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Focusing on the study of tacit knowledge sharing in nonprofit organizations (NPOs) in Portugal and taking as a case study the Portuguese voluntary firefighters, this study aims to identify the most relevant factors for sharing tacit knowledge and identify the types of barriers most prevalent to such sharing in these organizations.

A literature review on tacit knowledge sharing allowed the identification of indicators and barriers to the sharing of this knowledge. This was followed by a mixed methodology that combines qualitative and quantitative techniques through questionnaires and interviews, whose treatment allowed to determine the indicators that influence each of these factors and which types of barriers.

Three factors have been identified that lead to the sharing of tacit knowledge within these organizations: organizational culture, individual characteristics and organizational structure. Four typologies of barriers were identified: communicational, technological, personal and resource or infrastructure.

The present research focuses exclusively on the sharing of tacit knowledge, not considering other forms of knowledge. As a case study, although with heterogeneous organizations, the same cannot be replicated to different realities.

The investigation of the main factors and the main types of barriers that arise to the sharing of tacit knowledge in these organizations may serve as a basis for the creation of diagnostic and intervention tools.

Studies in this area targeting NPOs are scarce, as opposed to what is happening in the private and public sectors. The option of a case study of organizations such as the Portuguese fire brigades, unique in their action and identity, accompanies the need increasingly recognized by society, in enabling these organizations of competencies for the best possible performance, in the face of tragic events that have occurred in recent years in Portugal.

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.

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended for regional development. However, regional development policies have led economies to very different competitive positions in matters of innovation. To address these issues, this paper aims to identify the variables that most contribute to the positioning of economies in terms of performance innovation in Europe.

The data for this study were collected at the regional innovation scoreboard. This paper uses a quantitative methodology through a multivariate statistical technique (discriminant analysis).

The results suggest that specific innovation strategies explain the competitive positioning of economies within each group of countries. It was possible to demonstrate that economies with leader classification show greater comparative robustness in the variables “Small and medium enterprise (SMEs) with product or process innovations,” “SMEs with product or process innovations,” “research and development (R&D) expenditure public sector” and “population with tertiary education,” constituting an effective instrument of innovation policy. Furthermore, it was possible to show that the economies belonging to the modest group do not have a competitive advantage in any of the variables under study, thus providing a reflection opportunity for policymakers at this level.

The present research identifies which variables are most relevant to the classification considering the regional innovation performance in leader, strong, moderate and modest. Several suggestions were given to companies, policymakers and higher education institutions in the sense that the regions where they operate can improve their innovative performance, which may help to a change in their current classification.

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.

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 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.

This research study aims to introduce a maturity model based on capability maturity model integration (CMMI) that can assess the digital manufacturing maturity level of manufacturing companies.

A CMMI model for the manufacturing industry is designed to assess the digitalisation level of manufacturing industries. The model is developed exclusively for the process area “organisational process focus” (OPF), and the digitalisation level is quantified using fuzzy logic by employing a case study approach.

The CMMI is successfully employed to assess the digitalisation level of a manufacturing organisation using the fuzzy logic approach. The triangular fuzzy number of the Fuzzy CMMI Measure Index (FCMI) is obtained as (6.08, 7.33, 8.52). The transformation of FCMI into linguistic terms discloses the digitalisation level of the manufacturing organisation as “Capability Maturity Level 4” (CML 4).

The authors tested the suitability of CMMI in the manufacturing sector. The operational concept introduced in this research sets forth a unique framework to quantify the digitalisation level of manufacturing industries.

The purpose of this paper is to investigate the factors that impact the success of an organizational change within an organization under study.

Five hypotheses were developed to investigate independent variables as possible factors affecting employees’ commitment to change. The research is based on a three-step process consisting of a literature review and field research through survey and interviews. The literature review served to establish an important foundation for understanding the problem. The field research involved employees taking part in the change completing a survey. Individual interviews were also conducted with management level employees actively involved in the change process. The paper is an empirical-based case analysis.

The results demonstrate that employees’ commitment to change, in this organization, can be improved through increasing formal and informal communication, creating adaptive organizational systems and enhancing the role of transformational leaders during the change.

Sample size was affected due to the limited availability of employees during a specific organizational cycle. Increased sample size would allow further investigation about employees’ commitment to change. Additional research could be done on how the company’s systems and practices can be adapted to change.

The success of a change project is critical for its future strategy and major project implementation. It is crucial that a systematic process be followed to implement this change. It is suggested that the John Kotter’s “eight” step change management model be adopted as a framework to the implementation plan.

New behaviors are subject to degradation if they are not ingrained in social norms and shared values, i.e. if they do not become part of the culture. Managers emphasized that, even though they are creating a new team, they recognize the importance not to create a “Monster” or a new alien culture. Leaders must make sure that all new behaviors required for the integration become part of the existing culture.

The paper provides insight in some of the factors that lead to a successful organizational change and team integration. The paper recommends four steps to address communication, organizational systems and culture to improve employees’ commitment to change.

The native species from western Mato Grosso do Sul provide fruits with unique sensory characteristics and high nutrient concentrations. Tropical fruit consumption is increasing due to the growing recognition of the nutritional value. These fruits play important roles through the marketing of their products and nutritionally through their consumption. All fruits are edible and are used in the food industry and well sensory accepted. The purpose of this paper is to verify the physicochemical characteristics, bioactive compounds and antioxidant activity in fruits collected in the Cerrado and Pantanal biomes from western Mato Grosso do Sul.

The fruits of baru, bocaiuva, jatobá, jenipapo and pequi were collected. To characterize them, longitudinal diameter, transverse diameter, total soluble solids, acidity, pH, ascorbic acid, total phenolics, antioxidant capacity, total sugar, glucose and macro and micronutrient contents analysis were performed in triplicate.

Jatobá presented the highest values of vitamin C. For phenolics, baru and jatobá stood out; bocaiuva presented the highest results for sugars, while jatobá was superior for glucose. The fruits with the highest antioxidant capacity, DPPH, were pequi and baru, while via ABTS the jatobá was the highest. Baru presented higher levels of nitrogen, phosphorus, magnesium, copper, zinc and iron. The bocaiuva presented the highest results for potassium and sulfur, the jatobá calcium and manganese. Jenipapo stood out with its boron content. Magnesium presented superior results in the baru, jenipapo and bocaiuva. The results reinforce the diversity of physicochemical characteristics.

The increase of the flow of available information, together with the growth of multicultural influences, the search for wholesome diet and the diversity of flavors and colors make these fruits important regional development tools. Their characteristics encompass national and international exploration potential that arouse consumer interest and contribute to the pursuit of innovations. In addition, the native species mentioned above provide fruits with unique sensory characteristics. Their consumption is increasing in the markets due to the growing recognition of their nutritional value. Therefore, these fruits play important roles, economically, through the marketing of their products and nutritionally through their consumption.