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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

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  • Hazen, Benjamin T.
  • Boone, Christopher A.
  • Ezell, Jeremy D.
  • Jones-Farmer, L. Allison

Abstract

Today׳s supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance. However, management decisions informed by the use of these data analytic methods are only as good as the data on which they are based. In this paper, we introduce the data quality problem in the context of supply chain management (SCM) and propose methods for monitoring and controlling data quality. In addition to advocating for the importance of addressing data quality in supply chain research and practice, we also highlight interdisciplinary research topics based on complementary theory.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:154:y:2014:i:c:p:72-80
    DOI: 10.1016/j.ijpe.2014.04.018
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    References listed on IDEAS

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