Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential

2015 ◽  
Vol 36 (1) ◽  
pp. 120-132 ◽  
Author(s):  
Tobias Schoenherr ◽  
Cheri Speier-Pero
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Science ◽  
Predictive Analytics ◽  
Chain Management ◽  
Current State ◽  
Future Potential

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

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 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

Modeling big data enablers for operations and supply chain management

2018 ◽  
Vol 29 (2) ◽  
pp. 629-658 ◽  
Author(s):  
Kuldeep Lamba ◽  
Surya Prakash Singh
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Science ◽  
Structural Modeling ◽  
Security And Privacy ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type

Purpose The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM). Design/methodology/approach Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established. Findings The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM. Research limitations/implications The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community. Practical implications The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM. Originality/value MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

Big Data Analytics in Supply Chain Management

2022 ◽  
pp. 1801-1816
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

Sustainable supply chain management under big data: a bibliometric analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xinyi Zhang ◽  
Yanni Yu ◽  
Ning Zhang
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Bibliometric Analysis ◽  
Data Science ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management ◽  
Interdisciplinary Field

PurposeThis study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.Design/methodology/approachWe conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.FindingsWe discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.Originality/valueScientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.

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