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American Journal of Business and Operations Research

Volume 0 , Issue 2 , PP: 83-96, 2019 | Cite this article as | XML | Html | PDF | Full Length Article

Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making

Irina V. Pustokhina 1 * , Denis A. Pustokhin 2

  • 1 Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow 117997, Russia - (Pustohina.IV@rea.ru)
  • 2 Department of Logistics, State University of Management , Moscow 109542, Russia - (da_pustohin@guu.ru)

    • Doi: https://doi.org/10.54216/AJBOR.000203

    Abstract

    The impact of climate change has made responsible risk management a major research topic during the past 20 years. In conjunction with societal problems that affect the economies and cultures in which they function, industrial risks can release dangerous pollutants into the natural world. Advances in information and communication technology, particularly big data analytics, can contribute to the creation of fresh perspectives that enable the detection of business risks whose operations are unstable and the implementation of remedial actions. Although risk management has been the subject of numerous research, there are few that examine the impact of BDA. This study strives to offer a big data analytic framework that integrates a pipeline of statistical testing, data visualization, and machine learning algorithms to interpret market information. The applicability of our framework in recognizing and managing risks is demonstrated through a case study of the global commodity market. Extensive proof-of-concept experimentations validated the efficiency and effectiveness of the argued framework by providing useful insights about market behavior, which can lead the decision-making process to get informed risk management.

    Keywords :

    [1]  ,   ,   , Dicuonzo, G., Galeone, G., Zappimbulso, E., & , Dell'Atti, V. (2019). Risk management 4.0: The role of big data analytics in the bank sector.  , International Journal of Economics and Financial Issues,  , 9(6), 40-47.

    [2]  ,   ,   , Choi, T. M., Chan, H. K., & , Yue, X. (2016). Recent development in big data analytics for business operations and risk management.  , IEEE transactions on cybernetics,  , 47(1), 81-92.

    [3]  ,   ,   , Popovič, A., Hackney, R., Tassabehji, R., & , Castelli, M. (2018). The impact of big data analytics on firms&rsquo , high value business performance.  , Information Systems Frontiers,  , 20, 209-222.

    [4]  ,   ,   , Awwad, M., Kulkarni, P., Bapna, R., & , Marathe, A. (2018, September). Big data analytics in supply chain: a literature review. In  , Proceedings of the international conference on industrial engineering and operations management  , (Vol. 2018, pp. 418-25).

    [5]  ,   ,   , Malik, P. (2013). Governing big data: principles and practices.  , IBM Journal of Research and Development,  , 57(3/4), 1-1.

    [6]  ,   ,   , Baaziz, A., & , Quoniam, L. (2014). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry.  , arXiv preprint arXiv:1412.0755.

    [7]  ,   ,   , Ferraris, A., Mazzoleni, A., Devalle, A., & , Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance.  , Management Decision,  , 57(8), 1923-1936.

    [8]  ,   ,   , Baaziz, A., & , Quoniam, L. (2015). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry.  , Baaziz, A., & , Quoniam, L.(2013). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. International Journal of Innovation-IJI,  , 1(1), 19-25.

    [9]  ,   ,   , Arunachalam, D., Kumar, N., & , Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice.  , Transportation Research Part E: Logistics and Transportation Review,  , 114, 416-436.

    [10] Wang, J., Zhang, W., Shi, Y., Duan, S., & , Liu, J. (2018). Industrial big data analytics: challenges, methodologies, and applications.  , arXiv preprint arXiv:1807.01016.

    [11] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & , Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.

    [12] Ventola, C. L. (2018). Big data and pharmacovigilance: data mining for adverse drug events and interactions. Pharmacy and therapeutics, 43(6), 340.

    [13] LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & , Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT sloan management review.

    [14] Cerchiello, P., & , Giudici, P. (2016). Big data analysis for financial risk management. Journal of Big Data, 3(1), 1-12.

    [15] Goel, P., Datta, A., & , Mannan, M. S. (2017, December). Application of big data analytics in process safety and risk management. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 1143-1152). IEEE.

    [16] Wang, G., Gunasekaran, A., Ngai, E. W., & , Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110.

    References

    [1]    Dicuonzo, G., Galeone, G., Zappimbulso, E., & Dell'Atti, V. (2019). Risk management 4.0: The role of big data analytics in the bank sector. International Journal of Economics and Financial Issues, 9(6), 40-47.

    [2]    Choi, T. M., Chan, H. K., & Yue, X. (2016). Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), 81-92.

    [3]    Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20, 209-222.

    [4]    Awwad, M., Kulkarni, P., Bapna, R., & Marathe, A. (2018, September). Big data analytics in supply chain: a literature review. In Proceedings of the international conference on industrial engineering and operations management (Vol. 2018, pp. 418-25).

    [5]    Malik, P. (2013). Governing big data: principles and practices. IBM Journal of Research and Development, 57(3/4), 1-1.

    [6]    Baaziz, A., & Quoniam, L. (2014). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. arXiv preprint arXiv:1412.0755.

    [7]    Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936.

    [8]    Baaziz, A., & Quoniam, L. (2015). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. Baaziz, A., & Quoniam, L.(2013). How to use Big Data technologies to optimize operations in Upstream Petroleum Industry. International Journal of Innovation-IJI, 1(1), 19-25.

    [9]    Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.

    [10] Wang, J., Zhang, W., Shi, Y., Duan, S., & Liu, J. (2018). Industrial big data analytics: challenges, methodologies, and applications. arXiv preprint arXiv:1807.01016.

    [11] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.

    [12] Ventola, C. L. (2018). Big data and pharmacovigilance: data mining for adverse drug events and interactions. Pharmacy and therapeutics, 43(6), 340.

    [13] LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT sloan management review.

    [14] Cerchiello, P., & Giudici, P. (2016). Big data analysis for financial risk management. Journal of Big Data, 3(1), 1-12.

    [15] Goel, P., Datta, A., & Mannan, M. S. (2017, December). Application of big data analytics in process safety and risk management. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 1143-1152). IEEE.

    [16] Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International journal of production economics, 176, 98-110.

    Cite This Article As :
    V., Irina. , A., Denis. Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research, vol. , no. , 2019, pp. 83-96. DOI: https://doi.org/10.54216/AJBOR.000203
    V., I. A., D. (2019). Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research, (), 83-96. DOI: https://doi.org/10.54216/AJBOR.000203
    V., Irina. A., Denis. Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research , no. (2019): 83-96. DOI: https://doi.org/10.54216/AJBOR.000203
    V., I. , A., D. (2019) . Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research , () , 83-96 . DOI: https://doi.org/10.54216/AJBOR.000203
    V. I. , A. D. [2019]. Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making. American Journal of Business and Operations Research. (): 83-96. DOI: https://doi.org/10.54216/AJBOR.000203
    V., I. A., D. "Operational Risk Management: Integrating Big Data Analytics for Proactive Decision-Making," American Journal of Business and Operations Research, vol. , no. , pp. 83-96, 2019. DOI: https://doi.org/10.54216/AJBOR.000203

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