Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management
Open accessAbstract
We illuminate the myriad of opportunities for research where supply chain management ( SCM ) intersects with data science, predictive analytics, and big data, collectively referred to as DPB . We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM . We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB .
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Predictive analytics is concerned with forecasting and statistical modelling to determine future possibilities based on supervised, unsupervised, and semi-supervised learning models.
“This analytics is concerned with forecasting and statistical modelling to determine the future possibilities based on supervised, unsupervised, and semi-supervised learning models (Joseph & Johnson, 2013;Rehman et al., 2016;Waller & Fawcett, 2013).”
From Critical analysis of Big Data challenges and analytical methods (2017) · cites this paper as Waller & Fawcett, 2013
Data-driven decisions and analytics capabilities are particularly valuable to organizations in terms of fostering process innovation, supply chain design management, knowledge management and quality of decision-making.
“Indeed, data-driven decisions and analytics capabilities are particularly valuable to organizations in terms of fostering process innovation (Wu, Hitt and Lou, 2020;Wu, Lou and Hitt, 2019), supply chain design management (e.g.Waller and Fawcett, 2013), knowledge management (Khan and Vorley, 2017) and quality of decision-making (cf.Shamim et al., 2019).”
From COVID‐19 Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions (2021) · cites this paper as Waller and Fawcett, 2013
Such capabilities can enable firms to seize emerging opportunities or avoid threats through real-time process orchestration, dynamic resource allocation, and financial risk assessment.
“Such capabilities can, in turn, enable firms to seize emerging opportunities or avoid threats through real-time process orchestration [87], dynamic resource allocation, and financial risk assessment [92,93].”
From Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020) · cites this paper as [87]
BDACs can contribute toward identifying bottlenecks or other potential hazards in supply chains.
“With regards to external process sensing, BDACs can contribute toward identifying bottlenecks or other potential hazards in supply chains [87]”
From Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020) · cites this paper as [87]
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- Authors
- Waller, M. A., & Fawcett, S. E.
- Affiliations
- University of Arkansas at Fayetteville (US)
- Weber State University (US)
- Corresponding author
- Waller, Matthew A.
- Venue
- Journal of Business Logistics
- Publisher
- Wiley
- Published
- 2013-06
- Volume
- 34
- Issue
- 2
- Pages
- 77-84
- ISSN
- 0735-3766 (ISSN-L)
- 2158-1592
- OpenAlex
- W2145636832
- Cited by
- 1,522
- References
- 3
- Citations by year
- 2026: 119 · 2025: 120 · 2024: 117 · 2023: 128 · 2022: 131 · 2021: 158 · 2020: 172 · 2019: 165 · 2018: 163 · 2017: 110 · 2016: 73 · 2015: 38 · 2014: 25 · 2013: 2
- FWCI
- 92.72
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- Journal of Business Logistics (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Keywords
- data science, predictive analytics, big data, supply chain management, supply chain design, data scientists, domain knowledge, quantitative skills, supply chain management education, research questions, management theory
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