IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6745-d291706.html
   My bibliography  Save this article

Validation of Dynamic Risk Analysis Supporting Integrated Operations Across Systems

Author

Listed:
  • Shenae Lee

    (Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology NTNU, S.P. Andersens veg 3, 7031 Trondheim, Norway)

  • Gabriele Landucci

    (Department of Civil and Industrial Engineering, University of Pisa, Largo Lucio Lazzarino 2, 56126 Pisa, Italy)

  • Genserik Reniers

    (Faculty of Applied Economics, University of Antwerp Operations Research Group ANT/OR, 2000 Antwerp, Belgium
    Center for Corporate Sustainability (CEDON), HUB, KULeuven, 1000 Brussels, Belgium
    Safety Science Group, TU Delft, 2628 BX Delft, The Netherlands)

  • Nicola Paltrinieri

    (Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology NTNU, S.P. Andersens veg 3, 7031 Trondheim, Norway)

Abstract

Dynamic risk analysis (DRA) is a novel industrial approach that aims to capture changes in operational conditions over time and quantify their effect on risk. This aspect may be advantageous for providing insight into the causal factors that have substantial risk contributions and supporting decisions related to risk control. Some DRA methods were developed by the oil and gas industry to support the integration of work processes and the cooperation across virtual clusters, e.g., between offshore and onshore systems and/or oil company and supplier. However, DRA has not been extensively adopted and limited attention is given to its validity in practical applications. The objective of this article is to illustrate how this validity can be established based on common validation approaches for risk analysis. The case study focuses on a DRA method named risk barometer that was developed to support integrated operations across the oil and gas industrial systems. The outcome of this study may serve as a basis for the validation of other DRA methods, the use of DRA in practical cases, and ultimately the achievement of integrated operations (IO) capabilities.

Suggested Citation

  • Shenae Lee & Gabriele Landucci & Genserik Reniers & Nicola Paltrinieri, 2019. "Validation of Dynamic Risk Analysis Supporting Integrated Operations Across Systems," Sustainability, MDPI, vol. 11(23), pages 1-25, November.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6745-:d:291706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/23/6745/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/23/6745/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    2. Didier SORNETTE, 2009. "Dragon-Kings, Black Swans and the Prediction of Crises," Swiss Finance Institute Research Paper Series 09-36, Swiss Finance Institute.
    3. D. Sornette, "undated". "Dragon-Kings, Black Swans and the Prediction of Crises," Working Papers CCSS-09-005, ETH Zurich, Chair of Systems Design.
    4. Rae, Andrew & Alexander, Rob & McDermid, John, 2014. "Fixing the cracks in the crystal ball: A maturity model for quantitative risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 67-81.
    5. Alvin M. Weinberg, 1981. "Reflections on Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 5-7, March.
    6. Robert B. Cumming, 1981. "Is Risk Assessment A Science?," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 1-3, March.
    7. Nicola Paltrinieri & Faisal Khan & Valerio Cozzani, 2015. "Coupling of advanced techniques for dynamic risk management," Journal of Risk Research, Taylor & Francis Journals, vol. 18(7), pages 910-930, August.
    8. Aven, Terje & Krohn, Bodil S., 2014. "A new perspective on how to understand, assess and manage risk and the unforeseen," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 1-10.
    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. Catalin Popescu & Sorin Alexandru Gheorghiu, 2021. "Economic Analysis and Generic Algorithm for Optimizing the Investments Decision-Making Process in Oil Field Development," Energies, MDPI, vol. 14(19), pages 1-24, September.
    2. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sven Ove Hansson & Terje Aven, 2014. "Is Risk Analysis Scientific?," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1173-1183, July.
    2. Glette-Iversen, Ingrid & Aven, Terje, 2021. "On the meaning of and relationship between dragon-kings, black swans and related concepts," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    4. Darrell Jiajie Tay & Chung-I Chou & Sai-Ping Li & Shang You Tee & Siew Ann Cheong, 2016. "Bubbles Are Departures from Equilibrium Housing Markets: Evidence from Singapore and Taiwan," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-13, November.
    5. Terje Aven, 2018. "An Emerging New Risk Analysis Science: Foundations and Implications," Risk Analysis, John Wiley & Sons, vol. 38(5), pages 876-888, May.
    6. M. Wili'nski & A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Structural and topological phase transitions on the German Stock Exchange," Papers 1301.2530, arXiv.org, revised Jul 2013.
    7. Wosnitza, Jan Henrik & Leker, Jens, 2014. "Can log-periodic power law structures arise from random fluctuations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 228-250.
    8. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    9. Neil Johnson & Guannan Zhao & Eric Hunsader & Jing Meng & Amith Ravindar & Spencer Carran & Brian Tivnan, 2012. "Financial black swans driven by ultrafast machine ecology," Papers 1202.1448, arXiv.org.
    10. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    11. J. Lorenz & S. Battiston & F. Schweitzer, 2009. "Systemic risk in a unifying framework for cascading processes on networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 441-460, October.
    12. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.
    13. Terje Aven, 2012. "Foundational Issues in Risk Assessment and Risk Management," Risk Analysis, John Wiley & Sons, vol. 32(10), pages 1647-1656, October.
    14. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    15. A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Dynamic structural and topological phase transitions on the Warsaw Stock Exchange: A phenomenological approach," Papers 1301.6506, arXiv.org.
    16. F. A. Nava & V. H. Márquez-Ramírez & F. R. Zúñiga & C. Lomnitz, 2017. "Gutenberg–Richter b-value determination and large-magnitudes sampling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 1-11, May.
    17. Petr Geraskin & Dean Fantazzini, 2013. "Everything you always wanted to know about log-periodic power laws for bubble modeling but were afraid to ask," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 366-391, May.
    18. Jiang, Zhi-Qiang & Xie, Wen-Jie & Zhou, Wei-Xing, 2014. "Testing the weak-form efficiency of the WTI crude oil futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 235-244.
    19. Christian Hugo Hoffmann & Charles Djordjevic, 2020. "Complexity, Power Laws and a Humean Argument in Risk Management: The Fundamental Inadequacy of Probability Theory as a Foundation for Modeling Complex Risk in Banking," Homo Oeconomicus: Journal of Behavioral and Institutional Economics, Springer, vol. 37(3), pages 155-182, December.
    20. Domino, Krzysztof, 2020. "Multivariate cumulants in outlier detection for financial data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).

    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:gam:jsusta:v:11:y:2019:i:23:p:6745-:d:291706. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.