Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain

2013 ◽  
Vol 34 (4) ◽  
pp. 249-252 ◽  
Author(s):  
Matthew A. Waller ◽  
Stanley E. Fawcett
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Predictive Analytics ◽  
Theory Development ◽  
Maker Movement ◽  
Data Scientist

scholarly journals Impact of big data and predictive analytics capability on supply chain sustainability

2018 ◽  
Vol 29 (2) ◽  
pp. 513-538 ◽  
Author(s):  
Shirish Jeble ◽  
Rameshwar Dubey ◽  
Stephen J. Childe ◽  
Thanos Papadopoulos ◽  
David Roubaud ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Theoretical Model ◽  
Performance Measures ◽  
Structural Equation ◽  
Predictive Analytics ◽  
Equation Modeling ◽  
Sustainable Supply Chain ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization.Design/methodology/approachThe authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM.FindingsThe statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support forH4. Although the authors did not find support forH4(moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies.Originality/valueThis study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results.

Industry 4.0 and Supply Chain Management

2021 ◽  
pp. 192-217
Author(s):  
Pavitra Dhamija ◽  
Monica Bedi ◽  
M.L. Gupta
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Statistical Tools ◽  
Chain Management ◽  
Managerial Attention ◽  
Manufacturing Organizations ◽  
Developmental Opportunities

The association of Industry 4.0 and supply chain management assures tremendous growth and developmental opportunities towards manufacturing organizations. The two aspects (Industry 4.0 and supply chain management) are one of the most opted choices for research among academicians and researchers. The study in question accommodates 884 papers from past 10 years, which contributes towards Industry 4.0, supply chain management, cyber-physical systems, digitization, Internet of Things, and Big Data predictive analytics. The statistical tools include BibExcel and Gephi for bibliometric and network analysis. The results are presented in the form of top contributing authors, keywords, and citations. The article also shares a conceptual model based on the review of studies. The findings will help managers or officials to understand the importance of Industry 4.0 and its association with supply chain management. The formed clusters and their associations are providing new areas that require managerial attention. The article ends while discussing the current and future scope of research.

The social process of Big Data and predictive analytics use for logistics and supply chain management

2019 ◽  
Vol 49 (7) ◽  
pp. 706-726 ◽  
Author(s):  
Annibal Sodero ◽  
Yao Henry Jin ◽  
Mark Barratt
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Technology Use ◽  
Predictive Analytics ◽  
Social Process ◽  
Theory Approach ◽  
Chain Management ◽  
Content Type ◽  
The Social

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area.

Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda

2016 ◽  
Vol 101 ◽  
pp. 592-598 ◽  
Author(s):  
Benjamin T. Hazen ◽  
Joseph B. Skipper ◽  
Jeremy D. Ezell ◽  
Christopher A. Boone
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Research Agenda ◽  
Predictive Analytics

Big data and predictive analytics in humanitarian supply chains

2018 ◽  
Vol 29 (2) ◽  
pp. 485-512 ◽  
Author(s):  
Rameshwar Dubey ◽  
Zongwei Luo ◽  
Angappa Gunasekaran ◽  
Shahriar Akter ◽  
Benjamin T. Hazen ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Survey Data ◽  
Predictive Analytics ◽  
Cross Sectional ◽  
Content Type ◽  
Humanitarian Supply Chains ◽  
Swift Trust ◽  
Humanitarian Supply Chain

PurposeThe purpose of this paper is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains.Design/methodology/approachThe authors conceptualize a research model grounded in contingent resource-based view where the authors propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using ordinary least squares regression, the authors test the hypotheses using survey data collected from informants at 205 international non-government organizations.FindingsThe results indicate that BDPA has a significant influence on visibility and coordination. Further, the results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust acts as a mediating construct. Hence, the authors argue that swift trust is not the condition for improving coordination among the actors in humanitarian supply chains.Research limitations/implicationsThe major limitation of the study is that the authors have used cross-sectional survey data to test the research hypotheses. Following Guide and Ketokivi (2015), the authors present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias or endogeneity-related problems.Practical implicationsManagers can use this framework to understand: first, how organizational resources can be used to create BDPA, and second, how BDPA can help build swift trust and be used to improve visibility and coordination in the humanitarian supply chain.Originality/valueThis is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster-relief-related activities.

scholarly journals Big data and predictive analytics for supply chain and organizational performance

2017 ◽  
Vol 70 ◽  
pp. 308-317 ◽  
Author(s):  
Angappa Gunasekaran ◽  
Thanos Papadopoulos ◽  
Rameshwar Dubey ◽  
Samuel Fosso Wamba ◽  
Stephen J. Childe ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Organizational Performance ◽  
Predictive Analytics

Big data and predictive analytics applications in supply chain management

2016 ◽  
Vol 101 ◽  
pp. 525-527 ◽  
Author(s):  
Angappa Gunasekaran ◽  
Manoj Kumar Tiwari ◽  
Rameshwar Dubey ◽  
Samuel Fosso Wamba
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Predictive Analytics ◽  
Chain Management
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