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La presión actual de la competencia global está demandando productos más rápidos, confiables, flexibles y más baratos; por lo tanto, con el fin de mantenerse al día con las demandas de los clientes, las empresas necesitan mejorar continuamente sus sistemas de fabricación y desarrollar nuevas estrategias. La gestión de la cadena de suministro (GCS) es actualmente la táctica más importante que utilizan las empresas modernas para mantener su competitividad. El proceso de selección de proveedores (PSP) desempeña un papel importante en la GCS y ha sido ampliamente estudiado en los últimos años como una herramienta para orientar y ayudar a los investigadores y tomadores de decisiones. La gestión de la cadena de suministro verde (GCSV) surge debido al reciente incremento en la protección del medio ambiente. Entonces, el proceso de selección de proveedores verde (PSPV) se ha convertido en un elemento crítico en la GCSV. El presente manuscrito muestra una revisión de los trabajos publicados en revistas científicas en los últimos 5 años sobre los criterios verdes utilizados en la decisión multicriterio. Además, identifica las técnicas de selección multicriterio (TSM) más utilizadas, revistas, universidades y departamentos académicos, donde este tipo de investigación se está desarrollando.

Este trabajo presenta un análisis de los efectos que tienen la infraestructura regional y los servicios en el desempeño de la cadena de suministro en empresas manufactureras de Ciudad Juárez, Chihuahua, México. Para colectar la información se aplicaron cuestionarios a 120 empresas, y para obtener un modelo de ecuaciones estructurales explicativo de los efectos se utilizó la metodología de mínimos cuadrados parciales usando WarpPLS 3.0. Los resultados obtenidos muestran que cuando existe buen nivel en la infraestructura regional se tienen impactos positivos sobre los servicios logísticos; y éstos a su vez impactan en los costos. Asimismo, la infraestructura regional tiene impactos positivos sobre el tiempo de entrega de los productos. Con relación al nivel de infraestructura (transporte, telecomunicaciones, internet) y de servicios (aéreos, terrestres, financieros, legales) en Ciudad Juárez, los encuestados lo consideran bueno por lo que las entregas de producto han sido completas y a tiempo en los últimos 3 años.

The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios.

This study investigated the wind energy potential of Ciudad Juárez (Chihuahua, México) for a whole year. The viability of employing three small-scale wind turbine models, including the S-343, Bergey BWC Excel, and AOC 15/50, in Ciudad Juarez, located in the northern Mexican state of Chihuahua in Mexico, is studied in the first stage using RETScreen software. The performance of these three turbine models was subjected to sensitivity analysis in the following study stage using the three defined economic scenarios, assuming a 7% inflation rate, a 9% discount rate, a 7% fuel cost escalation rate, and a 20-year project duration. Finally, the circumstances under which these turbines operate in Ciudad Juarez are discussed economically. Findings indicate that the study site is not economically appropriate, not even for the installation of specific small wind turbines. Finally, it was suggested that the viability of adopting alternative renewable energy systems, such as solar and hybrid systems (photovoltaic wind), be examined in the upcoming study in Ciudad Juárez.

The effective management of medical records is essential in the ordinary and emergency operations of healthcare providers. This work uses blockchain to develop a smart contract algorithm for users of a medical record platform. This algorithm provides immutable execution and addresses authentication and reliability issues to control access to healthcare platforms. An executable distributed code is used to build the smart contract algorithm. In the proposed algorithm, management operations of the clinical history are carried out and integrated in an automated way in a distributed environment. Solidity is the programming language used to create the algorithm for a private and permissioned architecture with a proposed consensus algorithm requiring significantly less computational power using a 22% faster hash function.