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Visualization Approach for RAMI 4.0 Value Chain Analysis
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-9118-5861
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-3874-9968
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-9412-6872
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-4133-3317
2024 (English)In: IEEE Open Journal of the Industrial Electronics Society, E-ISSN 2644-1284, Vol. 6, p. 1-24Article in journal (Refereed) Published
Abstract [en]

Industry 4.0 has revolutionized industrial automation, with models like RAMI 4.0 providing a structured framework for optimizing value chains and processes. However, the complexity and abstract nature of RAMI 4.0 have limited its practical application, especially due to the lack of clear visualization methods to understand industrial ecosystems. Effective visualization is essential to translate this framework into actionable insights, enabling stakeholders to grasp system interactions, dependencies, and value-creation processes. This paper proposes a multidimensional visualization approach, illustrated through a smart heat pump example, to map information and operational technologies, their interactions, and value chains. Combining 3D visualizations for integrated system overviews with 2D visualizations for task-specific analysis, the approach provides a comprehensive understanding of RAMI 4.0 value chains, enabling stakeholders to address their analytical needs with clarity. It facilitates run-time value chain analysis, offering real-time insights for decision-making during operations. The approach maps industrial systems across RAMI 4.0 axes and aligns them with engineering processes and lifecycle phases, enabling the exploration of system interactions, dependencies, and stakeholder contributions. This supports the analysis of engineering and business processes, optimizes infrastructure, and facilitates smooth technological transitions. It enhances RAMI 4.0’s utility for real-time decision-making and operational efficiency, boosting competitiveness in industrial ecosystems.

Place, publisher, year, edition, pages
IEEE, 2024. Vol. 6, p. 1-24
Keywords [en]
Industry 4.0, Smart Industry Ecosystems, Microservice Architecture, Lifecycle Management, Run-time Value Chain Analysis, Real-time Decision Making, 2D and 3D Visualizations, Stakeholder Collaboration
National Category
Computer and Information Sciences
Research subject
Cyber-Physical Systems
Identifiers
URN: urn:nbn:se:ltu:diva-111002DOI: 10.1109/OJIES.2024.3520410ISI: 001395226300005Scopus ID: 2-s2.0-85212964999OAI: oai:DiVA.org:ltu-111002DiVA, id: diva2:1919394
Funder
European Commission, 101111977, 101092069
Note

Validerad;2025;Nivå 1;2025-01-30 (signyg);

Full text license: CC BY 4.0

Available from: 2024-12-09 Created: 2024-12-09 Last updated: 2025-01-30Bibliographically approved
In thesis
1. Data-Driven Optimizations in Production Value Networks
Open this publication in new window or tab >>Data-Driven Optimizations in Production Value Networks
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industry 4.0 is transforming traditional production systems into dynamic and adaptive value networks. However, this shift reveals significant challenges in achieving seamless interoperability, workflow optimization, and effective value chain analysis within complex production environments. This thesis contributes to addressing these challenges through a structured approach, supporting data-driven and adaptive decision-making to optimize operations in dynamic production value networks.

The research commences by architecting microservice-oriented systems that facilitate the integration of legacy and brownfield technologies with Industry 4.0-compliant environments. By leveraging the Eclipse Arrowhead framework, the thesis demonstrates how diverse systems can exchange data and collaborate at runtime, establishing the foundation for cohesive and interoperable production networks.

Building on this interoperable structure, the thesis explores AI-driven optimizations across key areas, including workflow optimization, predictive maintenance, and demand response. These approaches support operational efficiency and adaptability in production value networks. Case studies showcase collaborative learning models for condition monitoring and an edge-based framework to optimize energy use, demonstrating tangible improvements in efficiency and resilience.

Finally, a significant contribution of this thesis is the introduction of tools for visualization and analysis of value chains. Using the Reference Architectural Model for Industry 4.0 (RAMI 4.0), the research provides methods to map and evaluate value creation within dynamic production networks. By integrating activity-based costing with microservice architectures, it offers granular insights into cost and value dynamics at runtime, enabling agile and informed decision-making in complex industrial environments.

Through these contributions, the thesis advances the understanding and implementation of data-driven optimizations in production value networks, supporting agility and sustainability while contributing to the transition to circular business models by enabling value chain analysis across the product lifecycle. The thesis serves as a bridge between theoretical frameworks and practical applications, providing valuable insights for both academia and industry, and paving the way for more efficient and sustainable production ecosystems.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2025
Keywords
Industry 4.0, Smart Industry Ecosystems, Microservice Architecture, Lifecycle Management, Run-time Value Chain Analysis, Real-time Decision Making, Production Value Networks, Stakeholder Collaboration
National Category
Engineering and Technology
Research subject
Cyber-Physical Systems
Identifiers
urn:nbn:se:ltu:diva-111004 (URN)978-91-8048-716-0 (ISBN)978-91-8048-717-7 (ISBN)
Public defence
2025-02-26, A117, Luleå University of Technology, Luleå, 09:00 (English)
Opponent
Supervisors
Projects
Arrowhead fPVNAI REDGIO 5.0
Funder
European Commission
Available from: 2024-12-09 Created: 2024-12-09 Last updated: 2025-01-30Bibliographically approved

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Javed, Salmanvan Deventer, JanPaniagua, CristinaDelsing, Jerker

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