The process industry faces significant challenges in achieving seamless data interoperability and effective stakeholder communication due to heterogeneous data sources and formats. Traditional data management methods often lead to inefficiencies, errors, and miscommunication. This paper explores the transformative potential of digital transformation, emphasizing the role of ontologies and semantic modeling. By leveraging standardized frameworks like the Industrial Data Ontology (IDO, ISO 23726-3), we demonstrate how semantic models can enhance data interoperability and stakeholder communication. Using a pump station as a case study, we illustrate the practical application of ontologies, showcasing the benefits of reasoning engines, axioms, and querying capabilities. Our findings highlight the importance of adopting semantic models and ontologies to improve operational efficiency, data consistency, and collaboration within the process industry. This research contributes to the field by providing a detailed example of ontology implementation, bridging the gap between theoretical benefits and practical applications.

This paper addresses two critical challenges in industrial processes: enhancing communication between customers and suppliers and ensuring product traceability through semantic modeling. By semantically structuring customer requirements and supplier specifications, we ensure completeness and clarity on both sides, minimizing misunderstandings and gaps in information exchange. Once this semantic alignment is achieved, the product itself can be semantically modeled, enabling the creation of a Digital Product Passport (DPP) for product traceability. Using the Interplanetary File System (IPFS) for decentralized, independent storage, this solution offers data accessibility without requiring stakeholders to modify their existing infrastructure. The proposed approach demonstrates a pathway to digital transformation, improving transparency and operational efficiency without the need for substantial system overhauls.