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The Design For Six Sigma (DFSS) methodology is one of the most important to achieving excellence in an organization’s product development process. This paper aims to propose a roadmap for product development based on the DFSS for the consumer durables manufacturing industries. The proposed roadmap presents a systematic approach to the phases of the product development process, integrating the statistical techniques and quality tools that should be used in each phase.

This study presents a detailed roadmap for product development, which was built by identifying gaps in the DFSS methods, based on previous studies on the subject. In this step, the opportunities are provided in all phases from creation to discontinuation of the product in the market. In addition, the roadmap presented was validated by team of stakeholders in the product development process of different industrial companies.

The proposed roadmap for the product development process based on six sigma design suggests a visual tool with sequential steps and techniques that allow you to follow the evolution of the development process from idea conception until the product is discontinued in the market. Identifying the priorities of organizations, especially the consumer, regarding the quality and reliability of the product.

The roadmap seeks to facilitate an understanding of the important stages of the product development process and to provide an approach to improving and optimizing the product before the manufacturing process step through the principles of DFSS methodology. This research provides a guide step by step to apply statistical techniques and quality tools in the product development process to achieve high quality and six sigma level in the manufacturing process.

The proposed roadmap of this research combines design for sigma and product development concepts, covering a wide spectrum of relevant activities that include the product development process, the application of statistical techniques and the design of high-quality durable consumer goods to match manufacturing technologies.

E-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.

An approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.

The results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.

This research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.

A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.

The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.

This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

The aim of this paper is to investigate the potential impact of Industry 4.0 (I4.0) digital technologies on promoting sustainability in small and medium-sized enterprises (SMEs) within developing economies such as Brazil. Additionally, we present a comprehensive framework that consolidates this correlation.

Qualitative research was conducted through semi-structured interviews with leaders of SMEs to identify the specific challenges in achieving sustainability. Additionally, interviews were conducted with technology provider firms to evaluate the existing solutions available to SMEs. The interview results were analyzed, and technological solutions were proposed through a focus group session involving four experts in I4.0. These proposed solutions were then compared with the offerings provided by the technology providers. Based on this, a second round of meetings was conducted to gather feedback from the SMEs.

The findings of this study confirm the feasibility of implementing I4.0 and sustainable practices in SMEs. However, it is crucial to tailor the technologies to the specific circumstances of SMEs. The study presents propositions on how specific applications of technology can address the economic, environmental and social demands of SMEs. Furthermore, a framework is proposed, emphasizing the integration of smart technologies as essential components across sustainability dimensions.

This study makes a significant contribution to the current body of literature as it pioneers the examination of the relationship between I4.0 technologies and sustainability, focusing specifically on SMEs in a developing country context.

El objetivo de este estudio es investigar el potential impacto de las tecnologías digitales de la Industria 4.0 en la promoción de la sostenibilidad en las pequeñas y medianas empresas (PYMES) en economías en desarrollo, como Brasil.

Realizamos una investigación cualitativa mediante entrevistas semiestructuradas a líderes de PYMES para identificar los desafíos que enfrentan en la búsqueda de la sostenibilidad. También llevamos a cabo entrevistas con empresas proveedoras de tecnología para evaluar las soluciones existentes. Los resultados de las entrevistas se analizaron y se propusieron soluciones tecnológicas a través de una sesión de grupo focal con cuatro expertos en la Industria 4.0. Estas soluciones se compararon con las ofertas proporcionadas por los proveedores de tecnología. Posteriormente, se realizaron una segunda reunión para recopilar comentarios de las PYMES.

Los hallazgos de este estudio confirman la viabilidad de implementar la Industria 4.0 y prácticas sostenibles en las PYMES. Sin embargo, es crucial adaptar las tecnologías a las circunstancias de las PYMES. Presentamos propuestas sobre cómo las aplicaciones de la tecnología pueden abordar las demandas económicas, ambientales y sociales de las PYMES. Además, proponemos un marco que destaca la integración de tecnologías como componentes esenciales de la sostenibilidad.

Este estudio es pionero en examinar la relación entre las tecnologías de la Industria 4.0 y la sostenibilidad, centrándose específicamente en las PYMES en un contexto de país en desarrollo.

This paper aims to present a systematic review of design for six sigma (DFSS) methods applicable to the product development process (PDP) of durables goods and identify a research opportunity on the subject proposing integration of DFSS and a reference model for the PDP. In this way, through the analysis of the theoretical references identified in the scientific databases, it was possible to propose a conceptual model for the PDP oriented to the DFSS.

This paper is based on the theoretical framework presented in peer-reviewed scientific research papers during the period 2000 to 2018 on the theme DFSS applied in the PDP, as well as such as the product development tools/techniques and statistics addressed. By means of key words defined by the acronyms of DFSS methods (DMADOV, ICOV, DMEDI, IDOV, DDOV, PIDOV, DMADIC, DCCDI, DMADV, IDDOV, CDOV and DCOV), DFSS and the acronym DFSS. Applying Boolean expression during the conduction of the searches through the scientific evidence at the Brazilian scientific database platform (Capes database). This database platform is maintained by coordination for the improvement of higher education personnel, which including Emerald Insight (Emerald), Scopus (Elsevier), Science Direct, SpringerLink, Taylor Francis, Scielo (Web of Science), Wiley Online Library, Web of Science (Clarivate Analytics), etc. It was obtained, by means of the searches, 269 papers related to subject DFSS, of which 18 papers had been critically selected for the composition of a conceptual model for the process of development of product guided to the DFSS.

This study presents a review of the literature (systematic review and content analysis) on DFSS and its effectiveness for the PDP. The DFSS methodology is disseminated in the scientific literature through a variety of methods that are often mistaken for the six sigma methodology – DMAIC, which is directed toward process improvement. The PDP integrated with the DFSS concepts contributes to eliminating possible failures during the design of a new product, directing to reduce costs and improve the quality of the product and process.

This paper presents a literature review that guided to a proposal of a preliminary conceptual model DFSS focused on the process of product development with the purpose of being a friendly model that meets the dynamics of the organizations and the expectations of the consumers.

Through the systematic review and content analysis, it was possible to observe that the DFSS methods applied to product development are not related to the PDP reference models available in the literature. In this way, the fusion of the concepts of the DFSS methods and PDP reference models for the construction and proposition of a preliminary conceptual model DFSS oriented to the process of product development intends to contribute in the development of new products with the reduction of time, reduction of the cost, competitive price and consumer satisfaction.

The purpose of this paper is to propose a framework of decision making to aid practitioners in modeling and optimization experimental data for improvement quality of industrial processes, reinforcing idea that planning and conducting data modeling are as important as formal analysis.

The paper presents an application was carried out about the modeling of experimental data at mining company, with support at Catholic University from partnership projects. The literature seems to be more focussed on the data analysis than on providing a sequence of operational steps or decision support which would lead to the best regression model given for the problem that researcher is confronted with. The authors use the concept of statistical regression technique called generalized linear models.

The authors analyze the relevant case study in mining company, based on best statistical regression models. Starting from this analysis, the results of the industrial case study illustrates the strong relationship of the improvement process with the presented framework approach into practice. Moreover, the case study consolidating a fundamental advantage of regression models: modeling guided provides more knowledge about products, processes and technologies, even in unsuccessful case studies.

The study advances in regression model for data modeling are applicable in several types of industrial processes and phenomena random. It is possible to find unsuccessful data modeling due to lack of knowledge of statistical technique.

An essential point is that the study is based on the feedback from practitioners and industrial managers, which makes the analyses and conclusions from practical points of view, without relevant theoretical knowledge of relationship among the process variables. Regression model has its own characteristics related to response variable and factors, and misspecification of the regression model or their components can yield inappropriate inferences and erroneous experimental results.