Advertisement

Decomposition of Tasks in Business Process Outsourcing

  • Kurt Sandkuhl
  • Alexander Smirnov
  • Nikolay Shilov
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 288)

Abstract

In industrial areas with a highly competitive environment many enterprises consider outsourcing of IT-services as an option to reduce IT-related costs. In this context, cloud computing architectures and outsourcing of business processes into the cloud are potential candidates to improve resource utilization and to reduce operative IT-costs. In this paper, we focus on a specific aspect of cloud computing and outsourcing: the use of concepts from crowd-sourcing or crowd computing in business process outsourcing (BPO). The approach used in this paper is to bring together techniques from enterprise modeling and from crowd-computing for the purpose of business process decomposition. The contributions of the paper are an analysis of requirements to process decomposition from a business process outsourcing perspective, three different strategies for performing the decomposition and an initial validation of these strategies using an industrial case.

Keywords

Business Process Outsourcing Crowdsourcing Enterprise modeling Task pattern Process decomposition 

Notes

Acknowledgements

This work was partially financially supported by the Project 213 within the research program I.5P of the Russian Academy of Sciences, by Government of Russian Federation, Grant 074-U01. Furthermore, it was partly financed by the German Ministry of Research and Education, research project KOSMOS-2.

References

  1. 1.
    Carr, N.G.: IT doesn’t matter. IEEE Eng. Manage. Rev. 32(1), 24–32 (2004)CrossRefGoogle Scholar
  2. 2.
    Krogstie, J.: Model-Based Development and Evolution of Information Systems - A Quality Approach. Springer, London (2012)CrossRefGoogle Scholar
  3. 3.
    Thuan, N.H., Antunes, P., Johnstone, D.: Factors influencing the decision to crowdsource. In: Antunes, P., Gerosa, M.A., Sylvester, A., Vassileva, J., Vreede, G.-J. (eds.) CRIWG 2013. LNCS, vol. 8224, pp. 110–125. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41347-6_9 CrossRefGoogle Scholar
  4. 4.
    Schneider, D., de Souza, J., Moraes, K.: Multidões: a nova onda do CSCW? In: 8th Brazilian Symposium on Collaborative Systems. SBSC (2011)Google Scholar
  5. 5.
    Knuth, D.E.: Computer-drawn flowcharts. Commun. ACM 6(9), 555–563 (1963)CrossRefGoogle Scholar
  6. 6.
    White, S.A.: Introduction to BPMN, vol. 2. IBM Cooperation, Armonk (2004)Google Scholar
  7. 7.
    Scheer, A.-W., Nüttgens, M.: ARIS architecture and reference models for business process management. In: Aalst, W., Desel, J., Oberweis, A. (eds.) Business Process Management. LNCS, vol. 1806, pp. 376–389. Springer, Heidelberg (2000). doi: 10.1007/3-540-45594-9_24 CrossRefGoogle Scholar
  8. 8.
    Vernadat, F.B.: Enterprise Modelling and Integration. Chapman & Hall, London (1996)zbMATHGoogle Scholar
  9. 9.
    Sandkuhl, K., Stirna, J., Persson, A., Wißotzki, M.: Enterprise Modeling: Tackling Business Challenges with the 4EM Method (The Enterprise Engineering Series). Springer, Heidelberg (2014). ISBN 978-3662437247CrossRefGoogle Scholar
  10. 10.
    Lillehagen, F., Krogstie, J.: Active Knowledge Modelling of Enterprises. Springer, Heidelberg (2009). ISBN 978-3-540-79415-8Google Scholar
  11. 11.
    Lillehagen, F.: The foundations of AKM technology. In: 10th International Conference on Concurrent Engineering (CE) Conference, Madeira, Portugal (2003)Google Scholar
  12. 12.
    Enterprise Integration Patterns. http://www.eaipatterns.com/. Accessed 26 Mar 2012
  13. 13.
    Sandkuhl, K., Smirnov, A., Shilov, N.: Configuration of automotive collaborative engineering and flexible supply networks. In: Cunningham, P., Cunningham, M. (eds.) Expanding the Knowledge Economy – Issues, Applications, Case Studies. IOS Press, Amsterdam (2007). ISBN 978-1-58603-801-4Google Scholar
  14. 14.
    Sandkuhl, K.: Capturing product development knowledge with task patterns: evaluation of economic effects. Q. J. Control Cybern. 39(1), 259–273 (2010). Systems Research Institute, Polish Academy of ScienceszbMATHGoogle Scholar
  15. 15.
    Schenk, E., Guittard, C.: Towards a characterization of crowdsourcing practices. J. Innov. Econ. 7(1), 93–107 (2011)CrossRefGoogle Scholar
  16. 16.
    Howe, J.: The rise of crowdsourcing. Wired Mag. 14, 1–4 (2006). Dorsey PressGoogle Scholar
  17. 17.
    Smirnov, A., Ponomarev, A., Shilov, N.: Hybrid crowd-based decision support in business processes. In: CENTERIS 2014, 15–17 October 2014, Lisbon, Portugal, vol. 16, pp. 376–384 (2014)Google Scholar
  18. 18.
    Kulkarni, A., Can, M., Hartmann, B.: Collaboratively crowdsourcing workflows with turkomatic. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 1003–1012. ACM (2012)Google Scholar
  19. 19.
    Little, G., Chilton, L.B., Goldman, M., Miller, R.C.: Turkit: human computation algorithms on mechanical turk. In: Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology, pp. 57–66. ACM (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kurt Sandkuhl
    • 1
    • 3
  • Alexander Smirnov
    • 2
    • 3
  • Nikolay Shilov
    • 2
    • 3
  1. 1.The University of RostockRostockGermany
  2. 2.SPIIRASSt. PetersburgRussia
  3. 3.ITMO UniversitySt. PetersburgRussia

Personalised recommendations