Purpose: This paper aims to provide a service-based integrated prototype framework for the design of reusable modular assembly systems (RMAS) incorporating reusability of equipment into the process. It extends AutomationML (AML) developments for an engineering data exchange to integrate and standardize the data formats that support the design of RMAS. Design/methodology/approach: The approach provides a set of systematic procedures and support tools for the design of RMAS. This includes enhanced domain knowledge models that facilitate the interpretation and integration of information across the design phases. Findings: The inclusion of reusability aspects in the design phase improves the sustainability of future assembly systems, by ensuring equipment use until its end-of-life. Moreover, the integrated support tools reduce the design time, while improving the quality/performance of the system design solution, as it enables the exploration of a larger solution space. This will result in a better response to dynamic and rapidly changing system requirements. Social implications: This work provides a sustainable approach for the design of modular assembly systems (MAS), which will ensure better resource utilization. Additionally, the standardization of the data and the support of low cost tools is expected to benefit industrial companies, particularly the small- and medium-sized enterprises. Originality/value: This approach offers a service-based platform which uses production data to incorporate reusability aspects into the design process of modular assembly system. Moreover, it provides a framework for modular assembly system design by extending the current design processes and interactions between stakeholders. To support this, a standardized method for information representation and exchange across the several phases of the RMAS design activity is briefly illustrated with an industrial case study.

The development of interoperable and reusable information models is a key challenge for digitalization in manufacturing domains with heterogeneous and complex process chains. Ensuring seamless data exchange requires the standardization of both data syntax and semantics, while maintaining compatibility with existing industry standards. This paper presents a methodology for deriving standardizable and generalizable OPC UA information models tailored to domains with high process variability and interdisciplinary requirements. The methodology integrates system analysis, parameter mapping, and the development of modular submodels, supported by expert input and validation. It emphasizes the reuse and extension of existing OPC UA Companion Specifications to reduce complexity, avoid redundancy, and enable long-term standardization. The approach is exemplified by its application to battery cell production, an emerging manufacturing domain combining process and mechanical engineering with continuous and discrete processes. Its high degree of heterogeneity and lack of domain-specific standards pose significant challenges for model development. Through iterative expert workshops and structured model validation, a dedicated and transferable OPC UA framework is created. The resulting layered model structure combines a cross-industry standard with newly developed, process-aware model elements. This enables both broad applicability and the depth required for complex production environments, while supporting use cases such as traceability, regulatory reporting (e.g., EU Battery Passport), and process optimization. The resulting model improves interoperability, transparency, and data integration, offering a scalable blueprint for other complex manufacturing sectors.