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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. [ABSTRACT FROM AUTHOR]

Featured Application: The present case study reports the 8D technique applied to a real manufacturing production process. Future applications can be adapted to other manufacturing industries by integrating the most important variables in their own contexts. Customer satisfaction is a key element for survival and competitiveness in industrial companies. This paper describes a case study in a manufacturing company that deals with several customer complaints due to defective custom cable assemblies that are integrated in an engine. The goal of this research is to find a solution to this problem, as well as prevent its recurrence by implementing the eight disciplines (8Ds) method in order to: (1) develop a team, (2) describe the problem, (3) develop an interim containment action, (4) determine and verify root causes, (5) develop permanent corrective actions, (6) define and implement corrective actions, (7) prevent recurrences, and (8) recognize and congratulate teamwork as well as individual contributions. Therefore, a software tool is proposed to conduct a functional test on assembly lines. After the test, the problem was successfully reduced and detected, because from 67 engines that were identified with problems, 51 were redesigned before being sent to customers, consequently decreasing the number of defective products by 75%, whereas the remaining 16 engines were replaced by new engines. In conclusion, the research goal was accomplished, and the 8Ds method proved to be a helpful model with which to increase employees' motivation and involvement during the problem-solving process. [ABSTRACT FROM AUTHOR]

Featured Application: The application of stochastic and optimization tools through simulation and Response Surface Methodology (RSM) is useful defining the optimal distribution cost in a logistic process. The present work aims at the comprehensive application of stochastic and optimization tools with the support of Information and Communication Technologies (ICT) through a case study in a logistics process for electronic goods; simulation and Response Surface Methodology (RSM) are applied for this purpose. The problem to be evaluated is to define an optimal distribution cost for products shipped to wholesale customers located in different cities in Mexico from a manufacturing plant in Tijuana, Mexico. The factors under study are the product allocation for each distribution center, finished good inventory level and on time deliveries, which are supposed to be significant to get the objective. The methodology applied for this problem considers the design of a discrete event simulation model to represent virtually the real life of logistics process, which is considered a complex system due to different activities are interrelated to carry it out. This model is used to execute the different experiments proposed by the RSM. The results obtained from simulation model were analyzed with the RSM to define the mathematical model that allows identifying the parameters of the factors in order to optimize the process. The findings prove how the ICT facilitate the application of stochastic tools with the purpose of process optimization. [ABSTRACT FROM AUTHOR]

Featured Application: The tool of PDCA cycle, supported by other graphical tools, such as Pareto charts and flowchart, can be applied in the manufacturing industry to delete or reduce occurrence of defects. Defects are considered as one of the wastes in manufacturing systems that negatively affect the delivery times, cost and quality of products leading to manufacturing companies facing a critical situation with the customers and to not comply with the IPC-A-610E standard for the acceptability of electronic components. This is the case is a manufacturing company located in Tijuana, Mexico. Due to an increasing demand on the products manufactured by this company, several defects have been detected in the welding process of electronic boards, as well as in the components named Thru-Holes. It is for this reason that this paper presents a lean manufacturing application case study. The objective of this research is to reduce at least 20% the defects that are generated during the welding process. In addition, it is intended to increase 20% the capacity of three double production lines where electronic boards are processed. As method, the Plan-Do-Check-Act (PDCA) cycle, is applied. The Pareto charts and the flowchart are used as support tools. As results, defects decreased 65%, 79%, and 77% in three analyzed product models. As conclusion, the PDCA cycle, the Pareto charts, and the flowchart are excellent quality tools that help to decrease the number of defective components. [ABSTRACT FROM AUTHOR]