Industry Voices on Software Engineering Challenges in Cyber-Physical Production Systems Engineering

Feichtinger, Kevin; Meixner, Kristof; Rinker, Felix; Koren, István; Eichelberger, Holger; Heinemann, Tonja; Holtmann, Jörg; Konersmann, Marco; Michael, Judith; Neumann, Eva-Maria; Pfeiffer, Jérôme; Rabiser, Rick; Riebisch, Matthias; Schmid, Klaus

doi:10.1109/etfa52439.2022.9921568

Cited by 22 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, this results in a high level of organisational complexity. In addition, each discipline would like to keep its familiar tools, processes, artefacts and ways of working, as they have been using them for years and are not necessarily willing to exchange them for another (Feichtinger et al, 2022).…”

Section: Challenges Of Integrating Alm and Plmmentioning

confidence: 99%

Interdisciplinary system lifecycle management – a systematic literature review

Wyrwich,

Kharatyan,

Dumitrescu

2024

Proc. Des. Soc.

View full text Add to dashboard Cite

The increasing proportion of software in technical products means that both the products and the associated development processes are becoming more complex. An integration of the existing lifecycle considerations Application Lifecycle Management and Product Lifecycle Management into an interdisciplinary System Lifecycle Management promises to make the complexity manageable. To obtain an overview of the current benefits, challenges, requirements, approaches and open research gaps in the context of an ALM-PLM integration, this contribution presents the results of a Systematic Literature Review.

show abstract

Section: Challenges Of Integrating Alm and Plmmentioning

confidence: 99%

Interdisciplinary system lifecycle management – a systematic literature review

Wyrwich,

Kharatyan,

Dumitrescu

2024

Proc. Des. Soc.

View full text Add to dashboard Cite

show abstract

“…To overcome these challenges, we investigate how agile software engineering approaches, such as model-based software engineering [13] and continuous development [14], have identified solution approaches to automate and maintain software development processes. However, these approaches assume only software artefacts and are not sufficiently tailored to traceable multi-view model changes in PSE [15].…”

Section: C2 Manual Local Model Mapping and Integration Effortsmentioning

confidence: 99%

“…In software engineering, DevOps [14] focuses on enabling continuous integration to achieve, among other goals, traceability: agile approaches, such as the Git workflow 14 , and orchestration and automation software (e.g., Docker 15 , Ansible 16 , Chef 17 ) improve the quality and traceability of software development processes. Furthermore, configuration management in continuous integration environments is based on configuration files (often text) that provide details about infrastructure and parameters to trace different versions.…”

Section: Model-based Devops For Traceabilitymentioning

confidence: 99%

See 1 more Smart Citation

Traceable Multi-view Model Integration: A Transformation Pipeline for Agile Production Systems Engineering

et al. 2023

Self Cite

View full text Add to dashboard Cite

Purpose. Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several disciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies on concepts of PSE assets, such as plans and configurations. Thus, tracing multi-view changes to PSE assets is challenging. Method. Following the Design Science approach, we (i) elicit requirements for tracing multi-view changes to PSE assets from a domain analysis in automotive manufacturing; (ii) introduce and evaluate the Traceable Multi-view Model Transformation (TMvMT) process; and (iii) propose the TMvMT pipeline architecture to provide traceable model integration capabilities for agile PSE. Results. In a feasibility study on robot cell models, we evaluate the TMvMT process and architecture regarding the requirements for traceability compared to traditional approaches. Conclusion. The proposed TMvMT approach provides traceability of changes in multi-view modelling as a basis through the separation of modelling transformation steps and provision of clear input and output artefacts to achieve traceable configuration management and validation of system designs for production system assets in agile PSE.

show abstract

“…The ability to integrate perspectives from multiple disciplines poses a central challenge for the engineering of complex production systems (Feichtinger et al, 2022). One solution that is frequently proposed is exchanging the sequential mechatronic approach for a model-based Systems Engineering approach (Kübler et al, 2018).…”

Section: How Can Shared Mental Models Enhance the Systems Engineering...mentioning

confidence: 99%

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Schmidt

Müller

2023

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

Interdisciplinary collaboration frequently comes into play when existing problems cannot be solved by one discipline alone. However, the interlocking of contributions from different disciplines is by no means trivial. This exploratory study examines one foundation of successful teamwork, namely shared mental models. To this end, we compared the contents of mental models between members of different but interdependent disciplines who collaboratively solve knowledge‐intensive, creative tasks. Five automation and five mechanical engineers were recruited from a company that produces packaging machines. In semi‐structured interviews, participants reported their approach to evaluating the process behavior of a packaging machine, and their mental models were represented in concept maps. Quantitative analyses revealed that the maps of automation engineers were smaller than those of mechanical engineers. In qualitative analyses, the focus on different levels of abstraction and on contents from the two disciplines was examined. Automation engineers represented a large proportion of rather abstract machine functions, whereas mechanical engineers additionally represented the physical implementation of these functions. The disciplinary focus also differed in the sense that automation engineers mainly attended to automated machine processes, while mechanical engineers attended to both mechanical and automated processes. Overall, automation engineers' focus was narrower than that of mechanical engineers. We explain these results by considering typical tasks and reasoning processes in both disciplines, and discuss how shared mental models can aid the integration of different disciplinary perspectives, for instance, during Systems Engineering.

show abstract

Industry Voices on Software Engineering Challenges in Cyber-Physical Production Systems Engineering

Cited by 22 publications

References 0 publications

Interdisciplinary system lifecycle management – a systematic literature review

Interdisciplinary system lifecycle management – a systematic literature review

Traceable Multi-view Model Integration: A Transformation Pipeline for Agile Production Systems Engineering

Discipline differences in mental models: How mechanical engineers and automation engineers evaluate machine processes

Contact Info

Product

Resources

About