(untitled) (Statistics, Optimization & Information Computing, 2025)
Achieving a seamless transition to Industry 4.0 requires a holistic, knowledge-driven approach that integrates multiple dimensions of digital transformation. This paper proposes a smart, data-driven ontology-based system that integrates strategic, operational, technological, and cultural dimensions for Industry 4.0 maturity assessment. Built using OWL (Ontology Web Language) for structured knowledge representation and SWRL rules (Semantic Web Rule Language) for intelligent inference, the proposed ontology-based system assesses manufacturing enterprises into five maturity levels: Pre-Adoption, Experimental, Transitional, Integrated, and Transformational. It leverages technical KPIs from SCADA, ERP, IoT, and the industrial real-time data sources to enable automated reasoning and data-driven decision-making. An industrial case study in an automotive manufacturing plant is developed to validate the proposed ontology-based system potentialities and effectiveness in optimizing the industry 4.0 maturity assessement process, maturity levels aggregations and effective insights generation. The results highlight its adaptability across industries, offering a scalable and intelligent solution for Industry 4.0 assessment and adoption. It highlight also its potential to ensure domain-specific digital transformation benchmarking and previous maturity models interoperability.