Machine workshops generate high scrap rates, causing non-compliance with timely delivery and high production costs. Due to their natural characteristics of a low volume, high-mix production batches, and serial and parallel configurations, generally the causes of their failure are not well documented. Thus, to reduce the scrap rate, and evaluate and improve their reliability, their system characteristics must be considered. Based on them, our proposed methodology allows us to evaluate the system, subsystem, and component–subsystem relationship by using either the Weibull and/or the exponential distribution. The strategy to improve the system performance includes reliability tools, expert interviews, cluster analysis, and root-cause analysis. In the application case, the failure sources were found to be mechanical and human errors. The component maintenance/setup, institutional conditions/attitude, and subsystem process/operation were the machine factors that presented the lowest reliability indices. The improved activities were monitored based on the Weibull β and η parameters that affect the system reliability. Finally, by using a life–effort analysis, and the method of comparative analysis of two sequential periods, we identified the causes that generated a change in the Weibull parameters. The contribution of this methodology lies in the grouping of the tools in the proposed application context.

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Emotions are a fundamental part of mental health and human behavior. In the workplace, optimal performance of employees is necessary for productivity enhancements and its relation to the quality of a manufacturing product, therefore leading a company to advantages and competitiveness. This means that the workplace staff must remain in a neutral or a calm emotional state, for an adequate job performance. When an operation is not pleasant or the same task is carried out for a long period of time (repetitive), it can cause negative emotions such as stress, and this will have repercussions in poor work performance. The purpose of this research is, by means of an electroencephalogram (EEG), to identify the stress in the repetitive assembly of a manufacturing product. To measure brain waves, the Emotiv Epoc equipment was used and a manufacturing line was designed, divided into three workstations, where the assembly of product comprising a LEGO car was carried out within a manual repetitive approach. The appearance of stress was determined by employing two different methodologies, the prefrontal relative gamma marker (RG) and the valence, arousal, and dominance (VAD) emotional categories. The results obtained from the first methodology, corresponding to the RG marker, displayed a significant more change between the relaxation state and the product assembly carried out at 70% of the standard time (ST). A less significant change was observed between the relaxation state and the product assembly carried out at 100% ST, thus signaling the presence of stress. Additionally, the results from the VAD methodology resulted in moderate and low levels of stress, when the product assembly was carried out at 70% and 100% standard time, respectively.

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