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Article
Publication date: 1 April 1999

Herbert Kindler

439

Abstract

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Journal of Managerial Psychology, vol. 14 no. 2
Type: Research Article
ISSN: 0268-3946

Open Access
Article
Publication date: 16 October 2017

Ray Zhong, Xun Xu and Lihui Wang

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be…

116213

Abstract

Purpose

The purpose of this paper is to review the food supply chain management (FSCM) in terms of systems and implementations so that observations and lessons from this research could be useful for academia and industrial practitioners in the future.

Design/methodology/approach

A systematical and hierarchical framework is proposed in this paper to review the literature. Categorizations and classifications are identified to organize this paper.

Findings

This paper reviews total 192 articles related to the data-driven systems for FSCM. Currently, there is a dramatic increase of research papers related to this topic. Looking at the general interests on FSCM, research on this topic can be expected to increase in the future.

Research limitations/implications

This paper only selected limited number of papers which are published in leading journals or with high citations. For simplicity without generality, key findings and observations are significant from this research.

Practical implications

Some ideas from this paper could be expanded into other possible domains so that involved parties are able to be inspired for enriching the FSCM. Future implementations are useful for practitioners to conduct IT-based solutions for FSCM.

Social implications

As the increasing of digital devices in FSCM, large number of data will be used for decision-makings. Data-driven systems for FSCM will be the future for a more sustainable food supply chain.

Originality/value

This is the first attempt to provide a comprehensive review on FSCM from the view of data-driven IT systems.

Details

Industrial Management & Data Systems, vol. 117 no. 9
Type: Research Article
ISSN: 0263-5577

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