Big Data Applications in Food Supply Chain Management: A Conceptual Framework

Abstract

The paper provides a systematic review and analysis of the current literature on big data (BD) applications in the context of food supply chain management (FSCM) in order to categorize the state-of-the-art research trends exploring the adoption and implementation of big data analytics (BDA) across different segments of food supply chain (FSC). The use of BDA brings the digital transformation of FSCs closer providing sustainable implications and added value to their operation. Harnessing BD’s potential is becoming more and more relevant in addressing the constantly evolving complexities in food systems. However, the field of BD applications in the FSCM domain is severely fragmented and relatively “primitive”. The present research is one of the earliest attempts to recognize and present a comprehensive analysis for the BD applications across different segments of FSC proposing a conceptual framework that illustrates the role of BD in a data-driven FSCM environment. For the purposes of our research, we adopted the systematic literature review (SLR) method aiming at the identification of the dominant categories and themes within the research area. Based on the SLR findings, we propose a conceptual framework that captures the interconnection between FSC performance and BD applications by using the input-process-output (IPO) model within a data-driven FSCM context. The main research contribution lies on the thematic classification of relevant research, the conceptualization of this fragmented field, the development of a conceptual framework, and the presentation of a future research agenda pertaining to BD applications in a data-driven FSCM context.