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Percorrer por autor "Pereira, Teresa"

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  • AI-Powered Data Management to Optimize Data Collection and Processing in a Painting Laboratory
    Publication . Pereira, Teresa; Oliveira, Marisa; Tavares, Miguel; Guimarães, André
    Industrial laboratories often remain under-digitized compared to production lines, creating a gap between data acquisition and analytical intelligence, critical for advanced quality control. This study addresses this gap by proposing and validating a novel framework that combines Low-Code digitalisation tools with Machine Learning (ML) and Causal Inference to optimise data collection and analysis in an automotive painting laboratory. A Microsoft Power Apps-based platform was developed in order to digitalise all measurement records, eliminating manual transcription errors (previously ≈ 40.01%) and reducing data-handling time by up to 34% of an operator’s shift, while enabling centralised, traceable storage and Power BI integration. Four datasets were used to assess predictive capacity with Random Forest, XGBoost and Neural Networks; Random Forest consistently provided the most stable results—Mean Absolute Error (MAE) of 0.972, Mean Absolute Percentage Error (MAPE) of 16.45%, and Root Mean Square Error (RMSE) of 1.307. Causal models (Linear Regression, DoWhy, Causal Forest, Double Machine Learning) consistently identified ultrafiltrate I solid content of the electrodeposition process as a dominant causal factor for defects. This study provides a novel framework that bridges digitalisation and ML-based causal reasoning in laboratory settings, offering a scalable approach that can be extended and replicated in other industrial sectors, aiming to develop smart, data-driven quality control systems.
    2025Artigo de investigação Acesso aberto Ver mais
  • Effects of Lean Tools and Industry 4.0 technology on productivity: An empirical study
    Publication . Guimarães, André; Oliveira, Eduardo e; Oliveira, Marisa; Pereira, Teresa
    Purpose: In the 4th Industrial Revolution era, companies are increasingly adopting strategies to maximize the potential of Industry 4.0 technologies. Many organizations integrate established Lean and Six Sigma tools to support the effective deployment of these innovations. Although existing literature explores the interplay between Industry 4.0 and Lean methodologies, there is limited focus on their direct impact on productivity. This study bridges that gap by analyzing how Industry 4.0 technologies and Lean and Six Sigma practices influence overall productivity, emphasizing two dimensions: operational efficiency, achieved through process optimization and waste reduction, and financial performance, centered on profitability and economic sustainability. Methodology: The investigation is conducted through an empirical study involving surveys of industrial companies in a central region of Portugal. The analysis of the research results includes the application of statistical tests, such as exploratory factor analysis, and the use of structural equation modeling techniques for confirmatory analysis. Findings: The results indicate that Industry 4.0 immediately impacts productivity. On the other hand, the influence of Lean and Six Sigma tools on productivity may not be immediate. Still, when analyzed over a more extensive time, their impact becomes more significant. Originality/value: This paper contributes significantly by presenting an empirical study that examines the impact of Lean tools and Industry 4.0 technologies on productivity. While the existing literature mainly consists of literature reviews or empirical analyses linking Lean tools and Industry 4.0, this study uniquely addresses the connection between these two concepts and productivity through an empirical study. Additionally, the findings emphasize that the influence of both Lean tools and Industry 4.0 on productivity is contingent on the duration since their implementation.
    2025Artigo de investigação Acesso restrito Ver mais
  • Industry 4.0 in Portugal: Validation of a Readiness Assessment Model Through an Empirical Approach
    Publication . Guimarães, André; Reis, Pedro; Pereira, Teresa; Antonio J. Marques Cardoso
    Purpose – Industry 4.0 requires businesses to adapt strategically and continuously assess their readiness. However, many manufacturing companies struggle to evaluate and implement Industry 4.0 due to the lack of clear assessment frameworks. This study addresses this gap by applying a structured maturity model to assess Industry 4.0 readiness in Portuguese companies. Design/methodology/approach – The study usesthe Shift2Future model, an adaptation of the IMPULS model designed for Portuguese companies. Data were collected through a structured questionnaire and empirical research validated the model. The internal consistency of responses, measured by Cronbach’s alpha (0.9040), confirmed its reliability. Findings – The results highlight the importance ofstructured assessmentsin guiding digital transformation. The Shift2Future model helps companies understand their current Industry 4.0 readiness and plan their transition. The study also shows that success requires more than just investing in technology; it demands a holistic approach, including strategy and workforce skills. Practical implications – This research provides a practical tool for companies to assess their Industry 4.0 readiness and identify areas for improvement. It can also help policymakers and business leaders develop strategies to support digital transformation. Originality/value – Thisstudy fills a gap in the literature by offering a structured, validated model tailored to the Portuguese industrial context. The Shift2Future model provides a reliable f
    2025Artigo de investigação Acesso aberto Ver mais