In Cyber-Physical Production System (CPPS) engineering, multi-domain teams work concurrently and iteratively on assets to design and construct components of a CPPS. Prior research has demonstrated that these engineering assets can be visualized efficiently within a Multi-Domain Engineering Graph (MDEG). However, due to evolving requirements and conditions, the engineering assets for these system components undergo continuous adaptation, testing, and validation. Thus, establishing agile CPPS engineering is crucial, as it allows for iterative development and rapid response to changes, ensuring the system remains robust and adaptable. This paper investigates how changes can be visualized using a graph-based approach to support engineers during the change management process in the multi-domain CPPS engineering lifecycle. Therefore, we identify and describe common changes in an MDEG in engineering and sketch a method for change visualization within multi-domain change management. Finally, we provide a research agenda that outlines directions to implement such an approach.

To develop Cyber-Physical Production Systems (CPPSs), engineers from various domains work together from their respective work environments. However, exchanging arti-facts between these domain-specific views is hard, if the representations of similar concepts differ. This paper introduces the Multi-Domain Modeling for CPPS (MDM-CPPS) method to facilitate (i) defining view-specific local concepts; (ii) negotiating a common concept view for collaborating domains and their dependencies; and (iii) for reviewing the impact of local property changes on related domain views, based on established GitOps practices and on semantic analysis. We evaluated MDM-CPPS with a tool chain prototype in a feasibility study on an industrial use case. The study results indicate MDM-CPPS to provide (i) effective means for common system understanding, (ii) efficient change coordination, and (iii) effective CPPS asset management.

In Cyber-Physical Production System (CPPS) engineering, multi-domain teams work concurrently and iteratively in heterogeneous environments on a variety of assets to design and build a CPPS. While multi-domain change management bears a range of practical challenges, There is very little survey reporting on the state of the industry. This paper reports on the results of an expert survey in the field of multi-domain change impact analysis and management, including the perceived usefulness and applicability of a multi-view change management workflow. The results of the survey indicate a need for a multi-domain change impact analysis process with corresponding tool support. From the survey analysis insights, this paper derives candidate objectives for a research roadmap.

As digitalization shapes Industry 4.0, integrating software engineering practices into the manufacturing domain becomes a key focus. DevOps is such a practice and methodology that enhances collaboration and automates processes in software development and IT operations. In previous work, we discussed challenges and opportunities of applying the DevOps paradigm in Cyber-Physical Production Systems (CPPS) engineering, which are complex systems that integrate physical components with computer-controlled operations. This paper instantiates our DevOps4CPPS framework, spotlighting the application of GitOps and microservices in a CPPS environment. A real-world case study from laser ultra short pulse manufacturing illustrates how the approach effectively addresses the inherent challenges of CPPS, boosting agility and operational performance, while reducing downtime. The paper provides guidance to researchers and practitioners on applying DevOps practices to CPPS, and suggests future research directions.

In Cyber-Physical Production System (CPPS) engineering, multi-domain teams work concurrently and iteratively on a variety of assets to design and build (parts of) a CPPS. Previous work has shown that engineering assets can be organized in a Multi-Domain Engineering Graph (MDEG). However, the engineering assets for such system components must be continuously adapted, tested, and validated due to changing requirements and conditions. This paper investigates how a change impact analysis in multi-domain work environments can be organized to improve the change management process in the CPPS engineering lifecycle. We outline an agile framework in the context of the VDI 3695 activities for the adaptation and usage of change impact analysis techniques and tools for a multi-domain environment based on a common knowledge base, which is promising in terms of cost-savings and quality assurance. Additionally, we provide a research agenda that outlines research directions to implement such an approach for real-world use cases.

Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. A CPPS creates a complex environment between different domains (mechanical, electrical, software engineering), requiring multidisciplinary solutions to tackle growing complexity issues and reduce (maintenance) effort. Software plays an increasingly important role in assuring an effective and efficient operation of CPPSs. However, software engineering methods applied for CPPSs seem to lag behind modern software engineering methods, where tremendous progress has been made in the last years. We initiated the Software Engineering in Cyber-Physical Production Systems Workshop (SECPPS-WS) to analyze and overcome this gap. After two instances with mostly academic participants, we conducted a full-day workshop with nine industry representatives from eight companies that develop and maintain CPPSs. Each industry representative presented their current work and challenges. We collected these challenges and condensed a categorized list of challenges backed by industry statements and literature. This paper presents the resulting list and pointers to (partial) solutions to offer guidance for academia and identify promising research opportunities in this area.

In agile Cyber-physical Production System (CPPS) engineering, multi-disciplinary teams work concurrently and iteratively on various CPPS engineering artifacts, based on engineering models and Product-Process-Resource (PPR) knowledge, to design and build a production system. However, in such settings it is difficult to keep track of (i) the effects of changes across engineering disciplines, and (ii) their implications on risks to engineering quality, represented in Failure Mode and Effects Analysis (FMEA). To tackle these challenges and systematically co-evolve FMEA and PPR models, requires propagating and validating changes across engineering and FMEA artifacts. To this end, we design and evaluate a Multi-view FMEA +PPR (MvFMEA+PPR) meta-model to represent relationships between FMEA elements and CPPS engineering assets and trace their change states and dependencies in the design and validation lifecycle. We evaluate the MvFMEA + PPR meta-model in a feasibility study on the quality of a screwing process from automotive production. The study results indicate the MvFMEA + PPR meta-model to be more effective than alternative traditional approaches.

Cyber-Physical Production Systems (CPPSs) are envisioned as next-generation adaptive production systems combining modern production techniques with the latest information technology. In CPPS engineering, basic planners define the functional relations between Product-Process-Resource (PPR) views to specify valid production process and resource designs that fulfill the customer requirements. Using the Formalised Process Description standard (VDI 3682) allows to visually model these PPR views but is hard to process by machines and insufficiently defined formally. In this paper, we present the design of a Domain Specific Language (DSL), the PPR DSL, to effectively and efficiently represent PPR aspects and evaluate constraints defined for these aspects. We illustrate the PPR DSL with the use case rocker switch, abstracted from an industrial use case. We identify requirements and iteratively design and evaluate the PPR DSL. We show that the PPR DSL can model (a) the functional view of CPPSs and (b) define and efficiently evaluate constraints of a CPPS using technologies well-established in industry. We argue that the PPR DSL provides a valuable contribution for the community and industry to describe PPR aspects and evaluate constraints on these aspects. This way, PPR model can be defined and evaluated more easily for researchers and/or practitioners.

Industry 4.0 envisions adaptive production systems, i.e., Cyber-Physical Production Systems (CPPSs), to manufacture products from a product line. Product-Process-Resource modeling represents the essential aspects of a CPPS. However, due to discipline-specific models, e.g., mechanical, electrical, and automation models, it is often unclear how to integrate the proprietary data into an integrated model due to missing common understanding. This paper investigates (i) how to integrate local engineering views with Common Concepts (CCs) and using them as a defined taxonomy for modeling a network of engineering concepts; (ii) how to build an engineering network graph for visualisation and analysis considering discipline-specific needs. We motivate a method to support CPPS engineering organisations to integrate their heterogeneous data using CCs. This builds the basis for defining multi-domain engineering graphs for visualisation and analysis aspects. In this paper, we present a research agenda discussing open issues and expected results.

In the engineering of Cyber-Physical Production Systems (CPPSs), the coordination and data exchange of Product (P), Process (P‘) and Resource (R) assets is success-critical for creating high-quality engineering products. In industry, product, process, and resources are often addressed individually without explicitly expressing their dependencies. Therefore, isolated assets can hinder efficient collaboration within CPPS engineering projects that can lead to risks in case of overseen dependencies. Thus, we see the need for explicitly expressing PPR assets within an PPR Asset Network (PAN) that is (a) capable of handling assets from different viewpoints and (b) can enable efficient added value application such as risk, requirements, and configuration management. The goal of this paper include a process description for the elicitation of the PAN and to illustrate added-value applications based on a selected use case, i.e., the Industry 4.0 Testbed at CTU in Prague. We build on the PPR concept as foundation for the PAN and for added-value applications. PAN added-value applications aim at supporting risk management, requirements engineering, or configuration management by focusing on PPR assets and dependencies in context of CPPS engineering. Although PAN provides a valuable foundation for added-value applications there is the need for initial effort for the creation/generating the PAN.

Efficient and effective engineering data exchange is increasingly considered a key success factor in the life cycle of production systems, leading to the intensified development of data logistic solutions. As small and medium-size companies (SMEs) play important roles in modern engineering organization structures, SMEs have to improve their capabilities to take part in these data logistics solutions. Unfortunately, SMEs have strong human and financial resource limitations. In this paper, we introduce a modular and easy-to-use data logistics architecture that aims at enabling SMEs to implement proof-of-concept software structures, applicable to validate benefits and challenges of data logistic solutions. This data logistics architecture provides a migration path towards the full participation of SMEs in data logistic solutions for engineering data exchange. We demonstrate the application of the architecture on use cases in automotive, steel, and machining industries.

Production Systems Engineering (PSE) networks create engineering results represented by interdependent engineering data models. A key aspect of engineering result quality is the consistency achieved in and among the discipline-specific data models. However, the heterogeneity of discipline-specific data models makes it hard to represent and evaluate consistency across engineering disciplines. In this paper, we introduce PSE-MECon - a method for Modelling and Evaluating Consistency rules in PSE environments across disciplines. We describe a meta-model for representing consistency rules in a multi-disciplinary data model and demonstrate the viability of the meta-model by representing dependencies in AutomationML as a foundation for consistency checks for engineering data logistics in multi-disciplinary engineering networks.

Production system engineering involves various engineering disciplines and tools within an engineering organization. Digitalization in such an engineering organization strongly depends on the quality of the data logistics that integrates the domain-specific languages applied in and between the engineering disciplines. In this paper, we introduce a meta model and method for identifying common concepts, the common elements of the involved disciplines, for establishing the foundation for data logistics. The Common Concepts Identification method elicits common concepts on pathways through the engineering process and, thereby, facilitates designing an optimized data logistics for the digitalization in engineering.

Advanced production systems, for example in the European automotive or steel industries, incorporate Industry 4.0 defined structures based on Industry 4.0 components and their asset administration shells. Several development and standardization initiatives define structures, behavior patterns, meta models, etc. These initiatives assume the Industry 4.0 asset administration shell (I4.0AAS) as a digital twin of the Industry 4.0 component to contain all relevant engineering and runtime data. However, there is only limited discussion on how to collect and represent these data effectively and efficiently. In this paper, we discuss the collection and representation of engineering data as part of the I4.0AAS. We introduce the I4.0AAS completion method to facilitate the collection of the I4.0AAS engineering data set and its export to an I4.0AAS serialization. In the multi-disciplinary engineering of production systems, the I4.0AAS completion method builds on the AutomationML standard and on the data logistics between engineering workgroups. We evaluate the I4.0AAS completion method in a feasibility study with an I4.0 measurement cell.

Technical Debt (TD) has proven to be a suitable communication concept for software-intensive contexts to raise awareness regarding longterm negative effects of deviations from standards and guidelines. TD has also been introduced to systems engineering domain, to communicate design shortcomings in long-running, software-assisted systems. We analysed potential TD in the engineering data exchange for production system engineering. Similar to requirements engineering in software-intensive systems, data exchange in the design phase plays an integral part in Software Engineering (SE) for Production Systems Engineering: Specifications, and physical logic have to be derived from heterogeneous plant models or parameter tables designed by different stakeholders. However, traditional procedures and inadequate tool support lead to inefficient data extraction and integration. We identified debt arising from knowledge representation, data model and the exchange process. The refinement validation of identified TD was achieved through semi-structured interviews with representatives in two analysed companies. In an online survey with ten participants from an industrial consortium we evaluated whether the identified TD concepts also applied to other companies, which is true for the majority of TD. Furthermore, we discuss promising TD management strategies to repay and manage negative effects and the accumulation of additional debt, such as improved communication, test-driven model engineering and visualisation of engineering models.

Data exchange and integration in an engineering network are challenges that each engineering organization has to solve. Most practitioners have realized that these challenges require detailed consideration and involvement of the engineering disciplines involved. However, these engineering disciplines usually do not speak the same language. Thus, a data exchange logistics is required overseeing the complete engineering network and ensuring proper data transformation between the disciplines. This data logistics requires automated mapping and integration of engineering data, following the VDI Guideline 3695. In this paper, we introduce a methodology to set up such a data logistics and envision a new role, the engineering data curator, responsible for the necessary knowledge management within this methodology. Following the technical background of the authors, the methodology is presented and validated in a proof of concept using AutomationML.

Multi-disciplinary data exchange still poses many challenges: Heterogeneous data sources, diverging data views, and lack of communication lead to defects, late resolving of errors, and mismatches over the project lifecycle. Semantic approaches such as ontologies are a viable solution to derive common concepts between disciplines to limit negative effects in their collaboration. However, the application of these semantic approaches is still quite limited due to its inherent complexity. The purpose of this paper, is to discuss the concept of local glossaries as a step towards a Common Concept Glossary (CCG) method: A tool-supported method to enable and simplify the creation of common concepts derived from local glossaries that are built by discipline-specific workgroups. The local concepts can aid by making changes visible and enabling traceability and maintainability of common models between different disciplines.

Similar to advanced software engineering, multi-disciplinary systems engineering, such as industrial production systems engineering (PSE), has to integrate partial results from workgroups that design in parallel. Due to the heterogeneity of data sources and the divergence of local data models the exchanged engineering artefacts between PSE workgroups are complex, making the automation of the data exchange process in PSE difficult and prone to technical debt (TD). In this paper, we report on a case study at a large PSE company to analyze TD effects, items, and causes in PSE, focusing on the engineering data exchange process. We identified key use cases and TD types, i.e., TD in data models and TD in data instances, which have adverse effects on project effort, cost and duration as well as data quality.

The realization of a cyber-physical production system (CPPS) requires suitable methods and tools for the exchange and integration of engineering data between collaborating disciplines. Unfortunately, the description languages used in a CPPS Engineering (CPPSE) organization to describe discipline-specific views are not necessarily well suited for high-quality data exchange between workgroups. In this paper, we identify technical debt and risks regarding CPPSE description languages for data exchange using the VDI 3695 guideline as best practice. We report on effects and likely causes of the quality risks identified in a case study at a large CPPSE company. Based on data from workshops and semi-structured interviews with 28 domain experts from 12 workgroups, we propose a preliminary model relating causes and effects as foundation for analyzing and managing risks in the CPPSE data exchange process.

Data exchange and integration in a heterogeneous engineering network are challenges that an engineering organi-zation has to address. Most practitioners have realized that this challenge requires detailed consideration and involvement of the related engineering disciplines to specify and implement an appropriate data logistics. In this paper, we introduce a meta-model and method for the development of a data-mapping infrastructure to support the work of the engineers responsible for data logistics. The meta-model is the foundation for data logistics with automated data transformation, integration, and exchange by automating the mapping of engineering data as envisioned in VDI Guideline 3695. We report on a first validation of the method with a proof of concept using AutomationML.