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Big Data Analytics for Rapid, Impactful, Sustained, and Efficient (RISE) Humanitarian Operations

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  • Jayashankar M. Swaminathan

Abstract

There has been a significant increase in the scale and scope of humanitarian efforts over the last decade. Humanitarian operations need to be—rapid, impactful, sustained, and efficient (RISE). Big data offers many opportunities to enable RISE humanitarian operations. In this study, we introduce the role of big data in humanitarian settings and discuss data streams which could be utilized to develop descriptive, prescriptive, and predictive models to significantly impact the lives of people in need.

Suggested Citation

  • Jayashankar M. Swaminathan, 2018. "Big Data Analytics for Rapid, Impactful, Sustained, and Efficient (RISE) Humanitarian Operations," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1696-1700, September.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:9:p:1696-1700
    DOI: 10.1111/poms.12840
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    Cited by:

    1. Bell, David & Lycett, Mark & Marshan, Alaa & Monaghan, Asmat, 2021. "Exploring future challenges for big data in the humanitarian domain," Journal of Business Research, Elsevier, vol. 131(C), pages 453-468.
    2. Safinaz H. Abourokbah & Reem M. Mashat & Mohammad Asif Salam, 2023. "Role of Absorptive Capacity, Digital Capability, Agility, and Resilience in Supply Chain Innovation Performance," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    3. Chaurasia, Shailly & Pati, Rupesh Kumar & Padhi, Sidhartha S. & Gavirneni, Srinagesh, 2024. "Is localization better than globalization for sustainability? Evidence from the nutraceuticals industry for managing malnutrition in India," International Journal of Production Economics, Elsevier, vol. 268(C).
    4. Dusan Repík & Pavel Foltin, 2023. "Analysis Of Problems Of The Performance Management Of Humanitarian Supply Chains," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 23, pages 217-238.
    5. Eunae Yoo & Elliot Rabinovich & Bin Gu, 2020. "The Growth of Follower Networks on Social Media Platforms for Humanitarian Operations," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2696-2715, December.
    6. Josip Marić & Carlos Galera-Zarco & Marco Opazo-Basáez, 2022. "The emergent role of digital technologies in the context of humanitarian supply chains: a systematic literature review," Annals of Operations Research, Springer, vol. 319(1), pages 1003-1044, December.
    7. Erica L. Plambeck & Kamalini Ramdas, 2020. "Alleviating Poverty by Empowering Women Through Business Model Innovation: Manufacturing & Service Operations Management Insights and Opportunities," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 123-134, January.
    8. Yuli Zhang & Amber R. Richter & Jeyaveerasingam George Shanthikumar & Zuo‐Jun Max Shen, 2022. "Dynamic Inventory Relocation in Disaster Relief," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1052-1070, March.
    9. Nur Sunar & Jayashankar M. Swaminathan, 2022. "Socially relevant and inclusive operations management," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4379-4392, December.
    10. Alekh Gour & Shikha Aggarwal & Subodha Kumar, 2022. "Lending ears to unheard voices: An empirical analysis of user‐generated content on social media," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2457-2476, June.
    11. Suyuan Luo & Tsan‐Ming Choi, 2022. "E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2107-2126, May.
    12. Sunil Mithas & Zhi‐Long Chen & Terence J.V. Saldanha & Alysson De Oliveira Silveira, 2022. "How will artificial intelligence and Industry 4.0 emerging technologies transform operations management?," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4475-4487, December.
    13. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    14. Labro, Eva & Lang, Mark & Omartian, James D., 2023. "Predictive analytics and centralization of authority," Journal of Accounting and Economics, Elsevier, vol. 75(1).
    15. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    16. Jónas Oddur Jónasson & Kamalini Ramdas & Alp Sungu, 2022. "Social impact operations at the global base of the pyramid," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4364-4378, December.
    17. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    18. Gloria Urrea & Eunae Yoo, 2023. "The role of volunteer experience on performance on online volunteering platforms," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 416-433, February.
    19. Mengshi Lu & Zuo‐Jun Max Shen, 2021. "A Review of Robust Operations Management under Model Uncertainty," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1927-1943, June.

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