Innovation projects has established as an emerging field of research, a number of issues have emerged; among the others, measurability and predictability of product development. Moreover, whilst great relevance has been given to aspects such as innovation factors, product results, cognitive knowledge, poor attention has been given to other fundamental factors: product development process and effectiveness of design process as a consequence of complex problem situation. A formalization in affordances of design process may help overcome said issues and enhance product effectiveness, by identifying and highlighting potential designers’ workload perception during the Product Development Process. In this scenario, this paper describes an approach for continuously monitoring and measuring the complexity of design process during the creation of a new product. After an overview of the related work, this study proposes a common instructional design model for Product Development Process, which was split in order to be measured by using NASA-TLX. This research propose that the use of NASA-TLX to predict and measure the designer workload during the design process contributes to a better understanding of the relationships among the different design phases and designer workload. After having shown a case study, a discussion about the possible developments, as well as the limitations of the proposed method, concludes this work.

Failure causes of production systems need to be readily identified, for effective recovery solutions. Root cause analysis can be used for this, integrated by a method for identifying coherent and sustainable solutions. In this article, root cause analysis for failure cause identification is coupled with a method of problem solving to guide the search for effective solutions, called theory of inventive problem solving. The integration of these methodologies is implemented and demonstrated on a real case concerning the solution to the potential failure of the equipment used in the torque test performed on truck engines used in high-tonnage mining operation. The incorporation of a key performance indicator analysis allows evaluating realistically the results obtained and determining the success of the solution.

In collective sports, reactive agility training methodologies allow to evaluate and improve the player performance, being able to consider a mixture of technical, tactical, physical, and psychological abilities, similarly to real game-play situations. In this article, we present a new methodology for reactive agility training (neural training), the technological setup for the methodology, and a new footstep tracking algorithm, as the key element for automating the speed data gathering process, necessary for obtaining the relevant variables of the neural training approach. This new methodology is oriented to accurately measure two of the most relevant variables for reactive agility training: total response time (sprint time) and response correctness, related to a stimuli sequence presented to a player. The stimuli were designed to properly represent realistic competitive conditions for player training, contextualized to soccer. In order to automate the gathering process, a new computer vision based automatic footstep detection algorithm has been integrated to the system. The algorithm combines Kalman Filters, segmentation techniques, and perspective geometry, for obtaining highly precise detections of the moment a relevant footstep occurs in real-time, reaching a precision higher than 97%. Plus, the algorithm does not require any special marker, invasive sensor, or clothing constraint on the player.

Reliability determines, in large part, the operational productivity. Nevertheless, a frequent problem is the absence of effective mechanisms to support maintenance management. In particular, there is a need for methodologies focused on improving the detection and analysis of risks that affect reliability. This article presents a methodological proposal for the resolution of these problems, using a high-impact failure mode analysis. The methodology is based on four phases: identification of failure modes, ranking and criticality analysis of them, identification of the root cause(s) and search for highly effective solutions. Among the variety of tools that can be used, it is proposed the use of three specific tools: Criticality Analysis, which allows discrimination and ranking of phenomena and assets; Root Cause Analysis, which focuses on the identification of the real causes of the problems; and a tool for generation of effective and efficient solutions (TRIZ), which it is not usually applied to reliability problems. The proposal is applied in a mining filtration plant, identifying and classifying current problems and generating solutions.

Los emprendimientos universitarios se caracterizan por ser una actividad de alto riesgo, debido al elevado número de factores que influyen en su desarrollo, ya sean sociales, económicos o tecnológicos. A estos factores, se suma la inexperiencia de los emprendedores universitarios en la utilización de métodos, técnicas y herramientas, que les permitan alcanzar el éxito. Por ello, la definición adecuada de una estrategia es relevante, con miras a aumentar las posibilidades de éxito de un emprendimiento, además de ayudar a atenuar y disminuir el riesgo de este. Sin embargo, los emprendedores universitarios no siempre cuentan con las capacidades y el conocimiento necesario que les permita crear un análisis apropiado, lo que trae como consecuencia una mayor dificultad en el proceso de obtención de información relevante, así como en la planificación de sus actividades y de los recursos que posee. Frente a esta problemática, el proceso de enseñanza de los emprendedores requiere de nuevas herramientas, técnicas y métodos que les permitan evaluar y anticipar problemas, con el fin de disminuir el riesgo futuro de su emprendimiento. Teniendo en cuenta esta premisa, este artículo presenta una metodología estructurada, que tiene como objetivo contextualizar y analizar un emprendimiento universitario, pero teniendo en cuenta para ello los recursos reales que este posee, junto a la priorización de soluciones que los emprendedores puedan aplicar, con base en la teoría de resolución de problemas de inventiva.