IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v245y2015i1p320-332.html
   My bibliography  Save this article

Managing operational disruptions through capital adequacy and process improvement

Author

Listed:
  • Mizgier, Kamil J.
  • Hora, Manpreet
  • Wagner, Stephan M.
  • Jüttner, Matthias P.

Abstract

Firms maintain a capital charge to manage the risk of low-frequency, high-impact operational disruptions. The loss distribution approach (LDA) measures the capital charge using two inputs: the frequency and severity of operational disruptions. In this study, we investigate whether or not capital charge could be combined with process improvement, an approach predominantly employed for managing high-frequency, low-impact operational disruptions. Using the categorization of events defined by the Basel Accord for different types of operational risk events, we verify three propositions. First, we test whether classification of operational disruptions is warranted to manage the risk. Second, we posit that classification of operational disruptions will display different statistical properties in manufacturing and in the financial services sector. Finally, we test whether risk of operational disruptions can be managed through a combination of process improvement and capital adequacy. We obtained data on 5442 operational disruptions and ran Monte Carlo simulations spanning both these sectors and seven event types. The results reveal that process improvement can be a first line of defense to manage certain types of operational risk events.

Suggested Citation

  • Mizgier, Kamil J. & Hora, Manpreet & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Managing operational disruptions through capital adequacy and process improvement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 320-332.
  • Handle: RePEc:eee:ejores:v:245:y:2015:i:1:p:320-332
    DOI: 10.1016/j.ejor.2015.02.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221715001307
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Grundke, Peter, 2010. "Top-down approaches for integrated risk management: How accurate are they?," European Journal of Operational Research, Elsevier, vol. 203(3), pages 662-672, June.
    2. Cornalba, Chiara & Giudici, Paolo, 2004. "Statistical models for operational risk management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 166-172.
    3. Wagner, Stephan M. & Bode, Christoph & Koziol, Philipp, 2009. "Supplier default dependencies: Empirical evidence from the automotive industry," European Journal of Operational Research, Elsevier, vol. 199(1), pages 150-161, November.
    4. repec:cup:jfinqa:v:46:y:2011:i:06:p:1683-1725_00 is not listed on IDEAS
    5. Bellini, Tiziano, 2013. "Integrated bank risk modeling: A bottom-up statistical framework," European Journal of Operational Research, Elsevier, vol. 230(2), pages 385-398.
    6. Talluri, Srinivas & Narasimhan, Ram & Chung, Wenming, 2010. "Manufacturer cooperation in supplier development under risk," European Journal of Operational Research, Elsevier, vol. 207(1), pages 165-173, November.
    7. Schmalensee, Richard, 1985. "Do Markets Differ Much?," American Economic Review, American Economic Association, vol. 75(3), pages 341-351, June.
    8. Dahen, Hela & Dionne, Georges, 2010. "Scaling models for the severity and frequency of external operational loss data," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1484-1496, July.
    9. Wang, Weijia & Plante, Robert D. & Tang, Jen, 2013. "Minimum cost allocation of quality improvement targets under supplier process disruption," European Journal of Operational Research, Elsevier, vol. 228(2), pages 388-396.
    10. Jan Dhaene & Andreas Tsanakas & Emiliano A. Valdez & Steven Vanduffel, 2012. "Optimal Capital Allocation Principles," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 1-28, March.
    11. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    12. Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
    13. de Fontnouvelle, Patrick & Dejesus-Rueff, Virginia & Jordan, John S. & Rosengren, Eric S., 2006. "Capital and Risk: New Evidence on Implications of Large Operational Losses," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(7), pages 1819-1846, October.
    14. Yimin Wang & Wendell Gilland & Brian Tomlin, 2010. "Mitigating Supply Risk: Dual Sourcing or Process Improvement?," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 489-510, September.
    15. Kevin B. Hendricks & Vinod R. Singhal, 2005. "Association Between Supply Chain Glitches and Operating Performance," Management Science, INFORMS, vol. 51(5), pages 695-711, May.
    16. Fragnière, Emmanuel & Gondzio, Jacek & Yang, Xi, 2010. "Operations risk management by optimally planning the qualified workforce capacity," European Journal of Operational Research, Elsevier, vol. 202(2), pages 518-527, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:ejores:v:274:y:2019:i:1:p:155-164 is not listed on IDEAS
    2. Tong Shu & Xiaoqin Gao & Shou Chen & Shouyang Wang & Kin Keung Lai & Lu Gan, 2016. "Weighing Efficiency-Robustness in Supply Chain Disruption by Multi-Objective Firefly Algorithm," Sustainability, MDPI, Open Access Journal, vol. 8(3), pages 1-27, March.
    3. repec:taf:tprsxx:v:55:y:2017:i:18:p:5243-5258 is not listed on IDEAS
    4. Li, Bo & Arreola-Risa, Antonio, 2017. "Financial risk, inventory decision and process improvement for a firm with random capacity," European Journal of Operational Research, Elsevier, vol. 260(1), pages 183-194.
    5. Wagner, Stephan M. & Mizgier, Kamil J. & Papageorgiou, Stylianos, 2017. "Operational disruptions and business cycles," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 66-78.
    6. repec:gam:jsusta:v:8:y:2016:i:3:p:250:d:65341 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:245:y:2015:i:1:p:320-332. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/eor .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.