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.