Big data analytics in supply chain management: A state-of-the-art literature review

By Truong Van Nguyen, Li Zhou, Virginia L. M. Spiegler, Petros Ieromonachou, Yong Lin. Computers & Operations Research, 2017

483 citations recorded by the source corpus.

The rapid growing interest from both academics and practitioners towards the application of Big Data Analytics (BDA) in Supply Chain Management (SCM) has urged the need of review up-to-date research development in order to develop new agenda. This review responds to this call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structured based on the content analysis method of Mayring (2008), addressing four research questions on (1) what areas of SCM that BDA is being applied, (2) what level of analytics is BDA used in these application areas, (3) what types of BDA models are used, and finally (4) what BDA techniques are employed to develop these models. The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

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

View Paper: https://www.semanticscholar.org/paper/866b171e691124e5eaa490ea140f8e7144c66691

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In Management Information Systems

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Citations in the corpus

Role of big data analytics in developing sustainable capabilities (2019, background): “Nguyen et al. (2017) proposed a framework that provides a comprehensive view of the usage of big data analytics in the SCM context, which is one of the few key business aspects affected by large data analytics due to the implementation of data-driven strategies and operations.”
A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2020, background): “Nguyen et al. (2018) identify areas where data analytics can be applied in SCs in the near future.”
The adoption of digital technologies in supply chains: Drivers, process and impact (2021, background): “For instance, some firms installed IoT devices to collect the real-time data of storage and transportation, and the data analysis can help arrange the warehouse location and optimise the transportation routines automatically (Nguyen et al., 2018).”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, background): “Dubey et al. [11], Papadopoulos et al. [10], Gunasekaran et al. [12], Choi et al. [72] and Nguyen et al. [73] provide evidence that data analytics is be-229 ing applied to SC management in procurement, manufacturing shop ﬂoors, routing optimization, real-time traﬃc operation monitoring, proactive…”
Supply chain risk management and artificial intelligence: state of the art and future research directions (2018, unknown): “This low adoption rate may be due to reasons such as lack of understanding and skills, inability to identify suitable data and low acceptance by SC partners (Nguyen et al. 2017).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “…or fall, and then combines this information with the relevant cost and inventory data to recommend prices that could maximise revenue and pro ﬁ ts. Nguyen et al. (2017) showed that optimisation is the most popular approach in prescriptive analytics application to logistics and transportation area.”
Prescriptive analytics: Literature review and research challenges (2020, unknown): “…reviews for big data analytics, operational research and management science (Barbosa, Vicente, Ladeira, & Oliveira, 2018; Howick & Ackermann, 2011; Nguyen, Li, Spiegler, Ieromonachou, & Lin, 2018; Seifert, Seifert, & Protopappa-Sieke, 2013), while similar methodologies have been followed in other…”
Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities (2020, methodology): “It shall be mentioned that there are literature review papers exploring the use of big data analytics in SCM [10, 16, 23, 54–67].”