An agent based model representation to assess resilience and efficiency of food supply chains

Key determinants to supply chain resilience to face pandemic disruption: An interpretive triple helix framework

Today, supply chain (SC) networks are facing more disruptions compared to the past. While disruptions are rare, they can have catastrophic long-term economic or societal repercussions, and the recovery processes can be lengthy. These can tremendously affect the SC and make it vulnerable, as observed during the COVID-19 pandemic. The identification of these concerns has prompted the demand for improved disruption management by developing resilient, agile, and adaptive SC. The aim of this study is to introduce an assessment framework for prioritizing and evaluating the determinants to supply chain resilience (SCR). To analyze the empirical data, fuzzy criteria importance through intercriteria correlation (fuzzy CRITIC) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) have been incorporated. Fuzzy CRITIC method was used to identify the critical determinants and fuzzy TOPSIS method was applied for determining relative ranking of some real-world companies. Finally, by developing propositions an interpretive triple helix framework was proposed to achieve SCR. This research stands out for its originality in both methodology and implications. By introducing the novel combination of Fuzzy CRITIC and Fuzzy TOPSIS in the assessment of determinants to SCR and applying these determinants with the help of interpretive triple helix framework to establish a resilient SC, this study offers a unique and valuable contribution to the field of SCR. The key findings suggest that 'Responsiveness' followed by 'Managerial coordination and information integration' are the most significant determinant to achieve SCR. The outcome of this work can assist the managers to achieve SCR with improved agility and adaptivity.

Systematic review on ensuring the global food security and covid-19 pandemic resilient food systems: towards accomplishing sustainable development goals targets

Covid-19, one of the most critical and widespread global pandemics, has resulted in extraordinary risk corollaries engulfing millions of people's lives and has caused an unprecedented economic downturn while amplifying food insecurity. A systematic review of 132 scientific communications was performed over a 15-year period, using articles from the ScienceDirect and Web of Science databases (2006-2021). In addition, 24 policy briefs, country papers, and publications from the UN, WHO, FAO, and OECD were cited. The aim of this paper is to provide a comprehensive review of existing literature on the adverse effects of the Covid-19 pandemic on agricultural food systems, as well as potential strategies for building robust, resilient, and sustainable food systems to ensure global food security, safety, and endeavors regarding future global emergencies, as well as new research policies while achieving SDG targets. This would fill a research gap while also having long-term implications for health, agricultural, and food resilience policy development in a rapidly changing world. Covid-19 demonstrates how human, animal, and environmental health are all interconnected, emphasizing the need for one health legislation and a paradigm shift in planetary health. Furthermore, it identifies potential mechanisms for rebuilding better systems by shifting priorities toward policy coherence, innovative food system governance, re-engineering market access, and nexus thinking in the food system approach. According to our findings, the COVID-19 posed unavoidable impediments to achieving SDG targets for food security and household poverty.

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Plate Waste Forecasting Using the Monte Carlo Method for Effective Decision Making in Latvian Schools

Food waste is a global problem, which becomes apparent at various stages of the food supply chain. The present research study focuses on the optimization of food consumption in schools and effective food management through data-driven decision making within the trends: zero food waste and digital transformation. The paper presents a plate waste forecasting system based on mathematical modeling and simulation using the Monte Carlo method, which showed an RMSE equal to ±3% and a MAPE of 10.15%. The solution based on the simulator provides a possibility to better understand the relationship between the parameters investigated through data visualization and apply this knowledge to train managers to make decisions that are more effective. The developed system has multi-disciplinary application: forecasting, education and decision making targeted to reduce food waste and improve public health and food management in schools.