Big data and connectivity in long-linked supply chains

Abstract

Purpose

This study aims to consider the developing of strategic use of big data in association with long-linked physical goods supply focusing on risk management.

Design/methodology/approach

Analysis is grounded on a case study of organizing the import of machine parts from Shanghai, China, to Norway. An analytical framework is developed through a literature review on long linked supply chains, big data and risk management.

Findings

Analysis reveals that big data use in this scenario encompasses mainly around handling risks associated with transformations in the supply chain, a data-driven approach. Complexity is founded in transformation – the flows of goods and information. Supply chain dynamics represent an important source for data acquisition for big data analytics.

Research limitations/implications

The qualitative nature of the study limits the aim of generalization. An alternative view of big data as process is discussed and proposed, adapted to supply chain management and industrial marketing functionality.

Originality/value

This is the first part in an ongoing research project aimed at developing a research approach to study information technology use in the inherently complex setting and scope of a long linked supply network. This scope of investigation enhances big data associated with operations dynamics providing foundation for future research on how to use big data to mitigate risk in long linked supply chains.

Citation

Engelseth, P. and Wang, H. (2018), "Big data and connectivity in long-linked supply chains", Journal of Business & Industrial Marketing, Vol. 33 No. 8, pp. 1201-1208. https://doi.org/10.1108/JBIM-07-2017-0168