Agrologistics 4.0: Emerging Trends and Innovative Research in Data-Driven Agri-Food Logistics

Dear Colleagues,

A main trend in agri-food chains over the past two decades is the wide application of information and communication technology (ICT) in different stages of the chain, from harvesting to transportation to the final consumer (Akhtar et al., 2016). All these technologies—including robotics, IoT sensors, big data analytics, and cloud computing—have resulted in a big volume of data in the sector. Safety and security initiatives have also further stimulated data gathering and information sharing along the chain (Denolf et al., 2018). As a result, the agri-food sector is a data-rich domain. However, in many cases, the value of existing data to improve the supply chain performance and logistics processes has not been sufficiently explored. At the same time, several recent studies show that despite substantial investments in ICT systems, many organizations have failed to obtain the expected improvements in their supply chain performance (Viet et al., 2018). This implies the need for systematic and innovative methods to re-design data-driven processes in agri-food logistics (Viet et al., 2020).

This Special Issue is intended to reflect the innovative trends, efforts, and cutting-edge research—methodological and theoretical developments as well as applications—that address the “Value of Data” in data-driven agri-food logistics and how information flows must be designed to support the material flows in a food supply chain. Potential topics include (but are not limited to):

• Data-driven prescriptive/descriptive analytics in agri-food logistics;
• Case studies of IoT-enabled applications in agri-food supply chains;
• Use of cloud, distributed, and digital manufacturing paradigms in agri-food supply chains;
• Machine learning techniques to improve food logistics processes;
• Model-based vs. data-based logistics planning in food supply chains;
• The value of data in managing abnormalities/disruptions in agri-food supply chains.

References:

Akhtar, P., Tse, Y. K., Khan, Z., & Rao-Nicholson, R. (2016). Data-driven and adaptive leadership contributing to sustainability: Global agri-food supply chains connected with emerging markets. International Journal of Production Economics, 181, 392-401.

Denolf, J. M., Trienekens, J. H., Wognum, P. N., Schütz, V., Van Der Vorst, J. G., & Omta, S. O. (2018). “Actionable” critical success factors for supply chain information system implementations. International Journal on Food System Dynamics, 9(1), 79-100.

Viet, N. Q., Behdani, B., & Bloemhof, J. (2018). The value of information in supply chain decisions: A review of the literature and research agenda. Computers & Industrial Engineering, 120, 68-82.

Viet, N. Q., Behdani, B., & Bloemhof, J. (2020). Data-driven process redesign: anticipatory shipping in agro-food supply chains. International Journal of Production Research, 58(5), 1302-1318.

Prof. Dr. Behzad Behdani 
Guest Editor

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• big data analytics
• agri-food supply chains
• value of information
• Internet of Things (IoT ) technologies
• Supply Chain 4.0
• food logistics
• artificial intelligence and machine learning