The Sustainable Development Goals (SDGs) present the emerging need to explore new ways of AgriFood production and food security as ultimate targets for feeding future generations. The study adopts a Design Science methodology and proposes Artificial Intelligence (AI) techniques as a solution to food security problems. Specifically, the proposed artefact presents the collective use of Agricultural Technology (AgriTech) drones inspired by the biomimetic ways of bird swarms. The design (artefact) appears here as a solution for supporting farming operations in inaccessible land, so as unmanned aerial devices contribute and improve the productivity of farming areas with limited capacity. The proposed design is developed through a scenario of drone swarms applying AI techniques to address food security issues. The study concludes by presenting a research agenda and the sectoral challenges triggered by the applications of AI in Agriculture.

The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.

Purpose: The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.Design/methodology/approach: The authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.Findings: The findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.Originality/value: This is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.

This paper examines the effect of different types of collaboration on the level of Postharvest Food Losses (PHFL) and the proportion of low-quality peaches produced using a unique data-set of Greek peach producers. Quantile regression techniques are adopted to estimate the effects at different points of the conditional distribution of our variables of interest. The findings of this study suggest that high levels of collaboration between producers and cooperatives are associated with both low levels of PHFL and a low proportion of low-quality peaches. We also find that specific types of collaboration, such as ‘goal congruence’, can play a significant role in reducing PHFL and improving the quality of peach production at the extremes of the distribution. Important policy implications regarding collaborative practices and systems that can be implemented to reduce PHFL and boost a producer’s performance together with sustainability credentials are drawn from this study.

The term AI refers to the digital technologies performing activities, tasks and decisions normally deployed by human intelligence (Pomerol 1997). Recently, the context of Artificial Intelligence (A...