Nguyen, Duy Tan ORCID: https://orcid.org/0000-0002-3581-0463, Adulyasak, Yossiri ORCID: https://orcid.org/0000-0002-6996-0742, Cordeau, Jean-François ORCID: https://orcid.org/0000-0002-4963-1298 and Ponce, Silvia I. ORCID: https://orcid.org/0000-0002-0725-7875 (2022) Data-driven operations and supply chain management: established research clusters from 2000 to early 2020. International Journal of Production Research, 60 (17). pp. 5407-5431. ISSN 0020-7543 (Print), 1366-588X (Online) (doi:10.1080/00207543.2021.1956695)

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Abstract

Despite the long-recognised importance of data-driven operations and supply chain management (OSCM) scholarship and practice, and the impressive development of big data analytics (BDA), research finds that firms struggle with BDA adoption, which suggests the existence of gaps in the literature. Therefore, we conduct this systematic literature review of journal articles on data-driven OSCM from 2000 to early 2020 to ascertain established research clusters and literature lacunae. Using co-citation analysis software and double-checking the results with factor analysis and multidimensional-scaling-based k-means clustering, we find six clusters of studies on data-driven OSCM, whose primary topics are identified by keyword co-occurrence analysis. Five of these clusters relate directly to manufacturing, which, in line with the existing literature, indicates the crucial role of production in OSCM. We highlight the evolution of these research clusters and propose how the literature on data-driven OSCM can support BDA in OSCM. We synthesise what has been studied in the literature as points of reference for practitioners and researchers and identify what necessitates further exploration. In addition to the insights contributed to the literature, our study is amongst the first efforts to deploy multiple clustering techniques to undertake a rigorous data-driven systematic literature review (SLR) of data-driven OSCM.

Item Type:	Article
Uncontrolled Keywords:	data-driven operations; supply chain management; systematic literature review; co-citation analysis; clustering
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > School of Business, Operations and Strategy
Last Modified:	26 Sep 2024 09:54
URI:	http://gala.gre.ac.uk/id/eprint/48188

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