Model-Based Systems Engineering (MBSE) relies on precise, unambiguous requirements; however, stakeholders often express needs in free-form natural language, leading to ambiguity and rework. This paper introduces an AI assistant powered by a Large Language Model (LLM) to support the formalization of such requirements. Guided by writing rules from industry standards such as INCOSE and IREB, the assistant identifies ambiguities, classifies requirement types, and rewrites them into consistent, standards-compliant statements. The structured requirements are serialized into SysML v2 using the Kernel Modeling Language (KerML), enabling seamless integration into MBSE workflows. A lightweight Streamlit interface allows users to upload, review, and export refined requirements. The approach is demonstrated through a practical case study, where requirements from educators and engineers are processed using the AI assistant. This work illustrates how LLM can enhance the front end of MBSE by bridging the gap between informal stakeholder input and formalized system specifications.

Efficient engineering data logistics remains a critical challenge in Production Systems Engineering, where diverse data formats and heterogeneous toolchains hinder seamless data exchange across disciplines. Manual and error-prone data transfers increase engineering overhead and pose risks to data consistency. This paper addresses these challenges by proposing the Integrated Engineering Data Transformation (IEDT) framework, which consolidates heterogeneous discipline-specific engineering artifacts into a unified AutomationML (Automation Markup Language)-based model. The framework supports both automated data integration and round-trip export back into native formats, thereby enhancing interoperability and traceability. A supporting software tool, PPR-AML, has been developed to operationalize the framework, facilitating automated data transformation and integration. The framework and tool are validated using a representative use case involving a model production plant, demonstrating their feasibility and effectiveness for streamlining engineering data logistics across disciplines.

The common concept method for citizen modelers in cross-domain engineering addresses the challenge of semantic inconsistencies arising from heterogeneous domain models in production systems engineering by integrating model-driven engineering, citizen modeling and ontology-based knowledge representation into a unified workflow. The proposed approach introduces a lightweight domain-specific modeling language embedded within a Sparx Enterprise Architect MDG extension. This extension enables domain experts, referred to as citizen modelers, to intuitively capture concepts, properties and relations without requiring deep MBSE expertise. A four-phase process guides the creation of a consistent common vocabulary. An AutomationML export maintains semantic integrity and facilitates seamless data exchange with downstream tools. The method was validated by engineering students acting as citizen modelers and in an industrial project. This paper uses a generic conveyor belt example to demonstrate the method. The study indicates that the CC method facilitates efficient, collaborative modeling, semantic alignment and enhanced interoperability across heterogeneous engineering domains.

Companies are currently concerned with the introduction of the digital product passport as one element in their digitalization strategies. But up to now there is only limited information on the amount of information and their structuring to be expected in a digital product passport. Within this paper a structure and an idea for an information collection methodology are proposed based on the consideration of product life cycle related use case analysis. Thereby practitioners shall be supported in carefully reviewing their own relevant application cases and their digitalization strategies.

In the contemporary manufacturing sector, efficiency and precision are paramount but often hindered by manual and error-prone transfers of data across disparate engineering disciplines. This paper addresses the critical challenges posed by the diversity of data formats and the high reliance on manual processes in engineering data logistics. We propose a novel strategy leveraging Automation Markup Language (AutomationML) to foster a more efficient and error reduced data exchange framework. AutomationML is utilized for its robust representation of plant engineering information, promoting seamless interaction among various engineering tools and systems. By transitioning from existing pipeline-based data logistics methodology to a centralized data storage approach, our method markedly diminishes the likelihood of information loss and reduces integration complexity. This research not only maps out the prevailing issues with current data logistics practices but also introduces a scalable, adaptable tool designed to optimize data integration and transform the engineering workflow. By addressing these potential advancements, the approach sets a path towards a more efficient and effective engineering data logistics paradigm.

In production systems engineering, effective conceptualization is paramount for devising efficient and optimized systems as the majority of project costs are being fixed in this design phase. The Product-Process-Resource (PPR) modeling approach has the potential to enhance this conceptualization by integrating with established Model-Based-Systems-Engineering (MBSE) frameworks. To ensure that interaction, the role of PPR modeling in facilitating early-stage design decisions and fostering a systems-thinking approach throughout the design process has to be investigated. This paper demonstrates the efficacy of PPR modeling in improving the conceptualization phase of production system design, followed by its integration into MBSE frameworks. An industrial case study illustrates how such an approach enables engineers to efficiently visualize and analyze the complex interactions between product components, manufacturing processes, and resource allocations, thereby creating more informed design decisions. Furthermore, the paper discusses challenges and opportunities associated with integrating PPR modeling within MBSE frameworks and proposes future research directions.

This paper discusses the challenges and associated risks that companies face due to the growing complexity of systems engineering. It highlights the significance of effective and efficient decision making during the early design phase, known as basic engineering, to mitigate potential risks. The paper recommends the representation of basic engineering data and associated risk models in the AutomationML data exchange format. This tool-neutral approach supports data-driven decisions in the design of production systems and enables continuous data expansion throughout the production system's lifecycle. The implementation of this approach and its potential impact on improving efficiency and risk management in systems engineering are also discussed.

As developing current or future industrial systems becomes increasingly complex, new methods for understandably structuring those systems need to arise. The Reference Architectural Model Industrie 4.0 (RAMI 4.0) provides a framework for describing intermingled industrial applications in the context of Cyber-Physical Production Systems (CPPSs). However, although RAMI 4.0 has been standardized for almost a decade, it still lacks actual industrial applications. Recent use cases have shown that the three-dimensional layout might be oversimplified for describing system architectures. Specifically, problems with the so-called live-cycle axis, which has shown significant shortcomings in capturing the dynamics of industrial processes, need to be tackled. This paper provides an analysis of RAMI 4.0 and an assessment of shortcomings that hinder its applicability. It presents alternative approaches that address these limitations by proposing modifications or replacements of parts of RAMI 4.0. Based on the paper's insights, we provide the research agenda for engineering future manufacturing systems, considering it a base for follow-up projects to ensure industrial readiness.

Failure Modes and Effect Analysis (FMEA) forms the backbone of proactive risk analysis in manufacturing. However, the reuse of FMEA models across varying contexts has remained an intricate, often labor-intensive process, primarily relying on expert knowledge. This paper introduces the Risk Model Reuse (RMR) approach, designed to enhance the efficiency and accuracy of FMEA model reuse. RMR is premised on Case-Based Reasoning (CBR) principles, supported by a robust ontology for Products, Processes, and Resources (PPR), to enable reuse of identified risks. An initial validation of the approach is presented through two distinct industrial welding use cases. Here, the approach facilitated the extraction and reuse of relevant risks from one welding case to another, enabling a nuanced consideration of case-specific risks while ensuring knowledge accuracy and context-appropriateness. Despite its promising application, the RMR approach acknowledges areas of future refinement, such as broadening its applicability across diverse joining applications, alignment with industry standards and advanced tool support for enhanced automation. By addressing these potential advancements, the approach sets a path towards a more efficient and effective risk analysis paradigm.

Production systems are becoming increasingly complex, as they have to be used to manufacture individualized products with the highest quality. In addition, rapidly changing customer requirements mean production systems are adapted at shorter intervals or designed to be highly flexible right from the start. With increased complexity, the risk of failures in production also rises. Since most failures are associated with high costs, the goal is to avoid them as efficiently and effectively as possible. Hazards identified in an early phase of the design process can be treated directly, and thus time and costs are reduced. For this reason, this paper aims to enable the evaluation of production systems with the Failure Mode & Effects Analysis (FMEA) during the basic design phase of the engineering life-cycle. To do so, the proposed model-based system quality analysis for basic engineering (SQA4BE) approach is implemented in a state-of-the-art software tool targeting model-based systems engineering (MBSE) of such systems, the so-called Reference Architecture Model Industrie 4.0 (RAMI 4.0) Toolbox. The resulting implementation of the approach is evaluated towards practicability by utilizing the example case study of a Fischertechnik screwing machine.

Industry 4.0 use case on products along the production lifecycle require an extensible representation of PPR dependencies, which are distributed on PLM, CRM, and production system assets. This paper introduces the PPRplus modeling method to facilitate the digital representation of a product along the product lifecycle, including PPR dependencies, and knowledge about performance indicators. We initially evaluated the feasibility and effectiveness of the PPRplus modeling method with two use cases on work lines for metal processing.

Industrial manufacturing is becoming increasingly complex due to the need to produce individual products with dynamic production processes. This means new methods for engineering flexible production systems, the interconnection between production system, process, and product, need to occur, as contemporary methods are reaching their limits. While varying approaches try to address this problem on a small-scale, the need for holistic methodologies becomes obvious. Thus, this paper proposes a holistic solution by aligning the concepts of the Product, Process, and Resource (PPR) notation for engineering industrial systems according to the Reference Architecture Model Industrie 4.0 (RAMI 4.0). By doing so, each other’s disadvantages are counteracted. In more detail, RAMI 4.0 is missing a concrete modeling notation allowing it to be practically applied in systems engineering. At the same time, PPR reaches its limits regarding large-scale modeling systems across multiple domains or granularity levels. The result of this work, an applicable PPR modeling notation for engineering complex production systems, could strongly contribute to future Model-based Systems Engineering (MBSE) within this area, as a ready-to-use methodology the so-called RAMI Toolbox provides them. Using the agile design science research methodology (ADSRM), the outcome is evaluated based on a real-world case study.