Towards Smart Logistics Process Management

  • Raef MousheimishEmail author
  • Yehia Taher
  • Béatrice Finance
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 239)


Logistics processes are generally agreed-upon, long running propositions between multiple partners, which are specified over Service Level Agreements as constraints to be maintained. However, these constraints can be violated at any time due to various unforeseen events that may stem from the process evolving context, leading the process to end up in unfortunate situations. In this paper, we present our framework that correlates critical business operations together with contextual events in order to predict possible violations prior to their occurrences while proactively generating mitigation countermeasures. In addition we develop a software and experiment it to demonstrate the practical applicability of the framework.


Business process management SLA violations Prediction Adaptation SLA Violations 


  1. 1.
    Kephart, J., Chess, D.: The vision of autonomic computing. Computer 36(1), 41–50 (2003)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wang, C., Pazat, J.-L.: A two-phase online prediction approach for accurate and timely adaptation decision. In: SCC, pp. 218–225 (2012)Google Scholar
  3. 3.
    Leitner, P., Mechlmayr, A., Rosenberg, F., Dustdar, S.: Monitoring, prediction and prevention of SLA violations in composite services. In: ICWS 2010, pp. 396–376 (2010)Google Scholar
  4. 4.
    Metzger, A., Franklin, R., Engel, Y.: Predictive monitoring of heterogeneous service-oriented business networks: The transport and logistics case. In: SRII 2012 Global ConferenceGoogle Scholar
  5. 5.
    Engel, Y., Etzion, O., Feldman, Z.: A basic model for proactive event-driven computing. In: DEBS (2012)Google Scholar
  6. 6.
    Marrella, A., Russo, A., Mecella, M.: Planlets: automatically recovering dynamic processes in YAWL. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 268–286. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  7. 7.
    Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Heidelberg (2014) Google Scholar
  8. 8.
    Cabanillas, C., Di Ciccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 424–432. Springer, Heidelberg (2014) Google Scholar
  9. 9.
    Bassil, S., Keller, R.K., Kropf, P.G.: A workflow-oriented system architecture for the management of container transportation. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 116–131. Springer, Heidelberg (2004) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Raef Mousheimish
    • 1
    • 2
    Email author
  • Yehia Taher
    • 1
  • Béatrice Finance
    • 1
  1. 1.Laboratoire PRiSMUniversité de Versailles Saint-Quentin-en-YvelinesVersaillesFrance
  2. 2.Fondation des Sciences du Patrimoine, LabEx PATRIMACergyFrance

Personalised recommendations