Purpose: This research examines the impact of integrating lean manufacturing (LM) principles and Industry 4.0 (I4.0) into the product development process (PDP) of manufacturing companies in southern Brazil, with the objective of improving market performance (MP).Design/methodology/approach: A survey was conducted with 110 product development managers to evaluate the level of LM and I4.0 principles, PDP phases, and the improvement of MP. Quantitative analysis was employed to analyse the data, including confirmatory factor analysis (CFA) and ordinary least squares (OLS) hierarchical regression.Findings: The study reveals that integrating LM and I4.0 principles in the PDP enhances product development and positively impacts the MP of manufacturing companies. Additionally, I4.0 partially mediates the LM-PDP relationship, while LM partially mediates the I4.0-PDP relationship, highlighting their interdependence rather than one being a prerequisite for the other.Research limitations/implications: The study has limitations regarding its narrow focus on product development managers in Brazil and the absence of multiple case studies.Practical implications: The study emphasises the importance of coordinated efforts in manufacturing companies to adopt and manage both LM and I4.0 principles, as they positively impact all phases of the PDP and ultimately contribute to the overall MP of the companies.Originality/value: The novelty of this study lies in its systemic approach, examining the relationships and effects of LM and I4.0 principles on the PDP, and demonstrating how these effects manifest in manufacturing companies.

Customer loyalty and retention are crucial for digital platforms, yet systematic studies integrating technological innovation and loyalty strategies remain scarce. This study addresses this gap by conducting a bibliometric analysis of key factors influencing customer retention in the digital environment. Our research employs a quantitative bibliometric approach using the Biblioshiny and Bibliometrix tools (RStudio 2022.02), analyzing 300 scientific articles from the Web of Science database (2021–2024). This study applies bibliometric techniques such as descriptive metrics, bibliographic coupling, co-citation, and scientific collaboration mapping to identify trends and thematic clusters. Our findings indicate that emerging technologies, including artificial intelligence and big data, significantly impact customer experience, trust, and engagement. Personalization and digital innovation emerge as fundamental drivers of customer retention, offering strategic insights for companies aiming to strengthen competitiveness in the global digital market.

The automotive industry constantly seeks intelligent technologies to increase competitiveness, reduce costs, and minimize waste, in line with the advancements of Industry 4.0. This study aims to implement and analyze a predictive model based on machine learning within the automotive industry, validating its capability to reduce the impact of unplanned downtime. The implementation process involved identifying the central problem and its root causes using quality tools, prioritizing equipment through the Analytic Hierarchy Process (AHP), and selecting critical failure modes based on the Risk Priority Number (RPN) derived from the Process Failure Mode and Effects Analysis (PFMEA). Predictive algorithms were implemented to select the best-performing model based on error metrics. Data were collected, transformed, and cleaned for model preparation and training. Among the five machine learning models trained, Random Forest demonstrated the highest accuracy. This model was subsequently validated with real data, achieving an average accuracy of 80% in predicting failure cycles. The results indicate that the predictive model can effectively contribute to reducing the financial impact caused by unplanned downtime, enabling the anticipation of preventive actions based on the model’s predictions. This study highlights the importance of multidisciplinary approaches in Production Engineering, emphasizing the integration of machine learning techniques as a promising approach for efficient maintenance and production management in the automotive industry, reinforcing the feasibility and effectiveness of predictive models in contributing to sustainability.

Ensuring that an interactive application allows users to perform their activities and reach their goals is critical to the overall usability of the interactive application. Indeed, the effectiveness factor of usability directly refers to this capability. Assessing effectiveness is a real challenge for usability testing as usability tests only cover a very limited number of tasks and activities. This paper proposes an approach towards automated testing of effectiveness of interactive applications. To this end we resort to two main elements: an exhaustive description of users' activities and goals using task models, and the generation of scenarios (from the task models) to be tested over the application. However, the number of scenarios can be very high (beyond the computing capabilities of machines) and we might end up testing multiple similar scenarios. In order to overcome these problems, we propose strategies based on task models manipulations (e.g., manipulating task nodes, operator nodes, information...) resulting in a more intelligent test case generation approach. For each strategy, we investigate its relevance (both in terms of test case generation and in terms of validity compared to the original task models) and we illustrate it with a small example. Finally, the proposed strategies are applied on a real-size case study demonstrating their relevance and validity to test interactive applications.

Poverty reduction is a complex global challenge where renewable energy emerges as a promising solution, particularly in resource-rich countries like Brazil. Renewable energy can enhance conditions in vulnerable communities without exacerbating climate change. This paper reviews the scientific literature on the role of renewable energy in alleviating energy poverty, focusing on Brazil and its global partnerships. Using the Web of Science database with the keywords poverty, renewable energy, and Brazil, this study analyzes 38 articles. The inclusion criteria were broad, focusing on the relationship between poverty in Brazil and renewable energy use. Keywords such as specific types of renewable energy (e.g., solar, wind) were excluded to avoid omitting important renewable sources. This review encompasses theoretical models, discourse analyses, case studies, and systematic reviews, addressing topics from rural electrification to socio-economic factors influencing sustainable energy adoption. Despite growing interest and international collaboration, research gaps remain, highlighting the need for increased academic engagement. The objective of this study is to address the existing gap in the literature concerning the impact of renewable energies on energy poverty in Brazil. Through a comprehensive review, this work maps current knowledge, identifies gaps, and highlights trends by integrating socio-economic analyses with technical assessments for a holistic perspective.