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.

Industry 4.0 and Supply Chain Management

2020 ◽  
Vol 7 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Pavitra Dhamija ◽  
Monica Bedi ◽  
M.L. Gupta
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Cyber Physical Systems ◽  
Chain Management ◽  
Physical Systems ◽  
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 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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