IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v22y2020i1p158-169.html
   My bibliography  Save this article

Data Analytics in Operations Management: A Review

Author

Listed:
  • Velibor V. Mišić

    (Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095;)

  • Georgia Perakis

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

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. Mišić & Georgia Perakis, 2020. "Data Analytics in Operations Management: A Review," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 158-169, January.
    • Handle: RePEc:inm:ormsom:v:22:y:2020:i:1:p:158-169
    DOI: 10.1287/msom.2019.0805
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/msom.2019.0805
    Download Restriction: no
    File URL: https://libkey.io/10.1287/msom.2019.0805?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
    2. Ravindra K. Ahuja & James B. Orlin, 2001. "Inverse Optimization," Operations Research, INFORMS, vol. 49(5), pages 771-783, October.
    3. Jacob Feldman & Alice Paul & Huseyin Topaloglu, 2019. "Technical Note—Assortment Optimization with Small Consideration Sets," Operations Research, INFORMS, vol. 67(5), pages 1283-1299, September.
    4. Negin Golrezaei & Hamid Nazerzadeh & Paat Rusmevichientong, 2014. "Real-Time Optimization of Personalized Assortments," Management Science, INFORMS, vol. 60(6), pages 1532-1551, June.
    5. Anil Aswani & Zuo-Jun Max Shen & Auyon Siddiq, 2019. "Data-Driven Incentive Design in the Medicare Shared Savings Program," Operations Research, INFORMS, vol. 67(4), pages 1002-1026, July.
    6. Long He & Ho-Yin Mak & Ying Rong & Zuo-Jun Max Shen, 2017. "Service Region Design for Urban Electric Vehicle Sharing Systems," Manufacturing & Service Operations Management, INFORMS, vol. 19(2), pages 309-327, May.
    7. Vivek F. Farias & Srikanth Jagabathula & Devavrat Shah, 2013. "A Nonparametric Approach to Modeling Choice with Limited Data," Management Science, INFORMS, vol. 59(2), pages 305-322, December.
    8. Dimitris Bertsimas & Vivek F. Farias & Nikolaos Trichakis, 2013. "Fairness, Efficiency, and Flexibility in Organ Allocation for Kidney Transplantation," Operations Research, INFORMS, vol. 61(1), pages 73-87, February.
    9. Pavithra Harsha & Shivaram Subramanian & Joline Uichanco, 2019. "Dynamic Pricing of Omnichannel Inventories," Service Science, INFORMS, vol. 21(1), pages 47-65, January.
    10. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
    11. Fernando Bernstein & Sajad Modaresi & Denis Sauré, 2019. "A Dynamic Clustering Approach to Data-Driven Assortment Personalization," Management Science, INFORMS, vol. 67(5), pages 2095-2115, May.
    12. Assaf Avrahami & Yale T. Herer & Retsef Levi, 2014. "Matching Supply and Demand: Delayed Two-Phase Distribution at Yedioth Group—Models, Algorithms, and Information Technology," Interfaces, INFORMS, vol. 44(5), pages 445-460, October.
    13. Sandeep Rath & Kumar Rajaram & Aman Mahajan, 2017. "Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application," Operations Research, INFORMS, vol. 65(6), pages 1460-1478, December.
    14. Jose Blanchet & Guillermo Gallego & Vineet Goyal, 2016. "A Markov Chain Approximation to Choice Modeling," Operations Research, INFORMS, vol. 64(4), pages 886-905, August.
    15. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    16. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
    17. Dimitris Bertsimas & Allison O’Hair & Stephen Relyea & John Silberholz, 2016. "An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer," Management Science, INFORMS, vol. 62(5), pages 1511-1531, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Velibor V. Miv{s}i'c & Georgia Perakis, 2019. "Data Analytics in Operations Management: A Review," Papers 1905.00556, arXiv.org.
    2. Francis de Véricourt & Georgia Perakis, 2020. "Frontiers in Service Science: The Management of Data Analytics Services: New Challenges and Future Directions," Service Science, INFORMS, vol. 12(4), pages 121-129, December.
    3. Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.
    4. Nathan Kallus & Madeleine Udell, 2020. "Dynamic Assortment Personalization in High Dimensions," Operations Research, INFORMS, vol. 68(4), pages 1020-1037, July.
    5. Ilan Lobel, 2021. "Revenue Management and the Rise of the Algorithmic Economy," Management Science, INFORMS, vol. 67(9), pages 5389-5398, September.
    6. 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.
    7. Qi Feng & J. George Shanthikumar, 2022. "Developing operations management data analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4544-4557, December.
    8. Yi-Chun Chen & Velibor V. Mišić, 2022. "Decision Forest: A Nonparametric Approach to Modeling Irrational Choice," Management Science, INFORMS, vol. 68(10), pages 7090-7111, October.
    9. Kameng Nip & Zhenbo Wang & Zizhuo Wang, 2021. "Assortment Optimization under a Single Transition Choice Model," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2122-2142, July.
    10. Antoine Désir & Vineet Goyal & Danny Segev & Chun Ye, 2020. "Constrained Assortment Optimization Under the Markov Chain–based Choice Model," Management Science, INFORMS, vol. 66(2), pages 698-721, February.
    11. Sheng Liu & Long He & Zuo-Jun Max Shen, 2021. "On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors," Management Science, INFORMS, vol. 67(7), pages 4095-4119, July.
    12. Ali Aouad & Daniela Saban, 2023. "Online Assortment Optimization for Two-Sided Matching Platforms," Management Science, INFORMS, vol. 69(4), pages 2069-2087, April.
    13. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    14. Çömez-Dolgan, Nagihan & Fescioglu-Unver, Nilgun & Cephe, Ecem & Şen, Alper, 2021. "Capacitated strategic assortment planning under explicit demand substitution," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1120-1138.
    15. Guillermo Gallego & Gerardo Berbeglia, 2021. "The Limits of Personalization in Assortment Optimization," Papers 2109.14861, arXiv.org, revised Jun 2024.
    16. Xi Chen & Chao Shi & Yining Wang & Yuan Zhou, 2021. "Dynamic Assortment Planning Under Nested Logit Models," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 85-102, January.
    17. Xi Chen & Zachary Owen & Clark Pixton & David Simchi-Levi, 2022. "A Statistical Learning Approach to Personalization in Revenue Management," Management Science, INFORMS, vol. 68(3), pages 1923-1937, March.
    18. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
    19. Crönert, Tobias & Martin, Layla & Minner, Stefan & Tang, Christopher S., 2024. "Inverse optimization of integer programming games for parameter estimation arising from competitive retail location selection," European Journal of Operational Research, Elsevier, vol. 312(3), pages 938-953.
    20. Turgay Ayer & Can Zhang & Anthony Bonifonte & Anne C. Spaulding & Jagpreet Chhatwal, 2019. "Prioritizing Hepatitis C Treatment in U.S. Prisons," Operations Research, INFORMS, vol. 67(3), pages 853-873, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:22:y:2020:i:1:p:158-169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.