Impact of Explainable Hybrid AI on Industry 4.0 Decision Systems: Operational Efficiency, Transparency, and Real‑World Deployment

The software package accompanying the paper An Explainable Hybrid AI Architecture for Operations Research in Industry 4.0: Deep Learning and Interpretable Predictive Modelling for Intelligent Decision Support provides a complete, end‑to‑end implementation of the EDNN–LR hybrid learning framework. It includes all modules required for data acquisition, preprocessing, feature engineering, model training, optimisation, evaluation, and reproducibility. The package integrates deep‑learning components with interpretable predictive modelling tools, enabling researchers and practitioners to replicate the full analytical workflow used in the study and adapt it to real‑world Industry 4.0 decision‑support environments. All mathematical formulations, algorithmic structures, optimisation routines, software development, data‑engineering pipelines, and model‑execution procedures contained in this package were solely designed, implemented, and validated by the corresponding author, Mohammad Heydari. The repository provides transparent access to the full computational architecture, ensuring that every component of the EDNN–LR system—from theoretical foundations to empirical deployment—can be independently verified, reproduced, and extended.