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

By Benjamin T. Hazen, Christopher A. Boone, Jeremy D. Ezell, L. A. Jones‐Farmer. 2014

845 citations recorded by the source corpus.

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.

Subfields: Information Systems, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/37095b714dad5895d946b1f8435a3a38dee1be8b

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Citations in the corpus

Debating big data: A literature review on realizing value from big data (2017, background): “timely, complete, or precise enough (Clarke, 2016), this can lead to decisions based on wrong insights or affect the quality of data-driven products and services (Hazen et al., 2014).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, neutral): “Further, if the quality of the data is not controlled properly then the decision-making process guided by BDPA may even have negative consequences (Hazen et al., 2014; Janssen et al., 2017).”
Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience (2019, background): “(2014) we argue that: data quality (Hazen et al. 2014; Corte-Real et al. 2019) and technological innovation (Singh, 2011; Chen et al.”
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions (2019, background): “Keywords: big data analytics; smart manufacturing; servitization; sustainable production; conceptual framework; product lifecycle Abbreviations ABC Artificial bee colony IoT Internet of things ABS Agent-based system IT Information technology ACO Ant colony optimization JIT Just-in-time AD Anomaly de”
Big data analytics capability in supply chain agility (2019, positive): “In the context of supply chain management, big data analytics capability enables to firms to examine alternatives related to supply and demand uncertainties (Waller and Fawcett, 2013; Hazen et al. 2014; Wang et al. 2016).”
A Survey on Data Quality: Classifying Poor Data (2015, background): “MAPPING DATA QUALITY PROBLEMS Problems regarding data quality, (i.e., dirty data [18]), have been analyzed in previous research and are present in different domains, including healthcare [50] , data science [51], or Cyber-Physical Systems [52], just to name a few.”
Management challenges in creating value from business analytics (2017, background): “Schoenherr and Speier-Pero (2015) provided a holistic view regarding DPB’s current state and future potential for LSCM while at a more tactical level Hazen et al. (2014) discussed data quality and Bendoly (2016) proposed how to better visualize data through the use of semiotics for sense making.”
Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook (2020, background): “…Intrinsic , which refers to the characteristics that are native to the data itself, including timeliness, completeness, accuracy and consistency [92]; (2) Contextual , meaning the attributes that are dependent on the context of the task at hand such as relevancy and quantity; (3)…”