Percorrer por autor "Tenera, Alexandra"
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- Designing strategic scenarios for the digital transitionPublication . Alcácer, Vítor; Tenera, Alexandra; Araújo, Francisco; Carvalho, Helena; Cruz-Machado, VirgílioStrategic scenarios support decision-making under uncertainty; however, existing Industry 4.0 (I4.0) research predominantly adopts technological perspectives and does not provide methodologies that integrate risk man-agement into scenario development. This study addresses this gap by proposing a methodology for developing strategic scenarios for I4.0 adoption based on risk assessment and risk appetite. Following a Design Science Re-search approach, the study includes a systematic literature review (20 studies) to examine existing scenario development approaches in I4.0, which did not identify any methodologies incorporating risk management. This is complemented by a qualitative case study involving 15 experts from industrial and academic contexts. A structured risk analysis identified 35 risks across 9 categories, enabling the prioritization of key factors influencing digital transformation. The study results in the development of a validated methodological framework and four strategic scenarios derived from the interaction between risk levels and organizational risk appetite. The findings demonstrate how integrating risk management into scenario planning enhances strategic decision-making under uncertainty. This study contributes by addressing a critical gap in risk-based scenario methodologies for I4.0 and by proposing a novel framework that integrates risk management into strategic scenario development.
- A statistical analytics framework for decision-ready scheduling in autonomous intralogisticsPublication . Moura, Ricardo; Santos, Nuno Pessanha; Madureira, Ana; Tenera, Alexandra; ElsevierThe widespread adoption of Internet of Things (IoT) technologies within Industry 4.0 has increased the need for more responsive and efficient intralogistics operations, especially as industries move toward highly customized, small-batch production. Autonomous Mobile Robots (AMRs) provide the flexibility needed for dynamic industrial environments. However, their effective integration relies on accurate task-duration estimates, which are difficult to obtain due to system variability and operational uncertainty. This work addresses this challenge by statistically modeling picking and loading/unloading times using real-world industrial data. Probability distribution-fitting techniques are employed to capture the random nature of these operations, thereby reducing the need for unrealistic assumptions often used in scheduling models. The proposed methodology enhances the statistical basis for robust AMR fleet scheduling, improving coordination and reducing operational variability. Validation is conducted by directly comparing empirical data with fitted distributions, demonstrating clear improvements in representativeness and predictive accuracy. The results demonstrate the potential of this approach to improve decision-making in highly dynamic intralogistics environments.
- A statistical analytics framework for decision-ready scheduling in autonomous intralogisticsPublication . Moura, Ricardo; Santos, Nuno Pessanha; Madureira, Ana; Tenera, AlexandraThe widespread adoption of Internet of Things (IoT) technologies within Industry 4.0 has increased the need for more responsive and efficient intralogistics operations, especially as industries move toward highly customized, small-batch production. Autonomous Mobile Robots (AMRs) provide the flexibility needed for dynamic industrial environments. However, their effective integration relies on accurate task-duration estimates, which are difficult to obtain due to system variability and operational uncertainty. This work addresses this challenge by statistically modeling picking and loading/unloading times using real-world industrial data. Probability distribution-fitting techniques are employed to capture the random nature of these operations, thereby reducing the need for unrealistic assumptions often used in scheduling models. The proposed methodology enhances the statistical basis for robust AMR fleet scheduling, improving coordination and reducing operational variability. Validation is conducted by directly comparing empirical data with fitted distributions, demonstrating clear improvements in representativeness and predictive accuracy. The results demonstrate the potential of this approach to improve decision-making in highly dynamic intralogistics environments.