Big data applications in operations/supply-chain management: A literature review

Purpose

Big data is increasingly becoming a major organizational enterprise force to reckon with in this global era for all sizes of industries. It is a trending new enterprise system or platform which seemingly offers more features for acquiring, storing and analysing voluminous generated data from various sources to obtain value-additions. However, current research reveals that there is limited agreement regarding the performance of “big data.” Therefore, this paper attempts to thoroughly investigate “big data,” its application and analysis in operations or supply-chain management, as well as the trends and perspectives in this research area. This paper is organized in the form of a literature review, discussing the main issues of “big data” and its extension into “big data II”/IoT–value-adding perspectives by proposing a value-adding framework.

Methodology/research approach

The research approach employed is a comprehensive literature review. About 100 or more peer-reviewed journal articles/conference proceedings as well as industrial white papers are reviewed. Harzing Publish or Perish software was employed to investigate and critically analyse the trends and perspectives of “big data” applications between 2010 and 2015.

Findings/results

The four main attributes or factors identified with “big data” include – big data development sources (Variety – V₁), big data acquisition (Velocity – V₂), big data storage (Volume – V₃), and finally big data analysis (Veracity – V₄). However, the study of “big data” has evolved and expanded a lot based on its application and implementation processes in specific industries in order to create value (Value-adding – V₅) – “Big Data cloud computing perspective/Internet of Things (IoT)”. Hence, the four Vs of “big data” is now expanded into five Vs.

Originality/value of research

This paper presents original literature review research discussing “big data” issues, trends and perspectives in operations/supply-chain management in order to propose “Big data II” (IoT – Value-adding) framework. This proposed framework is supposed or assumed to be an extension of “big data” in a value-adding perspective, thus proposing that “big data” be explored thoroughly in order to enable industrial managers and businesses executives to make pre-informed strategic operational and management decisions for increased return-on-investment (ROI). It could also empower organizations with a value-adding stream of information to have a competitive edge over their competitors.