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.

This publication presents the development of a method that seeks to monitor the parameters β (shape) and η (scale) for each component-subsystem combination following the Weibull distribution, necessary for the calculation of the reliability of the man-machine system in the machining area. This system defines the workshops of the metal-mechanic, with high-mix and low-volume batch production where conventional and Computerized Numerical Control (CNC) machines are involved, which share the manufacturing of parts that sometimes are unique, or their manufacturing period is short. The design of the man-machine system is based on the analysis of the failures of non-conforming parts in the machining area and on the failure rates, which the statistical model is developed for its evaluation, considering the 2-parameter Weibull distribution, and a redundant system with series-parallel configuration. The results obtained were based on the theoretical-practical, using mathematical and statistical models, as well as the Study Case. With the use of mathematical and statistical models, it is demonstrated that the probability of failure (risk) of the man-machine system is time-dependent and is generated by mechanical type stresses, which occur in the manufacture of parts.

This article presents the results of a research project whose objective was to design a simulation process using a stochastic-dynamic model and the Jupyter Notebook tool with Phyton programming, to understand and analyze the behavior of the costs associated with an inventory management system with stochastic demand and periodic review, in a walnut distribution company. Data was collected on inventory holding costs, shortage costs, ordering costs, warehouse capacity, delivery times and demand for the last 6 months. The results obtained revealed that, over a 3-month horizon, the operating cost presents a parabolic behavior in relation to demand, which allows identifying the optimal or equilibrium point between inventory and total average costs, depending on an uncertain demand.

The Six Sigma methodology has been widely used in various companies worldwide to reduce variation and waste while increasing efficiency and productivity. It can be applied to both manufacturing and service companies, as well as throughout the supply chain or at a workstation. The Six Sigma methodology is based on the DMAIC process, which stands for define, measure, analyze, implement, and control. This process allows for the identification of critical quality issues and helps understand their root causes. By identifying the problem, it becomes possible to solve it. It is important to note that different types of tools are used for quality assessment and process improvement within each phase of the DMAIC process. Therefore, this research presents the foundations of the Six Sigma methodology, the phases of the process, and the most significant tools used in each phase. The objective is to provide a guide for the correct implementation, quality assurance, and decision-making through the Six Sigma methodology.