Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework

Abstract

Purpose

The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective.

Design/methodology/approach

A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles.

Findings

By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data.

Research limitations/implications

This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence.

Practical implications

Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data.

Originality/value

Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

Citation

Brinch, M. (2018), "Understanding the value of big data in supply chain management and its business processes: Towards a conceptual framework", International Journal of Operations & Production Management, Vol. 38 No. 7, pp. 1589-1614. https://doi.org/10.1108/IJOPM-05-2017-0268