The evolution of Industry 4.0 standards and trends introduces new challenges for traditional Manufacturing Execution Systems, necessitating their transformation into modular, interconnected components with standardized communication interfaces. Although these trends provide a conceptual framework, a distributed architecture for monitoring and aggregating production data remains underdeveloped. This paper addresses this gap by proposing a system of distributed monitoring components that interact hierarchically and heterarchically using Industry 4.0 technologies. The approach integrates Asset Administration Shells for passive data representation with Multi-Agent Systems and investigates its applicability for different control system architectures in different case studies.

Flexibility, agility and resilience of value chains are becoming increasingly important. One possible approach to achieving these goals could be the paradigm of cloud manufacturing, especially the shared production scenario, where a federation of trusted partners dynamically share manufacturing capabilities, capacity, and services over digital interfaces throughout the product lifecycle. This includes an automated bidding procedure in a cross-enterprise network. The Capability, Service, Skill (CSS) model provides a common terminology for Industry 4.0 applications. The paper focuses on enabling a shared production based on the CSS model through the implementation of the Asset Administration Shell (AAS), the use of the Industry 4.0 Language (I4.0L) and Gaia-X. It shows what a centralized approach to shared production using these technologies could look like. The scenario of a negotiation process to find suitable manufacturers for a customized product is described, and the different concepts are assigned to the elaborated steps of the I4.0L bidding procedure. In addition, the required AAS and its Submodels are designed for each step of the bidding procedure. Based on the results, the necessary standardization needs are outlined.

To achieve flexible, resilient and sustainable production, the generation of dynamic cross-company supply chains gains importance. The Shared Production scenario aims at sharing manufacturing services within distributed production network through digital interfaces. A prerequisite is secure data sharing in a trustworthy network with certified participants and a common understanding concerning vocahulary, message structure and interaction patterns Gaia-X provides a solution for a secure and federated data infrastructure according to European values (ie., European Data Protection, transparency and trust). Asset Administration Shells (AAS) provide semantic and vendor-independent information models and VDI/VDE 2193 proposes message structure and interaction protocols for bidding procedures. In this paper, we present the combination of the three concepts to enable the Shared Production scenario. We elaborate on the requirements of Shared Production, develop a system architecture and model AAS Submodel templates to perform an automated bidding procedure. Additionally, the implementation is evaluated based on the bidding of production services to produce model trucks in a demonstrator ecosystem.

To improve the fault tolerance of production systems against unforeseen events, modern production systems must be able to react more autonomously with suitable solutions to reach their goals. Such events within the company, e.g., resource breakdowns, require the possibility of internal replanning of the production schedule. Multi-Agent Systems (MAS) can provide this required flexibility. To enable standardized interaction for agents, a vendor-independent description of products and production equipment, as well as their capabilities and skills, must be developed. The Asset Administration Shell (AAS) provides the standardized meta-model to achieve this goal. In this paper, a product AAS and the submodel for the required production plan are designed. Moreover, the associated MAS and interaction for the execution and replanning of the production sequence are presented. The system is validated using an assembly use-case in the real-world demonstrator environment of the SmartFactoryKL. Combining the concepts of MAS with the standardized structures of the AAS, skills and capabilities tackles the problem of MAS to be deployed in different companies. This approach enables widespread use and scalability of MAS leveraging their advantages in terms of flexibility and efficient reconfiguration.