Understanding supply chain analytics capabilities and agility for data-rich environments

Abstract

Purpose

Big data-driven supply chain analytics capability (SCAC) is now emerging as the next frontier of supply chain transformation. Yet, very few studies have been directed to identify its dimensions, subdimensions and model their holistic impact on supply chain agility (SCAG) and firm performance (FPER). Therefore, to fill this gap, the purpose of this paper is to develop and validate a dynamic SCAC model and assess both its direct and indirect impact on FPER using analytics-driven SCAG as a mediator.

Design/methodology/approach

The study draws on the emerging literature on big data, the resource-based view and the dynamic capability theory to develop a multi-dimensional, hierarchical SCAC model. Then, the model is tested using data collected from supply chain analytics professionals, managers and mid-level manager in the USA. The study uses the partial least squares-based structural equation modeling to prove the research model.

Findings

The findings of the study identify supply chain management (i.e. planning, investment, coordination and control), supply chain technology (i.e. connectivity, compatibility and modularity) and supply chain talent (i.e. technology management knowledge, technical knowledge, relational knowledge and business knowledge) as the significant antecedents of a dynamic SCAC model. The study also identifies analytics-driven SCAG as the significant mediator between overall SCAC and FPER. Based on these key findings, the paper discusses their implications for theory, methods and practice. Finally, limitations and future research directions are presented.

Originality/value

The study fills an important gap in supply chain management research by estimating the significance of various dimensions and subdimensions of a dynamic SCAC model and their overall effects on SCAG and FPER.

Citation

Fosso Wamba, S. and Akter, S. (2019), "Understanding supply chain analytics capabilities and agility for data-rich environments", International Journal of Operations & Production Management, Vol. 39 No. 6/7/8, pp. 887-912. https://doi.org/10.1108/IJOPM-01-2019-0025

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