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.

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 apply 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.

The Brazilian craft beer market has experienced continuous growth, increasing operational challenges for small- and medium-sized breweries that frequently rely on empirical and spreadsheet-based production routines. These practices often lead to inefficient resource allocation, production instability, and sustainability concerns. This study proposes an integrated analytical framework combining Machine Learning (ML) and Operations Research (OR) to improve demand forecasting and production planning. The methodology is based on a synthetic dataset calibrated to the operational conditions of a Brasília-based craft brewery, incorporating realistic demand patterns such as seasonality, trend, and intermittency across multiple SKUs over an 18-month horizon. Forecasting models—including Moving Average, Single Exponential Smoothing, and a global ML-based proxy—were evaluated using rolling-origin validation. The resulting probabilistic forecasts were integrated into a capacity-constrained optimization model based on linear programming, extended with risk-aware decision-making using Conditional Value-at-Risk (CVaR). The results indicate that the ML-based approach achieved competitive forecasting performance (sMAPE = 5.83% and MAE = 11.76) while enabling the generation of capacity-feasible and risk-aware production plans aligned with service-level targets. The integration of probabilistic forecasts into the optimization model allowed explicit trade-offs between cost, service level, and resource utilization. The main contribution of this study lies in demonstrating how the integration of predictive and prescriptive analytics can support more sustainable production planning in resource-constrained manufacturing environments. By replacing ad hoc spreadsheet routines with a closed-loop decision-support system, the proposed framework advances the literature on data-driven PPC and provides practical guidance for SMEs operating under uncertainty.

A gestão de resíduos sólidos em instituições públicas de ensino superior representa um desafio crescente diante da necessidade de sustentabilidade, conformidade legal e eficiência operacional. Este estudo apresenta o desenvolvimento do SIGRES-UnB — Sistema Integrado de Gestão de Resíduos Sólidos — um protótipo de software voltado ao monitoramento, controle e análise dos resíduos gerados na Universidade de Brasília. A metodologia adotada baseou-se no ciclo PDCA (Planejar, Executar, Verificar e Agir), integrando levantamento documental, entrevistas com stakeholders e análise de requisitos técnicos para estruturar as funcionalidades do sistema. Os resultados incluem o design de doze telas principais e quatro modais complementares que abrangem processos de cadastro, coleta, destinação, transporte, indicadores e alertas normativos. O protótipo propõe uma gestão mais eficiente, transparente e alinhada às exigências legais, permitindo rastreabilidade e apoio à tomada de decisão. As contribuições abrangem tanto o fortalecimento das práticas sustentáveis da UnB quanto o avanço da literatura sobre integração entre metodologias de melhoria contínua e desenvolvimento de sistemas ambientais.

The broiler industry plays a crucial role in global food security and faces sustainability challenges that drive research aimed at assessing the environmental impacts of production. In this context, the objective of this study was to evaluate the environmental performance of different growth stages of broiler chickens in a Brazilian poultry industry. The analysis followed the Life Cycle Assessment (LCA) methodology and the ReCiPe 2016—Midpoint H calculation method. The data used were primary, provided by a company in the sector, and secondary, obtained from the Ecoinvent 3 inventory database. SimaPro version 9.5 software was used to perform the LCA analyses, with a functional unit of 1 kg of live broiler chicken and a system boundary at the farm gate. The results revealed that, at all stages, freshwater ecotoxicity had the greatest environmental impact, linked to the production of feed ingredients such as soybean and corn grains and crude soybean oil. Across the entire chain, the global warming potential value was 1.69 kg CO₂-eq, with the final fattening stage showing the worst environmental performance. The study contributes to quantifying environmental performance and identifying critical inputs associated with environmental burdens in broiler production systems, providing evidence to support environmental management and sustainability assessment in poultry production.