The increasing decentralization of industrial processes in Industry 4.0 necessitates the distribution and coordination of resources such as machines, materials, expertise, and knowledge across organizations in a value chain. To facilitate effective operations in such distributed environments, it is essential to digitize processes and resources, establish interconnectedness, and implement a scalable management approach. The present paper addresses these challenges through the knowledge-based production planning (KPP) system, which was originally developed as a monolithic prototype. It is argued that the KPP-System must evolve towards a service-oriented architecture (SOA) in order to align with distributed and interoperable Industry 4.0 requirements. The paper provides a comprehensive overview of the motivation and background of KPP, identifies the key research questions that are to be addressed, and presents a conceptual design for transitioning KPP into an SOA. The approach under discussion is notable for its consideration of compatibility with the Arrowhead Framework (AF), a consideration that is intended to ensure interoperability with smart production environments. The contribution of this work is the first architectural concept that demonstrates how KPP components can be encapsulated as services and integrated into local cloud environments, thus laying the foundation for adaptive, ontology-based process planning in distributed manufacturing. In addition to the conceptual architecture, the first implementation phase has been conducted to validate the proposed approach. This includes the realization and evaluation of the mediator-based service layer, which operationalizes the transformation of planning data into semantic function blocks (FBs) and enables the interaction of distributed services within the envisioned SO-KPP architecture. The implementation demonstrates the feasibility of the service-oriented transformation and provides a functional proof of concept for ontology-based integration in future adaptive production planning systems.
Gorgs et al. (Thu,) studied this question.