Advertisement

Identifying Variability in Process Performance Indicators

  • Bedilia Estrada-TorresEmail author
  • Adela del-Río-Ortega
  • Manuel Resinas
  • Antonio Ruiz-Cortés
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 260)

Abstract

The performance perspective of business processes is concerned with the definition of performance requirements usually specified as a set of Process Performance Indicators (PPIs). Like other business process perspectives such as control-flow or data, there are cases in which PPIs are subject to variability. However, although the modelling of business process variability (BPV) has evolved significantly, there are very few contributions addressing the variability in the performance perspective of business processes. Modelling PPI variants with tools and techniques non-suitable for variability may generate redundant models, thus making it difficult its maintenance and future adaptations, also increasing possibility of errors in its managing. In this paper we present different cases of PPI variability detected as result of the analysis of several processes where BPV is present. Based on an existent metamodel used for defining PPIs over BPs, we propose its formal extension that allows the definition of PPI variability according to the cases identified.

Keywords

Business process variability Process performance indicators Variability in PPIs 

References

  1. 1.
    Hallerbach, A., Bauer, T., Reichert, M.: Configuration and management of process variants. In: Handbook on Business Process Management 1. International Handbooks on Information Systems, pp. 237–255. Springer, Berlin Heidelberg (2010)Google Scholar
  2. 2.
    Reichert, M., Hallerbach, A., Bauer, T.: Lifecycle management of business process variants. In: Handbook on Business Process Management 1, pp. 251–278. Springer, Berlin Heidelberg (2015)Google Scholar
  3. 3.
    Hallerbach, A., Bauer, T., Reichert, M.: Guaranteeing soundness of configurable process variants in Provop. In: 2009 IEEE Conference on Commerce and Enterprise Computing, pp. 98–105, July 2009Google Scholar
  4. 4.
    da Mota Silveira Neto, P.A., do Carmo Machado, I., McGregor, J.D., de Almeida, E.S., de Lemos Meira, S.R.: A systematic mapping study of software product linestesting. Inf. Softw. Technol. 53(5), 407–423 (2011)CrossRefGoogle Scholar
  5. 5.
    Milani, F., Dumas, M., Ahmed, N., Matuleviius, R.: Modelling families of business process variants: A decomposition driven method. Inf. Syst. 56, 55–72 (2016)CrossRefGoogle Scholar
  6. 6.
    Aiello, M., Bulanov, P., Groefsema, H.: Requirements and tools for variability management. In: 2010 IEEE 34th Annual Computer Software and Applications Conference Workshops (COMPSACW), pp. 245–250 (July 2010)Google Scholar
  7. 7.
    Saidani, O., Nurcan, S.: Business process modeling: a multi-perspective approach integrating variability. In: Bider, I., Gaaloul, K., Krogstie, J., Nurcan, S., Proper, H.A., Schmidt, R., Soffer, P. (eds.) BPMDS 2014 and EMMSAD 2014. LNBIP, vol. 175, pp. 169–183. Springer, Heidelberg (2014)Google Scholar
  8. 8.
    Rosa, M.L., Dumas, M., ter Hofstede, A.H., Mendling, J.: Configurable multi-perspective business process models. Inf. Syst. 36(2), 313–340 (2011)CrossRefGoogle Scholar
  9. 9.
    La Rosa, M., van der Aalst, W.M.P., Dumas, M., Milani, F.P.: Business process variability modeling: A survey. Report, ACM Digital Library (2013)Google Scholar
  10. 10.
    Torres, V., Zugal, S., Weber, B., Reichert, M., Ayora, C., Pelechano, V.: A qualitative comparison of approaches supporting business process variability. In: La Rosa, M., Soffer, P. (eds.) Business Process Management Workshops. LNBIP, vol. 132, pp. 560–572. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Lodhi, A., Koppen, V., Wind, S., Saake, G., Turowski, K.: Business process modeling language for performance evaluation. In: 47th Hawaii International Conference on System Sciences (HICSS), pp. 3768–3777 Jan 2014Google Scholar
  12. 12.
    del Río-Ortega, A., Resinas, M., Cabanillas, C., Ruiz-Cortés, A.: On the definition and design-time analysis of process performance indicators. Inf. Syst. 38(4), 470–490 (2013)CrossRefGoogle Scholar
  13. 13.
    Milani, F., Dumas, M., Matulevičius, R.: Identifying and classifying variations in business processes. In: Bider, I., Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Wrycza, S. (eds.) EMMSAD 2012 and BPMDS 2012. LNBIP, vol. 113, pp. 136–150. Springer, Heidelberg (2012)Google Scholar
  14. 14.
    Cognini, R., Corradini, F., Polini, A., Re, B.: Extending feature models to express variability in business process models. In: Persson, A., Stirna, J. (eds.) CAiSE 2015 Workshops. LNBIP, vol. 215, pp. 245–256. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  15. 15.
    Rolland, C., Nurcan, S.: Business process lines to deal with the variability. In: 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1–10 (Jan 2010)Google Scholar
  16. 16.
    Machado, I., Bonifácio, R., Alves, V., Turnes, L., Machado, G.: Managing variability in business processes: An aspect-oriented approach. In: Proceedings of the 2011 I Workshop on Early Aspects. EA 11, pp. 25–30. ACM, New York, NY, USA (2011)Google Scholar
  17. 17.
    Hallerbach, A., Bauer, T., Reichert, M.: Capturing variability in business process models: the Provop approach. J. Softw. Maintenance Evol. Res. Pract. 22(6–7), 519–546 (2010)Google Scholar
  18. 18.
    Rosemann, M., van der Aalst, W.M.P.: A configurable reference modelling language. Inf. Syst. 32(1), 1–23 (2007)CrossRefGoogle Scholar
  19. 19.
    Razavian, M., Khosravi, R.: Modeling variability in business process models using UML. In: Fifth International Conference on Information Technology: New Generations, ITNG 2008, pp. 82–87 (April 2008)Google Scholar
  20. 20.
    del-Río-Ortega, A., Cabanillas, C., Resinas, M., Ruiz-Cortés, A.: PPINOT tool suite: a performance management solution for process-oriented organisations. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 675–678. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Strecker, S., Frank, U., Heise, D., Kattenstroth, H.: MetricM: a modeling method in support of the reflective design and use of performance measurement systems. Inf. Syst. e-Bus. Manage. 10(2), 241–276 (2011)CrossRefGoogle Scholar
  22. 22.
    Popova, V., Sharpanskykh, A.: Modeling organizational performance indicators. Inf. Syst. 35(4), 505–527 (2010)CrossRefGoogle Scholar
  23. 23.
    Suhartono, D.: Variability model implementation on key performance indicator application. Int. J. Innov. Manage. Technol. 6(1), 77–80 (2015)Google Scholar
  24. 24.
    Vianden, M., Lichter, H.: Variability model towards a metric specification process. In: Proceedings of the International Conference on Computer Science and Information Technology, pp. 76–79 (2011)Google Scholar
  25. 25.
    Apics, S.C.C.: Supply Chain Operations Reference Model: SCOR Version 11.0. Supply Chain Council APICS, CCOR, CPIM, CSCP, DCOR, SCOR, and SCORmark are all registered trademarks of APICS. All rights reserved (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bedilia Estrada-Torres
    • 1
    Email author
  • Adela del-Río-Ortega
    • 1
  • Manuel Resinas
    • 1
  • Antonio Ruiz-Cortés
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de SevillaSevilleSpain

Personalised recommendations