Big data analytics in logistics and supply chain management: Certain investigations for research and applications

By Gang Wang, Angappa Gunasekaran, Eric Wai Ting Ngai, Thanos Papadopoulos. 2016

1,396 citations recorded by the source corpus.

The amount of data produced and communicated over the Internet is significantly increasing, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this massive influx of big data. This is because big data can provide unique insights into, inter alia, market trends, customer buying patterns, and maintenance cycles, as well as into ways of lowering costs and enabling more targeted business decisions. Realizing the importance of big data business analytics (BDBA), we review and classify the literature on the application of BDBA on logistics and supply chain management (LSCM) – that we define as supply chain analytics (SCA), based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations). To assess the extent to which SCA is applied within LSCM, we propose a maturity framework of SCA, based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable SCA. We highlight the role of SCA in LSCM and denote the use of methodologies and techniques to collect, disseminate, analyze, and use big data driven information. Furthermore, we stress the need for managers to understand BDBA and SCA as strategic assets that should be integrated across business activities to enable integrated enterprise business analytics. Finally, we outline the limitations of our study and future research directions.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/fe2bc32726726e1086dcfb34f95834d9334c2d1c

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Citations in the corpus

Logistics 4.0: a systematic review towards a new logistics system (2019, background): “Wang et al. (2016) reviewed the literature on supply chain analytics, which is the combination of BDA and SCM to develop a maturity framework.”
Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain (2017, background): “However, most scholars emphasize that applying big data analytics in supply chain decisions is still in its infancy; therefore, studies related to BDA and supply chain management are scant [11,36,38,46].”
The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary (2018, background): “In Europe, the danger of an aging society has long been known, so that each country faces a decline in its workforce [10].”
Supply chain finance: Historical foundations, current research, future developments (2019, methodology): “The analysis provides answers to questions such as “What happened?” and “What is happening?” (Wang et al. 2016).”
Unearthing the determinants of Blockchain adoption in supply chain management (2020, background): “…of supply chain (SC) such as tracking and tracing (Dolgui and Proth 2010), big data analytics on procurement process (Fosso Wamba et al. 2018; Wang et al. 2016), demand forecasting (Hofmann and Rutschmann 2018) and risk analytics (Ivanov, Dolgui, and Sokolov 2019) as well as the use of…”
Big data analytics capability in supply chain agility (2019, neutral): “In the context of supply chain management, big data analytics capability enables to firms to examine alternatives related to supply and demand uncertainties (Waller and Fawcett, 2013; Hazen et al. 2014; Wang et al. 2016).”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, neutral): “The hype surrounding big data analytics is evident from the increasing investments made from firms, and particularly those working in complex and fast-paced environments (G. Wang, Gunasekaran, Ngai, & Papadopoulos, 2016).”
Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture (2019, neutral): “The term ‘big data’ is often used to describe massive, complex, and real-time data that requires sophisticated management, analytical and processing techniques to extract management insights (Gupta and George, 2016; Jin et al., 2016; Wang and Zhang, 2016; Wang et al., 2016; Sumbal et al., 2017; Khan”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “…we identi ﬁ ed in recent surveys on digitalisation applications to SCM (Sha ﬁ q et al. 2015; Addo-Tenkorang and Helo 2016; Gunasekaran et al. 2016, 2017; Küpper et al. 2016; Oesterreich and Teuteberg 2016; Richey et al. 2016; Schrauf and Berttram 2016; Wang et al. 2016; Nguyen et al. 2017), i.e.: .”