Omar Shatarat, Alessandro Dausilio, Valentina Lazzara, Velia Malizia, Stefania La Grutta and Giuseppe Aiello
Presenting author: Omar Shatarat
Abstract
This study presents a proof-of-concept method for ventilation-informed workplace-layout assessment. Measured carbon dioxide decay was combined with a computational fluid dynamics model of an occupied indoor room during one natural cross-ventilation event. After validation against measurements at a central monitoring point, the simulated CO2 field was evaluated within the breathing zone and local volumes around each seated operator. Mean concentration, percentile concentration, and the fraction of local volume above a comparative threshold characterized spatial and temporal dilution. Cross-ventilation reduced average CO2 concentration, but the improvement was spatially non-uniform. At the end of the analyzed period, op7 and op4 retained the highest local fractions above the threshold. The method identifies positions where layout or ventilation interventions should be prioritized. It serves as a screening and decision-support procedure rather than a completed layout optimization; comparison of alternative configurations is reserved for future work.
Sota Kimpara, Miku Kikkawa, Kojiro Ohara and Sumika Arima
Presenting author: Miku Kikkawa
Abstract
While crane operations in manufacturing are inherently high-risk, safety monitoring remains predominantly manual -even when supported by video surveillance- and this approach has proven insufficient to reduce industrial accident rates. To address this limitation, this study proposes a smart monitoring system using RGB-D data to automatically detect hazardous behaviors: "Distraction" and "Contact". For the Distraction detection, the system integrates YOLO-face, Gaze360, and LiDAR data to quantify 3D attention clusters, prioritizing distracted gaze during load movement to reduce false alarms. For the Contact detection, YOLO-Pose and ByteTrack enable consistent 3D coordinate tracking of operators and loads, approximating the load as a geometric model to identify physical proximity. This novelty lies in achieving robust 3D behavior understanding using a single monocular RGB-D sensor in dynamic environments. Experimental results demonstrate the feasibility of the proposed framework, providing a foundation for future real-time industrial safety monitoring.
Aleli Ruizan, Yoshiki Kurata, Jonathan Gregorio and Nhoela Jill Torreliza
Presenting author: Aleli J. Ruizan
Abstract
This study examined the impact of ethical dissonance and operational burnout on conflict management failure in the high-pressure industrial environments in the Philippines. Using the Lean Six Sigma (LSS) approach and the DMAIC framework, this study analyzed survey data from 50 professionals across sectors, including construction, government agencies, IT, and BPO. The study identified how to prioritize operational targets over personal values. This is coupled with high workloads and impairs collaborative dispute resolution. A Cronbach’s alpha value of 0.913 indicated high reliability. Results show that increased burnout and ethical strain are associated with greater reliance on avoidance-based or reactive conflict strategies. A conflict management failure index is established as a key performance indicator to measure organizational defects. Through lean principles and innovative structural interventions, the paper proposes strategies to mitigate communication breakdown and improve sustainable resolution processes in stressful industrial settings.
This paper develops a framework explaining how Industry 4.0 critical success factor drivers enhance sustainability and circular economy performance in UAE manufacturing. Although digital technologies are presented as sustainability enablers, limited studies explain the causal ordering of technological, organizational, human, supply-chain and policy drivers that shape sustainability transformation in industrial settings. A literature-derived set of critical success factor drivers was validated by 10 experts from manufacturing, sustainability, digital transformation and academic backgrounds. Their pairwise judgments were analyzed using Interpretive Structural Modeling (ISM) and MICMAC analysis to identify hierarchical relationships and driving-dependence structure. The results identify digital infrastructure, leadership commitment, data governance and policy support as influential driving critical success factors. Organizational readiness, human capital, supply-chain integration and implementation maturity form the linkage layer, while sustainability and circular economy performance are dependent outcomes. The paper provides a reproducible decision framework for sequencing Industry 4.0 investments toward sustainability and circular economy transformation.
