency and real-time adaptability of traditional value stream mapping (VSM) in smart manufacturing. We developed an XAI-enabled dynamic VSM framework that integrates IIoT data, time-stamped process mapping, XGBoost-based waste classification, SHAP explanations, and multi-objective optimization. Using 12 weeks of MES, PLC, sensor, and quality data from a 14-station electromechanical assembly line, the framework identified 38.2% baseline non-value-added (NVA) time, achieved 89.4% classification accuracy, and reduced cycle time by 22.3%, WIP by 31.7%, and increased throughput by 18.9%. Process variability (CV) dropped from 0.34 to 0.19. Before deployment, waste root causes were hidden and decisions reactive; after deployment, real-time heatmaps and SHAP explanations enabled proactive, interpretable interventions. These results demonstrate that combining dynamic VSM with XAI and optimization improves operational performance, operator trust, and continuous-improvement decision-making in Lean 4.0 environments.