This study aims to analyze the changes in the agrifood supply chain (AFSC) configurations in Latin America (LATAM) imposed by the markets as an effect of the COVID-19 pandemic. This paper analyzes the results of mobility trends, production rates, logistics performance, and the strategies developed by managers and decision makers of the organizations of the agro-industrial sector for supply chain configurations during the early stages of the pandemic in a food-exporting region that contributes 14% of the world’s production of agricultural and fishery products. Through a literature review of reports and scientific articles on the behavior of the pandemic published by international organizations, research centers, and researchers around the AFSC in LATAM, the impacts on logistical disruptions, configuration strategies, and disruptive technologies applied in Latin American agrifood supply chains during the lockdown are identified. As a result of this reconfiguration of the AFSC, the development of short supply chains and the digitalization of supply based on electronic commerce were the most used strategies during COVID-19 in LATAM. The results of this study show that Mexico, Chile, and Brazil maintained a prominent position as leaders in the different logistics indicators for the AFSC by developing strategies that include short supply chains and the digitalization of supplies based on electronic commerce. Finally, this study also highlights the imminent need to address new research and theories on the reconfiguration of the AFSC, which allows expanding analytical capabilities in organizations to face future risks and challenges that will enable the reconfiguration of the supply chain in the face of catastrophic scenarios such as COVID-19.

Manufacturing systems face several disturbances during production, such as sudden failures, defects, and unreliable subcontractors that reduce their production capacity. Currently, subcontracting represents an efficient alternative to support production decisions. The novelty of the study was the development of a new integrated model that properly coordinates production, subcontracting, and maintenances strategies in the context of stochastic uncertainty, quality deterioration, and random subcontracting availability. Such a set of characteristics has not been addressed before in the literature. A simulation–optimization approach was proposed to address such a stochastic model. A numerical case study was performed as an illustration of the approach and a comprehensive sensitivity analysis was performed to analyze the impact of several costs. Furthermore, the effect of the availability of the subcontractor and the producer was analyzed. The main finding of the study showed that the integrated model led to significant economic cost savings compared to other approaches that address such policies in isolation. The results also indicated that quality deterioration had a strong impact on the subcontracting rate and that the proposed joint control policy adequately coordinated these three key functions. The level of subcontracting participation was directly defined by its availability and the subcontracting cost.

Everyday there are more disasters that require Humanitarian Supply Chain (HSC) attention; generally these problems are difficult to solve in reasonable computational time and metaheuristics (MHs) are the indicated solution algorithms. To our knowledge, there has not been a review article on MHs applied to HSC. In this work, 78 articles were extracted from 2016 publications using systematic literature review methodology and were analyzed to answer two research questions: (1) How are the HSC problems that have been solved from Metaheuristics classified? (2) What is the gap found to accomplish future research in Metaheuristics in HSC? After classifying them into deterministic (52.56%) and non-deterministic (47.44%) problems; post-disaster (51.28%), pre-disaster (14.10%) and integrated (34.62%); facility location (41.03%), distribution (71.79%), inventory (11.54%) and mass evacuation (10.26%); single (46.15%) and multiple objective functions (53.85%), single (76.92%) and multiple (23.07%) period; and the type of Metaheuristic: Metaphor (71.79%) with genetic algorithms and particle swarm optimization as the most used; and non-metaphor based (28.20%), in which search algorithms are mostly used; it is concluded that, to consider the uncertainty of the real context, future research should be done in non-deterministic and multi-period problems that integrate pre- and post-disaster stages, that increasingly include problems such as inventory and mass evacuation and in which new multi-objective MHs are tested.

Background: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that the topic has been addressed in the literature, it is necessary to have quick response methods that can be used by decision-makers to adapt and utilize existing spaces as temporary shelters, in addition to knowing how to evacuate people. Methods: Addressing this void, a methodology to minimize evacuation and aid distribution costs is introduced. Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. Results: The research identifies optimal evacuation routes from 13 affected areas to 34 shelters and analyzes aid distribution costs under various demand scenarios: original, increased, and decreased by 10%, based on the number of transport units allocated and Google Maps distances. It also evaluates the costs associated with humanitarian aid distribution under varying collection strategies, involving state and municipal governments. Conclusion: This approach provides a decision-making foundation and can be adapted for similar analyses in other communities during extreme events.

En este trabajo se expone la relevancia y características de los operadores logísticos dentro de una cadena de suministro. Un operador logístico es un proveedor que ofrece servicios principalmente en las operaciones de almacenamiento y distribución representando una ventaja estratégica para la empresa que lo contrata, facilitando la entrega hacia el cliente desde el punto de salida hasta el punto de entrega con seguridad, flexibilidad, colaboración y control. Actualmente el sector de los proveedores logísticos se encuentra en pleno crecimiento como lo muestra los avances que México ha realizado para la creación de organismos que agrupen a los diferentes operadores logísticos con el propósito de establecer estándares de calidad.

La planificación y administración de la calidad juegan un papel esencial en el éxito y la continuidad de las empresas. En particular, la incorporación de prácticas de calidad adecuadas puede marcar la diferencia en el rendimiento y la competitividad de las Pequeñas y Medianas Empresas (PYMEs). Bajo este escenario el propósito de esta investigación es presentar de manera concreta un análisis sobre la calidad desde la perspectiva del sector metalmecánico, utilizando como caso de estudio una PYME. A través de diferentes herramientas de calidad, se muestra un enfoque práctico para la identificación, seguimiento y control que permita disminuir las no conformidades de los rechazos del producto terminado dentro de la empresa. Los resultados de este estudio describen los beneficios para la prevención y disminución de costos relacionados con la producción mediante una adecuada planeación y control de la calidad.