Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management

By M. Waller, Stanley E. Fawcett. 2013

1,377 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9c1b9598f82f9ed7d75ef1a9e627496759aa2387

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In Management Information Systems

In Management Science and Operations Research

In Strategy and Management

Citations in the corpus

Data science, predictive analytics, and big data in supply chain management: Current state and future potential (2015, positive): “The few papers that exist introduced and de ﬁ ned the domain, and called for further research (Waller and Fawcett 2013a,b), stressed the importance of data quality (Hazen et al. 2014), and tested the relationship between big data analytics and operational performance (Chae et al. 2014).”
Youth Perspectives on Critical Data Literacies (2017, background): “The collection, management, and analysis of these rapidly expanding datasets are often described as issues of “big data” [28, 21] and “data science” [53, 49].”
Design and application of Internet of things-based warehouse management system for smart logistics (2018, background): “CPS network synergises the growth of big data analytics with these separated keyenabling technologies to provide insight towards greater value proposition, analytical powers and decision-making process (Waller and Fawcett 2013).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, background): “Waller and Fawcett (2013) noted that owing to newness of big data technology and the skills associated with it, organizations with big data-skilled employees are likely to have significant advantage over their competitors.”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, neutral): “According to Waller and Fawcett (2013) and KPMG (2017), the application areas of SC analytics can be classi ﬁ ed into four areas, i.e.: . descriptive and diagnostic analysis, . predictive simulation and prescriptive optimisation, . real-time control and . adaptive learning Examples of SC and…”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, positive): “…Xu, Frankwick, & Ramirez, 2016), changes in client interfaces (Lehrer, Wieneke, vom Brocke, Jung, & Seidel, 2018), improved efficiency in supply chain management methods (Waller & Fawcett, 2013), as well as modified means for system risk analysis and fault detection (Hu, Zhao, Hua, & Wong, 2012).”
Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience (2019, background): “It is observed that researchers or practitioners often use data analytics, big data analytics, supply chain analytics and big data & predictive analytics interchangeably (see, Davenport, 2006; Waller and Fawcett, 2013; Agarwal and Dhar, 2014; Akter et al. 2016; Raffoni et al. 2017;Srinivasan and Swi”
Big data analytics in supply chain management between 2010 and 2016: Insights to industries (2018, background): “Waller & Fawcett (2013b) provided an example on the connection between big data and supply chain management.”
Smart manufacturing: Characteristics, technologies and enabling factors (2019, background): “Predictive analytics finds results with finding some results with the help of measurable variables in the data, and data mining is the field of exploring large amounts of data.(64) Since predictive analytics also deal with analysis of data, it is included in data analytics.”
Impact of Industry 4.0 on supply chain performance (2020, background): “…responsiveness and proactivity • Improved productivity, efficiency and, quality controls • Flexibility at the process level BDA • Simultaneous and systematic data collection and analysis (Davenport 2006) • Real-time data stream analysis (Li et al. 2016; Lopez Research 2014; Waller and Fawcett 2013”
Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management (2019, background): “Moreover, Waller and Fawcett [57] point out that IoT enablement leads to greater agility and proactivity in the company by enabling quick identiﬁcation of machine errors and enabling predictive maintenance.”