Purpose: Resilience in agri-food supply chains (AFSCs) is an area of significant importance due to growing supply chain volatility. While the majority of research exploring supply chain resilience has originated from a supply chain management perspective, many other disciplines (such as environmental systems science and the social sciences) have also explored the topic. As complex social, economic and environmental constructs, the priority of resilience in AFSCs goes far beyond the company specific focus of supply chain management works and would conceivably benefit from including more diverse academic disciplines. However, this is hindered by inconsistencies in terminology and the conceptual components of resilience across different disciplines. The purpose of this study is to use a systematic literature review to identify which multidisciplinary aspects of resilience are applicable to AFSCs and to generate a novel AFSC resilience framework.Design/methodology/approach: This paper uses a structured and multidisciplinary review of 137 articles in the resilience literature followed by critical analysis and synthesis of findings to generate new knowledge in the form of a novel AFSC resilience framework.Findings: Findings indicate that the complexity of AFSCs and subsequent exposure to almost constant external interference means that disruptions cannot be seen as a one-off event; thus, resilience must concern the ability to not only maintain core function but also adapt to changing conditions.Practical implications: A number of resilience elements can be used to enhance resilience, but their selection and implementation must be carefully matched to relevant phases of disruption and assessed on their broader supply chain impacts. In particular, the focus must be on overall impact on the ability of the supply chain as a whole to provide food security rather than to boost individual company performance.Originality/value: The research novelty lies in the utilisation of wider understandings of resilience from various research fields to propose a rigorous and food-specific resilience framework with end consumer food security as its main focus.

As much as one-third of the food intentionally grown for human consumption is never consumed and is therefore wasted, with significant environmental, social and economic ramifications. An increasing number of publications in this area currently consider different aspects of this critical issue, and generally focus on proactive approaches to reduce food waste, or reactive solutions for more efficient waste management. In this context, this paper takes a holistic approach with the aim of achieving a better understanding of the different types of food waste, and using this knowledge to support informed decisions for more sustainable management of food waste. With this aim, existing food waste categorizations are reviewed and their usefulness are analysed. A systematic methodology to identify types of food waste through a nine-stage categorization is used in conjunction with a version of the waste hierarchy applied to food products. For each type of food waste characterized, a set of waste management alternatives are suggested in order to minimize environmental impacts and maximize social and economic benefits. This decision-support process is demonstrated for two case studies from the UK food manufacturing sector. As a result, types of food waste which could be managed in a more sustainable manner are identified and recommendations are given. The applicability of the categorisation process for industrial food waste management is discussed.

There has been a significant growth in research and applications of product recovery and recycling over the last two decades, in particular with the view of recent product take-back legislation which has extended the responsibility of manufacturers to include the recovery and safe disposal of their products. However, at present, the global scale of product recovery applications is significantly disproportional to the total manufacturing output. Hence, to achieve the idealistic goal of ‘zero landfill’, there is a need to significantly improve and extend both the scale of product recovery activities and the range of manufacturing applications in which such activities have yet to be implemented. This paper examines a range of barriers, drivers and challenges in research and development for the next generation of product recovery initiatives. A range of existing applications and case studies undertaken for the UK market has been used to analyse issues related to: the need for improvement and expansion of current legislation on producer responsibility; product take-back and reverse logistic models for collection of used products; knowledge-based approaches for end-of-life considerations during the design phase; improved technologies and increased automation in pre- and post-fragmentation recycling processes and most importantly, the requirement for sustainable business models for establishing value recovery chains which can be based on the provision of services rather than products. The paper concludes by summarising the results of this analysis to bridge the gap between existing and future sustainable solutions for product recovery.

Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions.

Food waste is a significant contemporary issue in the UK, with substantial environmental, social and economic costs to the nation. Whilst efforts to reduce food waste are laudable, a significant proportion of food and drink manufacturer waste is unavoidable. On the one hand, there is a drive from industry to reclaim as much value from this waste as possible, for example, by conversion to valuable products in what is known as "valorisation". At the same time, growing social and legislative pressures mean that any attempts to valorise food waste must be performed in a sustainable manner. However, for every company and its specific food wastes, there will be multiple valorisation possibilities and few tools exist that allow food and drink manufacturers to identify which is most profitable and sustainable for them. Such a decision would need to not only consider environmental, social and economic performance, but also how ready the technology is and how well it aligns with that company's strategy. In response, this paper develops and presents a hybrid framework that guides a company in modelling the volumes/seasonality of its wastes, identifying potential valorisation options and selecting appropriate indicators for environmental, social and economic performance as well as technological maturity and alignment with company goals. The framework guides users in analyzing economic and environmental performance using Cost-Benefit Analysis and Life Cycle Assessment respectively. The results can then be ranked alongside those for social performance, technological maturity and alignment with company goals using a weighted sum model variant of Multi-Criteria Decision Analysis to facilitate easy visual comparison. This framework is demonstrated in the form of a case study with a major UK fruit consolidator to identify the optimal strategy for managing their citrus waste. Possibilities identified included sale of imperfect but still edible waste via wholesale at a significantly reduced profit and the investment in facilities to extract higher value pectin from the same waste stream using a microwave assisted pectin extraction process. Results suggest that continued sale of waste to wholesale markets is currently the most beneficial in terms of economic viability and environmental performance, but that in the medium to long term, the projected growth in the market for pectin suggests this could become the most viable strategy.

There is a lack of knowledge among food manufacturers about adopting the Internet of Things (IoT)-based water monitoring system and its ability to support water minimisation activities. It is therefore necessary to investigate the applicability of IoT-based real-time water monitoring systems in a real food manufacturing environment to pursue water-saving opportunities accordingly. This article aims to propose an architecture of an IoT-based water-monitoring system needed for real-time monitoring of water usage, and address any water inefficiencies within food manufacturing. This article looks at a study conducted in a food beverage factory where an IoT-based real-time water monitoring system is implemented to analyse the complete water usage in order to devise solutions and address water overconsumption/wastage during the manufacturing process. The successful implementation of an IoT-based real-time water monitoring system offered the beverage factory a detailed analysis of the water consumption and insights into the water hotspots that needed attention. This action initiated several water-saving project opportunities, which contributed to the improvement of water sustainability and led to an 11% reduction in the beverage factory’s daily water usage.