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Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations

Author

Listed:
  • Shivam Gupta

    (NEOMA - Neoma Business School)

  • Sachin Modgil

    (IMI Kolkata - International Management Institute)

  • Ajay Kumar

    (EM - EMLyon Business School)

  • Uthayasankar Sivarajah

    (University of Bradford)

  • Zahir Irani

    (University of Bradford)

Abstract

"Natural disasters are often unpredictable and therefore there is a need for quick and effective response to save lives and infrastructure. Hence, this study is aimed at achieving timely, anticipated and effective response throughout the cycle of a disaster, extreme weather and emergency operations management with the help of advanced technologies. This study proposes a novel, evidence-based framework (4-AIDE) that highlights the role of artificial intelligence (AI) and cloud-based collaborative platforms in disaster, extreme weather and emergency situations. A qualitative approach underpinned by organizational information processing theory (OIPT) is employed to design, develop and conduct semi-structured interviews with 33 respondents having experience in AI and cloud computing industries during emergency and extreme weather situations. For analysing the collected data, axial, open and selective coding is used that further develop themes, propositions and an evidence-based framework. The study findings indicate that AI and cloud-based collaborative platforms offer a structured and logical approach to enable two-way, algorithm-based communication to collect, analyse and design effective management strategies for disaster and extreme weather situations. Managers of public systems or businesses can collect and analyse data to predict possible outcomes and take necessary actions in an extreme weather situation. Communities and societies can be more resilient by transmitting and receiving data to AI and cloud-based collaborative platforms. These actions can also help policymakers identify critical pockets and guide administration for their necessary preparation for unexpected, extreme weather, and emergency events."

Suggested Citation

  • Shivam Gupta & Sachin Modgil & Ajay Kumar & Uthayasankar Sivarajah & Zahir Irani, 2022. "Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations," Post-Print hal-04325638, HAL.
    • Handle: RePEc:hal:journl:hal-04325638
    DOI: 10.1016/j.ijpe.2022.108642
    Note: View the original document on HAL open archive server: https://hal.science/hal-04325638v1
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    1. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Giannakis, Mihalis & Foropon, Cyril, 2023. "Data-driven digital transformation and the implications for antifragility in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 266(C).
    2. Liang, Chen & Zhu, Minghao & Lee, Peter K.C. & Cheng, T.C.E. & Yeung, Andy C.L., 2024. "Combating extreme weather through operations management: Evidence from a natural experiment in China," International Journal of Production Economics, Elsevier, vol. 267(C).
    3. Jiang, Jiehui & Ma, Jie & Chen, Xiaojing, 2024. "Multi-regional collaborative mechanisms in emergency resource reserve and pre-dispatch design," International Journal of Production Economics, Elsevier, vol. 270(C).

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    Keywords

    Artificial intelligence; Cloud technologies; Disaster management; Extreme weather; Organizational information processing theory;
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