Advertisement

Detectability Based Prioritization of Interdependent Supply Chain Risks

  • Abroon Qazi
  • John Quigley
  • Alex Dickson
  • Şule Önsel Ekici
  • Barbara Gaudenzi
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 250)

Abstract

Supply chain risks must be assessed in relation to the complex interdependent interaction between these risks. Generally, risk registers are used for assessing the importance of risks that treat risks in silo and fail to capture the systemic relationships. Limited studies have focused on assessing supply chain risks within the interdependent network setting. We adapt the detectability feature from the Failure Modes and Effects Analysis (FMEA) and integrate it within the theoretically grounded framework of Bayesian Belief Networks (BBNs) for prioritizing supply chain risks. Detectability represents the effectiveness of early warning system in detecting a risk before its complete realization. We introduce two new risk measures and a process for prioritizing risks within a probabilistic network of interacting risks. We demonstrate application of our method through a simple example and compare results of different ranking schemes treating risks as independent or interdependent factors. The results clearly reveal importance of considering interdependency between risks and incorporating detectability within the modelling framework.

Keywords

Supply chain risks Risk registers Systemic Detectability Failure modes and effects analysis Bayesian belief networks 

References

  1. 1.
    Son, J.Y., Orchard, R.K.: Effectiveness of policies for mitigating supply disruptions. Int. J. Phys. Distrib. Logistics Manage. 43(8), 684–706 (2013)CrossRefGoogle Scholar
  2. 2.
    Ackermann, F., Howick, S., Quigley, J., Walls, L., et al.: Systemic risk elicitation: using causal maps to engage stakeholders and build a comprehensive view of risks. Eur. J. Oper. Res. 238(1), 290–299 (2014)CrossRefGoogle Scholar
  3. 3.
    Handfield, R., Blackhurst, J., Craighead, C.W., Elkins, D.: Introduction: a managerial framework for reducing the impact of disruptions to the supply chain (2011)Google Scholar
  4. 4.
    Standards, Risk Management: Principles and Guidelines (AS/NZS ISO 31000: 2009). Standards Australia, Sydney (2009)Google Scholar
  5. 5.
    Khan, O., Christopher, M., Burnes, B.: The impact of product design on supply chain risk: a case study. Int. J. Phys. Distrib. Logistics Manage. 38(5), 412–432 (2008)CrossRefGoogle Scholar
  6. 6.
    Garvey, M.D., Carnovale, S., Yeniyurt, S.: An analytical framework for supply network risk propagation: a Bayesian network approach. Eur. J. Oper. Res. 243(2), 618–627 (2015)CrossRefGoogle Scholar
  7. 7.
    Badurdeen, F., Shuaib, M., Wijekoon, K., Brown, A., et al.: Quantitative modeling and analysis of supply chain risks using Bayesian theory. J. Manuf. Technol. Manage. 25(5), 631–654 (2014)CrossRefGoogle Scholar
  8. 8.
    Carbone, T.A., Tippett, D.D.: Project risk management using the project risk FMEA. Eng. Manage. J. 16(4), 28–35 (2004)CrossRefGoogle Scholar
  9. 9.
    Gilchrist, W.: Modelling failure modes and effects analysis. Int. J. Qual. Reliab. Manage. 10(5), 16–23 (1993)CrossRefGoogle Scholar
  10. 10.
    Nepal, B., Yadav, O.P.: Bayesian belief network-based framework for sourcing risk analysis during supplier selection. Int. J. Prod. Res. 53(20), 6114–6135 (2015)CrossRefGoogle Scholar
  11. 11.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, New York (2007)CrossRefGoogle Scholar
  12. 12.
    Qazi, A., Quigley, J., Dickson, A.: A novel framework for quantification of supply chain risks. In: 4th Student Conference on Operational Research, University of Nottingham, UK (2014)Google Scholar
  13. 13.
    Christopher, M., Lee, H.: Mitigating supply chain risk through improved confidence. Int. J. Phys. Distrib. Logistics Manage. 34(5), 388–396 (2004)CrossRefGoogle Scholar
  14. 14.
    Sodhi, M.S., Tang, C.S.: Managing Supply Chain Risk. International Series in Operations Research and Mangement Science, vol. 172. Springer, New York (2012)Google Scholar
  15. 15.
    Bradley, J.R.: An improved method for managing catastrophic supply chain disruptions. Bus. Horiz. 57(4), 483–495 (2014)CrossRefGoogle Scholar
  16. 16.
    Segismundo, A., Miguel, P.A.C.: Failure mode and effects analysis (FMEA) in the context of risk management in new product development. Int. J. Qual. Reliab. Manage. 25(9), 899–912 (2008)CrossRefGoogle Scholar
  17. 17.
    Tuncel, G., Alpan, G.: Risk assessment and management for supply chain networks: a case study. Comput. Ind. 61(3), 250–259 (2010)CrossRefGoogle Scholar
  18. 18.
    Lockamy, A., McCormack, K.: Analysing risks in supply networks to facilitate outsourcing decisions. Int. J. Prod. Res. 48(2), 593–611 (2009)CrossRefGoogle Scholar
  19. 19.
    Lockamy, A.: Benchmarking supplier risks using Bayesian networks. Benchmarking: Int. J. 18(3), 409–427 (2011)CrossRefGoogle Scholar
  20. 20.
    Lockamy, A., McCormack, K.: Modeling supplier risks using Bayesian networks. Industr. Manage. Data Syst. 112(2), 313–333 (2012)CrossRefGoogle Scholar
  21. 21.
    Lockamy, A.: Assessing disaster risks in supply chains. Industr. Manage. Data Syst. 114(5), 755–777 (2014)CrossRefGoogle Scholar
  22. 22.
    Dogan, I., Aydin, N.: Combining Bayesian networks and total cost of ownership method for supplier selection analysis. Comput. Industr. Eng. 61(4), 1072–1085 (2011)CrossRefGoogle Scholar
  23. 23.
    Qazi, A., Quigley, J., Dickson, A., Gaudenzi, B.: A new modelling approach of evaluating preventive and reactive strategies for mitigating supply chain risks. In: Corman, F., Voß, S., Negenborn, R.R. (eds.) ICCL 2015. LNCS, vol. 9335, pp. 569–585. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24264-4_39 CrossRefGoogle Scholar
  24. 24.
    Qazi, A., Quigley, J., Dickson, A., Gaudenzi, B., et al.: Evaluation of control strategies for managing supply chain risks using Bayesian belief networks. In: International Conference on Industrial Engineering and Systems Management (2015)Google Scholar
  25. 25.
    Qazi, A., Quigley, J., Dickson, A., Gaudenzi, B., et al.: Selection of optimal redundancy strategies for a supply network. In: Kirsten, W., Blecker, T., Ringle, C.M. (eds.) Hamburg International Conference of Logistics (2015)Google Scholar
  26. 26.
    Onisko, A.: Medical diagnosis. In: Pourret, O., Naïm, P., Marcot, B. (eds.) Bayesian Networks: A Practical Guide to Applications, vol. 73, pp. 15–32. Wiley, West Sussex (2008)CrossRefGoogle Scholar
  27. 27.
    Blodgett, J.G., Anderson, R.D.: A Bayesian network model of the consumer complaint process. J. Serv. Res. 2(4), 321–338 (2000)CrossRefGoogle Scholar
  28. 28.
    Qazi, A., Quigley, J., Dickson, A.: Supply chain risk management: systematic literature review and a conceptual framework for capturing interdependencies between risks. In: 5th International Conference on Industrial Engineering and Operations Management, Dubai (2015)Google Scholar
  29. 29.
    Sigurdsson, J.H., Walls, L.A., Quigley, J.L.: Bayesian belief nets for managing expert judgement and modelling reliability. Qual. Reliab. Eng. Int. 17(3), 181–190 (2001)CrossRefGoogle Scholar
  30. 30.
    GeNIe. The Decision Systems Laboratory, GeNIe and SMILE (2015). http://genie.sis.pitt.edu/about.html. Accessed 5 June 2015

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Abroon Qazi
    • 1
  • John Quigley
    • 1
  • Alex Dickson
    • 1
  • Şule Önsel Ekici
    • 2
  • Barbara Gaudenzi
    • 3
  1. 1.Strathclyde Business SchoolUniversity of StrathclydeGlasgowUK
  2. 2.Industrial Engineering DepartmentDogus UniversityIstanbulTurkey
  3. 3.Faculty of Business EconomicsUniversity of VeronaVeronaItaly

Personalised recommendations