Data-driven food supply chain management and systems

Dr Ray Y. Zhong (Department of Mechanical Engineering, University of Auckland, Auckland, New Zealand)
Professor Kim Tan (University of Nottingham, Nottingham, UK)
Professor Gopalakrishnan Bhaskaran (West Virginia University, Morgantown, West Virginia, USA)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 16 October 2017

4819

Citation

Zhong, D.R.Y., Tan, P.K. and Bhaskaran, P.G. (2017), "Data-driven food supply chain management and systems", Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1779-1781. https://doi.org/10.1108/IMDS-06-2017-0269

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Ray Y. Zhong, Kim Tan and Gopalakrishnan Bhaskaran

License

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


Data-driven food supply chain management and systems

Food supply chain management (FSCM) plays an important role in our daily life since it supplies us with the necessity for our lives (Marsden et al., 2000). However, inefficient and inappropriate management systems may cause large number of food losses. Gustavsson et al. (2011) pointed out that 492,000,000 tons of fruit and vegetables were wasted worldwide in 2011. In order to reduce the food waste, advanced technologies such as various sensors, Internet of Things (IoT), and cloud computing have been used to support FSCM (Yu et al., 2001; Kelepouris et al., 2007; Manzini and Accorsi, 2013; Yu and Nagurney, 2013). After deploying the advanced technologies like great myriad of sensors, vast data have been collected (Zhong et al., 2015). Such massive and invaluable data from FSCM may bring new challenges such as data processing, data visualization, data-driven decision models, decision support systems, etc. in the era of IoT.

Big Data, an emerging technology for dealing with large and complex data sets, is able to address the challenges (Tan et al., 2015; Zhong et al., 2016). Driven by the significant awareness and concerns for the food sustainability, this special issue aims to highlight some works like innovative research methodologies, Big Data-driven modeling and optimization for FSCM, case studies, FSCM system, and so on. There are total 17 research studies which could be categorized into the following dimensions.

FSCM modeling

For achieving a multi-objective approach under an RFID-enabled HMSC network design, a cost-effective decision-making algorithm was proposed (Mohammed et al., 2017). A new risk assessment model was introduced for agricultural products cold chain logistics (Zhang et al., 2017). Chandrasekaran and Ranganathan (2017) introduced a modeling and optimization of Indian traditional agriculture supply chain to reduce post-harvest loss and CO2 emission. Under the IoT-enabled fresh agricultural products supply chain, Yan et al. (2017) proposed a three-level supply chain coordination model to consider the influence of FAP on market demand and costs of controlling freshness on the road. To evaluate sustainability of supply chain, a dynamic network DEA approach was proposed (Shokri Kahi et al., 2017). Zhang et al. (2017) reported a modeling of an IoT-enabled supply chain for perishable food with two-echelon supply hubs using the real-time data. In the food supply network, a model for traffic flow routing and scheduling was illustrated to prevent traffic flow congestion by Bocewicz et al. (2017). A constraint-driven model was introduced to FSCM using generalization of data-based control (Sitek et al., 2017).

Data-driven FSCM systems and cases

Li et al. (2017) introduced an IoT-based tracking and tracing platform for prepackaged food supply chain. Hu et al. (2017) reported a comparative study on the effect of different food recall strategies on consumers’ reaction to different recall norm. Pan et al. (2017) demonstrated a case by using customers-related data to enhance E-grocery home delivery. Uddin (2017) introduced a case of the Australian agri-food industry supply chain using inter-organizational relational mechanism on firm performance to examine the influences of structural and economic issues on a supply chain performance. Kong et al. (2017) demonstrated a robot-enabled execution system for perishables auction logistics. Ghadge et al. (2017) took Greek dairy supply chains, for example, to discuss the drivers and barriers for SMEs who were implementing environmental practices in their business.

Review papers included in this special issue concentrated on the agri-fresh food supply chain quality (Ghadge et al., 2017), planning for food products supply chain (Memon et al., 2017), and FSCM (Zhong et al., 2017).

References

Bocewicz, G., Janardhana, M.N., Krenczyk, D. and Banaszak, Z. (2017), “Traffic flow routing and scheduling in a food supply network”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1972-1994.

Chandrasekaran, M. and Ranganathan, R. (2017), “Modelling and optimisation of Indian traditional agriculture supply chain to reduce post-harvest loss and CO2 emission”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1934-1953.

Ghadge, A., Kaklamanou, M., Choudhary, S. and Bourlakis, M. (2017), “Implementing environmental practices within the Greek dairy supply chain: drivers and barriers for SMEs”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1995-2014.

Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R. and Meybeck, A. (2011), Global Food Losses and Food Waste, Food and Agriculture Organization of the United Nations, Rome.

Hu, H., Djebarni, R., Zhao, X., Xiao, L. and Flynn, B. (2017), “Effect of different food recall strategies on consumers’ reaction to different recall norms: a comparative study”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 2045-2063.

Kong, X., Zhong, R.Y., Xu, G. and Huang, G.Q. (2017), “Robot-enabled execution system for perishables auction logistics”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1954-1971.

Kelepouris, T., Pramatari, K. and Doukidis, G. (2007), “RFID-enabled traceability in the food supply chain”, Industrial Management & Data Systems, Vol. 107 No. 2, pp. 183-200.

Li, Z., Liu, G., Liu, L., Lai, X. and Xu, G. (2017), “IoT-based tracking and tracing platform for prepackaged food supply chain”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1906-1916.

Manzini, R. and Accorsi, R. (2013), “The new conceptual framework for food supply chain assessment”, Journal of Food Engineering, Vol. 115 No. 2, pp. 251-263.

Marsden, T., Banks, J. and Bristow, G. (2000), “Food supply chain approaches: exploring their role in rural development”, Sociologia Ruralis, Vol. 40 No. 4, pp. 424-438.

Memon, M.A., Karray, M.H., Letouzey, A. and Archimède, B. (2017), “Semantic transportation planning for food products supply chain ecosystem within difficult geographic zones”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 2064-2084.

Mohammed, A., Wang, Q. and Li, X. (2017), “A cost-effective decision-making algorithm for an RFID-enabled HMSC network design: a multi-objective approach”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1782-1799.

Pan, S., Giannikas, V., Han, Y., Grover-Silva, E. and Qiao, B. (2017), “Using customer-related data to enhance e-grocery home delivery”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1917-1999.

Shokri Kahi, V., Yousefi, S., Shabanpour, H. and Farzipoor Saen, R. (2017), “How to evaluate sustainability of supply chains? A dynamic network DEA approach”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1866-1889.

Sitek, P., Wikarek, J. and Nielsen, P. (2017), “A constraint-driven approach to food supply chain management”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 2115-2138.

Tan, K.H., Zhan, Y., Ji, G., Ye, F. and Chang, C. (2015), “Harvesting big data to enhance supply chain innovation capabilities: an analytic infrastructure based on deduction graph”, International Journal of Production Economics, Vol. 165, pp. 223-233.

Uddin, N. (2017), “Inter-organizational relational mechanism on firm performance: the case of Australian agri-food industry supply chain”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1934-1953.

Yan, B., Wu, X.-h., Ye, B. and Zhang, Y.-w. (2017), “Three-level supply chain coordination of fresh agricultural products in the Internet of Things”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1842-1865.

Yu, M. and Nagurney, A. (2013), “Competitive food supply chain networks with application to fresh produce”, European Journal of Operational Research, Vol. 224 No. 2, pp. 273-282.

Yu, Z.X., Yan, H. and Cheng, T.C. (2001), “Benefits of information sharing with supply chain partnerships”, Industrial Management & Data Systems, Vol. 101 No. 3, pp. 114-121.

Zhang, H., Qiu, B. and Zhang, K. (2017), “A new risk assessment model for agricultural products cold chain logistics”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1800-1816.

Zhang, Y., Zhao, L. and Qian, C. (2017), “Modelling of an IoT-enabled supply chain for perishable food with two-echelon supply hubs”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 1890-1905.

Zhong, R., Xun, X. and Wang, L. (2017), “Food supply chain management: systems, implementations, and future research”, Industrial Management & Data Systems, Vol. 117 No. 9, pp. 2085-2114.

Zhong, R.Y., Newman, S.T., Huang, G.Q. and Lan, S.L. (2016), “Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives”, Computers & Industrial Engineering, Vol. 101, pp. 572-591.

Zhong, R.Y., Huang, G.Q., Lan, S.L., Dai, Q.Y., Xu, C. and Zhang, T. (2015), “A Big Data approach for logistics trajectory discovery from RFID-enabled production data”, International Journal of Production Economics, Vol. 165, pp. 260-272.

Acknowledgements

The editors would like to thank all the reviewers who gave their significant comments and suggestions for improving the published papers in this special issue. The editors give special thanks to Professor Hing Kai Chan and Professor Alain Yee Loong Chong who gave their great support to this special issue. The editors hope that this special issue will bridge the academic and practitioners so as to enhance the food supply chain management in the future.

Related articles