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Data Analytics in Operations Management: A Review

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  • Velibor V. Miv{s}i'c
  • Georgia Perakis

Abstract

Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability of data and recent advances in machine learning and optimization methodologies, there has been an increasing application of data analytics to problems in operations management. In this paper, we review recent applications of data analytics to operations management, in three major areas -- supply chain management, revenue management and healthcare operations -- and highlight some exciting directions for the future.

Suggested Citation

  • Velibor V. Miv{s}i'c & Georgia Perakis, 2019. "Data Analytics in Operations Management: A Review," Papers 1905.00556, arXiv.org.
    • Handle: RePEc:arx:papers:1905.00556
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    References listed on IDEAS

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    Cited by:

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    2. van Staden, Heletjé E. & Deprez, Laurens & Boute, Robert N., 2022. "A dynamic “predict, then optimize” preventive maintenance approach using operational intervention data," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1079-1096.
    3. Tava Lennon Olsen & Brian Tomlin, 2020. "Industry 4.0: Opportunities and Challenges for Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 113-122, January.
    4. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    5. Haifei Yu & Shanshan Zheng & Hao Wu, 2023. "User Privacy Awareness, Incentive and Data Supply Chain Pricing Strategy," Sustainability, MDPI, vol. 15(4), pages 1-24, February.

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