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An enhanced data-analytic framework for integrating risk management and performance management

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  • Thekdi, Shital
  • Aven, Terje

Abstract

There is increasing interest for agencies and industries to develop risk management processes for a wide variety of applications. Traditional risk management processes are motivated by controlling risk and avoiding losses. In contrast, other organizational processes focus on managing performance and value generation. In this paper we argue that risk management also adds an important contribution to these processes. However, this requires “proper†risk management extending beyond narrow safety oriented perspectives built on quantitative risk analysis and tolerability/acceptance criteria. There is need for a broad risk-performance framework with uncertainty being a main component of risk, and where knowledge and surprises are adequately reflected. In the paper we present and discuss such a framework. The framework is developed on the basis of an analysis of combinations of different risk management and performance management practices/policies. We show how the risk and performance management processes can be improved by proper risk conceptualization and a holistic thinking on how to develop and use goals in the organization, how to balance different concerns, and consider the need for agility – “sensitivity to operations†, as well as how to give weight to vulnerabilities, resilience, and antifragility.

Suggested Citation

  • Thekdi, Shital & Aven, Terje, 2016. "An enhanced data-analytic framework for integrating risk management and performance management," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 277-287.
  • Handle: RePEc:eee:reensy:v:156:y:2016:i:c:p:277-287
    DOI: 10.1016/j.ress.2016.07.010
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    References listed on IDEAS

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    1. Aven, Terje, 2011. "On the new ISO guide on risk management terminology," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 719-726.
    2. Karl E. Weick & Kathleen M. Sutcliffe, 2006. "Mindfulness and the Quality of Organizational Attention," Organization Science, INFORMS, vol. 17(4), pages 514-524, August.
    3. 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.
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    3. Ena Pecina & Danijela Miloš Sprčić & Ivana Dvorski Lacković, 2022. "Qualitative Analysis of Enterprise Risk Management Systems in the Largest European Electric Power Companies," Energies, MDPI, vol. 15(15), pages 1-19, July.
    4. P. Pablo Poveda-Orjuela & J. Carlos García-Díaz & Alexander Pulido-Rojano & Germán Cañón-Zabala, 2020. "Parameterization, Analysis, and Risk Management in a Comprehensive Management System with Emphasis on Energy and Performance (ISO 50001: 2018)," Energies, MDPI, vol. 13(21), pages 1-43, October.
    5. Hernandez-Perdomo, Elvis & Guney, Yilmaz & Rocco, Claudio M., 2019. "A reliability model for assessing corporate governance using machine learning techniques," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 220-231.
    6. Ivana Dvorski Lacković & Nataša Kurnoga & Danijela Miloš Sprčić, 2022. "Three-factor model of Enterprise Risk Management implementation: exploratory study of non-financial companies," Risk Management, Palgrave Macmillan, vol. 24(2), pages 101-122, June.
    7. P. Pablo Poveda-Orjuela & J. Carlos García-Díaz & Alexander Pulido-Rojano & Germán Cañón-Zabala, 2019. "ISO 50001: 2018 and Its Application in a Comprehensive Management System with an Energy-Performance Focus," Energies, MDPI, vol. 12(24), pages 1-33, December.

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    Keywords

    Risk management; Performance management;

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