Management of tire waste is an important aspect of sustainable development due to its environmental, economical and social impacts. Key aspects of Reverse Logistics (RL) and Green Logistics (GL), such as recycling, re-manufacturing and reusable packaging, can improve the management of tire waste and support sustainability. Although these processes have been performed with a high degree of efficiency in other countries such as Japan, Spain and Germany, the application in Mexico and Russia has faced setbacks due to the absence of guidelines regarding legislation, RL processes, and social responsibility. Within this context, the present work aims to develop an integrated RL model to improve on these processes by considering the RL models from Russia and Mexico. For this, a review focused on RL in Mexico, Russia, Japan and the European Union (EU) was performed. Hence, the integrated model considers regulations and policies performed in each country to assign responsibilities regarding RL processes for the management of tire waste. As discussed, the implementation of efficient RL processes for the management of tire waste depends of different social entities such as the user (customer), private and public companies, and manufacturing and state-of-the-art approaches to transform waste into different products (diversification) to consider the RL scheme as a total economic system.

Knowledge transfer associated with education in the automotive manufacturing and production fields is challenging due to the requirements of physical prototyping of mechanical components and laboratory-assisted testing. In this regard, aspects of Industry 4.0 such as virtual environments and enhanced human–computer interaction have been studied as important resources to improve teaching practices and achieve the equivalent Education 4.0 paradigm. Within the context of modern manufacturing techniques in the Industry 4.0 era and advanced tools for analysis and mechanical design, the present work describes the development of a virtual/augmented reality (VR/AR) laboratory to support learning, training, and collaborative ventures related to additive manufacturing for the automotive industry. The development was performed in accordance with the guidelines of the ISO/IEC TR 23842-1 standard, to ensure that the academic programs and the conditions of use of the laboratory were optimal. Experiences with students through the development of industry-related automotive projects support confidence in the suitability of the laboratory and the expectation of positive outcomes for future developments.

The application of Preventive Maintenance (PM) and Statistical Process Control (SPC) are important practices to achieve high product quality, small frequency of failures, and cost reduction in a production process. However there are some points that have not been explored in depth about its joint application. First, most SPC is performed with the X-bar control chart which does not fully consider the variability of the production process. Second, many studies of design of control charts consider just the economic aspect while statistical restrictions must be considered to achieve charts with low probabilities of false detection of failures. Third, the effect of PM on processes with different failure probability distributions has not been studied. Hence, this paper covers these points, presenting the Economic Statistical Design (ESD) of joint X-bar-S control charts with a cost model that integrates PM with general failure distribution. Experiments showed statistically significant reductions in costs when PM is performed on processes with high failure rates and reductions in the sampling frequency of units for testing under SPC.

As a complement to Industry 4.0, the term Education 4.0 has been defined as the evolution of traditional education considering the new digital age's technological tools, combining real and virtual world information. For emerging economies, the implementation of Education 4.0 is an essential asset for future economic and social development. In this context, the present work describes a set of conceptual Education 4.0 models to support three essential aspects in Latin American economies: entrepreneurship of small and medium enterprises, social welfare, and engineering education. These models highlight the human efforts and technological features required to adopt Education/Industry 4.0 in these aspects successfully.

An approach for the recognition of emotions in speech is presented. The target language is Mexican Spanish, and for this purpose a speech database was created. The approach consists in the phoneme acoustic modelling of emotion‐specific vowels. For this, a standard phoneme‐based Automatic Speech Recognition (ASR) system was built with Hidden Markov Models (HMMs), where different phoneme HMMs were built for the consonants and emotion‐specific vowels associated with four emotional states (anger, happiness, neutral, sadness). Then, estimation of the emotional state from a spoken sentence is performed by counting the number of emotion‐specific vowels found in the ASR’s output for the sentence. With this approach, accuracy of 87–100% was achieved for the recognition of emotional state of Mexican Spanish speech.

The textile and apparel industry is a major contributor to Latin American economies. However, in these economies the industry is characterized by limited technological infrastructure, which has led to inefficient performance and the significant generation of water, material, and product waste through its supply chain (SC). Currently, Industry 4.0 has led to important benefits in manufacturing industries, but its application in the textile field has been limited to few case studies. The present work expands on this aspect with a critical review of Industry 4.0 concepts and principles, and our main contribution consists of an implementation guideline model. The model was based on Six Sigma and the Rothwell and Zegveld model for continuous improvement and innovation projects in manufacturing and business enterprises. In each step, key aspects such as the specific skills of the interdisciplinary team, assessment tools, and implementation/prototyping tools are highlighted. A discussion of a case study is presented to support the applicability of the model for Industry 4.0 pilot projects.