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Back in business: operations research in support of big data analytics for operations and supply chain management

Author

Listed:
  • Benjamin T. Hazen

    (Air Force Institute of Technology)

  • Joseph B. Skipper

    (Georgia Southern University)

  • Christopher A. Boone

    (Georgia Southern University)

  • Raymond R. Hill

    (Air Force Institute of Technology)

Abstract

Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment between the work of OR scholars and the needs of practicing managers, especially those in the field of operations and supply chain management where data-driven decision-making is a key component of most job descriptions. In this paper, we attempt to address this misalignment. We examine both applied and scholarly applications of OR-based big data analytical tools and techniques within an operations and supply chain management context to highlight their future potential in this domain. This paper contributes by providing suggestions for scholars, educators, and practitioners that aid to illustrate how OR can be instrumental in solving big data analytics problems in support of operations and supply chain management.

Suggested Citation

  • Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2226-0
    DOI: 10.1007/s10479-016-2226-0
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    1. Surya Sahoo & Seongbae Kim & Byung-In Kim & Bob Kraas & Alexander Popov, 2005. "Routing Optimization for Waste Management," Interfaces, INFORMS, vol. 35(1), pages 24-36, February.
    2. Matthew Liberatore & Wenhong Luo, 2011. "INFORMS and the Analytics Movement: The View of the Membership," Interfaces, INFORMS, vol. 41(6), pages 578-589, December.
    3. Jeanne Coulibaly & Theodore Nouhoheflin & Casimir Aitchedji & Maiyaki Damisa & Stephen D'Alessandro & Dieudonne Baributsa & Jess Lowenberg-DeBoer, 2012. "PURDUE IMPROVED COWPEA STORAGE (PICS) SUPPLY CHAIN STUDY Abstract:The Purdue Improved Cowpea Storage (PICS) project was launched in 2007 with a grant from the Bill and Melinda Gates Foundation. The pr," Working Papers 12-4, Purdue University, College of Agriculture, Department of Agricultural Economics.
    4. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    5. Thomas A. Grossman, 2001. "Causes of the Decline of the Business School Management Science Course," INFORMS Transactions on Education, INFORMS, vol. 1(2), pages 51-61, January.
    6. Nihat Altintas & Michael Trick, 2014. "A data mining approach to forecast behavior," Annals of Operations Research, Springer, vol. 216(1), pages 3-22, May.
    7. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    8. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
    9. Hein Fleuren & Chris Goossens & Marco Hendriks & Marie-Christine Lombard & Ineke Meuffels & John Poppelaars, 2013. "Supply Chain–Wide Optimization at TNT Express," Interfaces, INFORMS, vol. 43(1), pages 5-20, February.
    10. Singh, Gaurav & Sier, David & Ernst, Andreas T. & Gavriliouk, Olena & Oyston, Rob & Giles, Tracey & Welgama, Palitha, 2012. "A mixed integer programming model for long term capacity expansion planning: A case study from The Hunter Valley Coal Chain," European Journal of Operational Research, Elsevier, vol. 220(1), pages 210-224.
    11. Matthew J. Liberatore & Wenhong Luo, 2010. "The Analytics Movement: Implications for Operations Research," Interfaces, INFORMS, vol. 40(4), pages 313-324, August.
    12. Harold Larnder, 1984. "OR Forum—The Origin of Operational Research," Operations Research, INFORMS, vol. 32(2), pages 465-476, April.
    13. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    14. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
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