Requirements Engineering for Cyber Physical Production Systems

  • Pericles Loucopoulos
  • Evangelia KavakliEmail author
  • Natalia Chechina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


Traditional manufacturing and production systems are in the throes of a digital transformation. By blending the real and virtual production worlds, it is now possible to connect all parts of the production process: devices, products, processes, systems and people, in an informational ecosystem. This paper examines the underpinning issues that characterise the challenges for transforming traditional manufacturing to a Cyber Physical Production System. Such a transformation constitutes a major endeavour for requirements engineers who need to identify, specify and analyse the effects that a multitude of assets need to be transformed towards a network of collaborating devices, information sources, and human actors. The paper reports on the e-CORE approach which is a systematic, analytical and traceable approach to Requirements Engineering and demonstrates its utility using an industrial-size application. It also considers the effect of Cyber Physical Production Systems on future approaches to requirements in dealing with the dynamic nature of such systems.


Requirements Engineering Industry 4.0 Factories of the Future (FoF) Cyber Physical Production Systems (CPPS) Capability-Oriented modelling 



The notion of ‘capability’ in the context of digital enterprises was first investigated by the authors in the EU-FP7 funded project CaaS (# 611351). The e-CORE approach was developed as part of the Open Models Initiative and series of NEMO Summer Schools. It was extended and applied in the Qatar National Research Fund project i-Doha (# NPRP 7-662-2-247). Aspects of the FCA use case described in this paper were part of the work carried out by the authors for the EU H2020-FOF-11-2016 project DISRUPT (# 723541). The authors wish to express their gratitude to all their colleagues with whom they collaborated in the aforementioned projects.


  1. 1.
    Deloitte, Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies (2016)Google Scholar
  2. 2.
    IEC: Factory of the Future - A White Paper, IEC (2016)Google Scholar
  3. 3.
    ITRE, Industry 4.0: Study of the ITRE Committee. Directorate General for Internal Policies, European Parliament (2016)Google Scholar
  4. 4.
    Instrument Society of America, Enterprise - Control System Integration Part 1: Models and Terminology (1999)Google Scholar
  5. 5.
    Instrument Society of America, Enterprise - Control System Integration Part 3: Activity Models of Manufacturing Operations Management (2004)Google Scholar
  6. 6.
    Garetti, M., Fumagalli, L.: P-PSO ontology for manufacturing systems. In: 14th IFAC Symposium on Information Control Problems in Manufacturing, Bucharest, Romania (2012)Google Scholar
  7. 7.
    Lemaignan, S., et al.: MASON: a proposal for an ontology of manufacturing domain. In: IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06) (2010)Google Scholar
  8. 8.
    Lin, H.K., Harding, J.A.: A manufacturing system engineering ontology model on the semantic web for inter-enterprise collaboration. Comput. Ind. 58, 428–437 (2007)CrossRefGoogle Scholar
  9. 9.
    Fumagalli, L., Pala, S., Garetti, M., Negri, E.: Ontology-based modeling of manufacturing and logistics systems for a new MES architecture. In: Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.) APMS 2014. IAICT, vol. 438, pp. 192–200. Springer, Heidelberg (2014). Scholar
  10. 10.
    Jarke, M., et al.: The brave new world of design requirements. Inf. Syst. 36(7), 992–1008 (2011)CrossRefGoogle Scholar
  11. 11.
    Loucopoulos, P.: Requirements engineering for emergent application software. In: Cordeiro, J., Maciaszek, Leszek A., Filipe, J. (eds.) ICEIS 2012. LNBIP, vol. 141, pp. 18–28. Springer, Heidelberg (2013). Scholar
  12. 12.
    Cao, J., et al.: Capability as requirement metaphor. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (2011)Google Scholar
  13. 13.
    Danesh, M.H., Yu, E.: Modeling enterprise capabilities with i*: reasoning on alternatives. In: Iliadis, L., Papazoglou, M., Pohl, K. (eds.) CAiSE 2014. LNBIP, vol. 178, pp. 112–123. Springer, Cham (2014). Scholar
  14. 14.
  15. 15.
    OMG: Value Delivery Metamodel, v. 1.0 (2013).
  16. 16.
    DoD: Systems engineering guide for systems of systems, Washington DC (2008)Google Scholar
  17. 17.
    MoD: NATO architecture framework v4.0 documentation (2013).
  18. 18.
    Teece, D.J.: Dynamic Capabilities and Strategic Management. Oxford University Press, New York (2009)Google Scholar
  19. 19.
    Helfat, C.E., Peteraf, M.A.: The dynamic resource-based view: capability lifecycles. Strat. Manag. J. 24, 997–1010 (2003)CrossRefGoogle Scholar
  20. 20.
    Sandkuhl, K., Stirna, J. (eds.): Capability Management in Digital Enterprises. Springer, Heidelberg (2018). Scholar
  21. 21.
    Barney, J.: Firm resources and sustained competitive advantage. J. Manag. 17, 99–120 (1991)Google Scholar
  22. 22.
    Loucopoulos, P., Kavakli, E.: Capability oriented enterprise knowledge modeling: the CODEK approach. Domain-Specific Conceptual Modeling, pp. 197–215. Springer, Cham (2016). Scholar
  23. 23.
    Loucopoulos, P., Kavakli, E.: Capability modeling with application on large-scale sports events. In: AMCIS 2016, San Diego, USA (2016)Google Scholar
  24. 24.
    Loucopoulos, P.: Capability modeling as a strategic analysis tool - keynote extended abstract. In: IEEE Conference on Requirements Engineering: RePa Workshop. IEEE Computer Society, Beijing (2016)Google Scholar
  25. 25.
    Dimitrakopoulos, G., et al.: Α capability-oriented modelling and simulation approach for autonomous vehicle management. Simul. Model. Pract. Theory 91, 28–47 (2018)CrossRefGoogle Scholar
  26. 26.
    Reza, H., et al.: Toward requirements engineering of cyber-physical systems: modeling cubesat. In: 2016 IEEE Aerospace Conference (2016)Google Scholar
  27. 27.
    Bertolino, A., et al.: A tour of secure software engineering solutions for connected vehicles. Softw. Qual. J. 26, 1223–1256 (2017)CrossRefGoogle Scholar
  28. 28.
    Singh, I., Lee, S.W.: Self-adaptive requirements for intelligent transportation system: a case study. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC) (2017)Google Scholar
  29. 29.
    Braun, P., et al.: Guiding requirements engineering for software-intensive embedded systems in the automotive industry. Comput. Sci.-Res. Dev. 29(1), 21–43 (2014)CrossRefGoogle Scholar
  30. 30.
    Chowdhury, N.M., Mackenzie, L., Perkins, C.: Requirement analysis for building practical accident warning systems based on Vehicular Ad-Hoc Networks. In: 2014 11th Annual Conference on Wireless On-demand Network Systems and Services (WONS) (2014)Google Scholar
  31. 31.
    Nelson, R.: Robots pick groceries, make sushi, assist amputees. EE-Eval. Eng. 57(2), 28–29 (2018)MathSciNetGoogle Scholar
  32. 32.
    Bogue, R.: Growth in e-commerce boosts innovation in the warehouse robot market. Ind. Robot.: Int. J. 43(6), 583–587 (2016)CrossRefGoogle Scholar
  33. 33.
    Breivold, H.P., Sandström, K.: Internet of things for industrial automation–challenges and technical solutions. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 532–539. IEEE (2015)Google Scholar
  34. 34.
    Weyns, D., Malek, S., Andersson, J.: FORMS: unifying reference model for formal specification of distributed self-adaptive systems. ACM Trans. Auton. Adapt. Syst. (TAAS) 2012(7), 1–61 (2012)Google Scholar
  35. 35.
    Souza, Vítor E.Silva, Lapouchnian, Alexei, Mylopoulos, John: Requirements-driven qualitative adaptation. In: Meersman, Robert, et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 342–361. Springer, Heidelberg (2012). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Digital Innovation and ResearchDublin 9Ireland
  2. 2.University of the AegeanMytileneGreece
  3. 3.Department of Computing and InformaticsBournemouth UniversityPooleUK

Personalised recommendations