The automotive industry is increasingly focused on waste management, elimination, and reduction to achieve sustainability and cost reduction. This focus drives the industry towards resource-efficient operations that minimize environmental impact while exceeding customer expectations. Meeting these demands necessitates the adoption of more efficient production methodologies, such as the PDCA cycle. This work presents a case study that illustrates the application of the PDCA methodology to minimize scrap generation due to process variability in a multinational company that manufactures electric motors for the automotive industry. The aim was to demonstrate how the PDCA methodology can improve quality standards by minimizing scrap generated during the manufacture of electrical armatures. Notably, the organization in this case study set a waste target of 0.7%, which was significantly exceeded. Finally, the implementation of this methodology can deliver significant economic benefits, with a total annual cost reduction of approximately USD 135,000.

The current study presents a non-random, quantitative experimental investigation detailing the steps required to accredit a non-regulated test method (referred to as its own) under the ISO/IEC 17025:2017 standard and the criteria set by the Mexican Accreditation Entity (EMA). The focus is on the methodology employed to validate the test method, particularly emphasizing the precision of the measurement system, along with the total variation in and tolerance of its components. For the measurement analysis, repeatability and reproducibility (r&R) studies were conducted using a variance analysis method variant (ANOVA). This variant is highlighted for its ability to estimate deviations more accurately. Furthermore, the chosen model incorporates random effect measurements for all factors or components of system variation (operators, parts, interaction, and instrument). This approach demonstrates the reliability, accuracy, and precision of the proposed measurement system within the test method, leading to its subsequent accreditation under ISO/IEC 17025:2017 as a conformity assessment body.

This research explores the effectiveness of Quality Management Systems (QMSs) certified under the ISO 9001 standard by applying Hotelling’s multivariate statistical test T². This research focuses on organizations in central Mexico, evaluating whether the adoption of the ISO 9001 standard generates the promised benefits (by the International Standardization Organization itself), such as process improvement, increased customer satisfaction, higher sales, and increased revenues. Using a comprehensive framework grounded in ISO 10014 and incorporating statistical tools, such as descriptive analysis, regression, simulation, and Hotelling’s T² test, this study examined performance differences across sectors and pinpointed the critical factors impacting QMS outcomes. The results demonstrated notable advantages, including average improvements exceeding 20% in anticipated benefits. Furthermore, the analysis underscored the importance of QMS maturity, process enhancement, and customer satisfaction as pivotal drivers of QMS success. Sector-specific patterns also emerged, revealing that public organizations prioritize process efficiency and customer satisfaction, whereas private entities emphasize sales and revenue growth. By employing multivariate techniques, this research offers valuable insights into the interconnected factors affecting QMS effectiveness and provides actionable recommendations for organizations to enhance their QMS performance.

The dynamic global automotive sector is characterized by an ever-evolving demand for high-quality vehicles and accessible prices. This research tackles the issue of scrap reduction in electric motor manufacturing for the automotive industry, specifically in a Chinese company, by employing the PDCA methodology. The paper delves into the company's background and current landscape, while pinpointing the problem: specific production lines segments experiencing hi-pot issues are the primary source of scrap. The overarching goal is to design and implement a strategy that, leveraging the PDCA approach, reduces scrap generated during the eight-months subassembly (armature) manufacturing process, from its current average of 2.92% to 0.7%, ultimately enabling the company to eliminate hi-pot defects.

The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed.