Quality Engineering and Management Group at the 6th International Conference on Quality Engineering and Management
From June 13 to 14, 2024, a delegation from the Quality Engineering and Management group participated in the 6th International Conference on Quality Engineering and Management held in Girona, Spain. This biannual event, renowned for gathering esteemed professionals, academics, and researchers from across the globe, provided an excellent platform for sharing knowledge, experiences, and advancements in the field of quality.
The conference facilitated numerous opportunities to reconnect with friends and colleagues and meet new researchers dedicated to quality engineering.
Our delegation from Politecnico di Torino presented five scientific works, showcasing our research and contributions to the field.
Enhancing Manufacturing Quality through Gamification: A Preliminary Study in a Collaborative Assembly Process
Authors: Matteo Capponi, Angelo Nicola Culotta, Riccardo Gervasi, Luca Mastrogiacomo, Fiorenzo Franceschini
This research delves into the innovative application of gamification in the manufacturing sector. It particularly focuses on its potential to boost engagement, quality, and productivity in collaborative assembly processes involving humans and collaborative robots (cobots). The study, conducted using the Octalysis framework, incorporated game design elements like scoring systems, progress tracking, and real-time feedback into a tile cutter assembly process. Preliminary findings suggest that these gamification elements significantly enhance operator engagement and, consequently, improve product and process quality.
Digital Traceability of Quality in the Food Processing Industry
Authors: Elisa Verna, Gianfranco Genta, Maurizio Galetto
This study examines the enhancement of quality traceability in the food processing industry through modern digital tools, particularly blockchain technology. By combining a comprehensive literature review with real-case studies analysis, the research demonstrates that blockchain-based approaches significantly enhance supply chain transparency and quality management. The findings provide practical guidelines for businesses in the food processing sector looking to implement these modern digital tools to meet societal demands for food safety and quality.
Digital Traceability of Quality in the Food Processing Industry 
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Effects of Digital VoC Anomalies on Product Quality Tracking Strategies
Authors: Federico Barravecchia, Mirco Bartolomei, Luca Mastrogiacomo, Fiorenzo Franceschini
Investigating the use of digital Voice of Customer (VoC) data in quality management, this article presents a preliminary framework for categorizing anomalies within the digital VoC. By analyzing vast volumes of unstructured textual data using Quality 4.0 approaches and text mining techniques, the study identifies deviations from expected feedback patterns. These anomalies are critical in highlighting areas for improvement, thus playing a pivotal role in quality management.
Quality of Remote STEM Education: Gap Analysis from the Perspectives of Students and Faculty
Authors: Domenico Augusto Maisano, Giovanna Carrera, Luca Mastrogiacomo, Fiorenzo Franceschini
This study assesses the critical aspects of remote STEM education from the perspectives of students and faculty from various European universities, in the context of the EU-funded REMOTE project. Utilizing structured questionnaires, the research identifies discrepancies in perceptions regarding assessment feedback and student-faculty interaction. These insights are valuable for educational policymakers and institutions aiming to refine online teaching strategies and assessment methods.
Quality of Bibliometric Databases: Accuracy in Classification of Document Types
Authors: Domenico Augusto Maisano, Luca Mastrogiacomo, Lucrezia Ferrara, Fiorenzo Franceschini
This research introduces a semi-automated methodology to assess the accuracy of document type (DT) classification in bibliometric databases such as Scopus and Web of Science. By identifying and analyzing discrepancies in DT classifications, the study provides a path to improving the quality and reliability of these databases. The findings, based on a realistic application example, highlight the effectiveness of the methodology in identifying DT-classification errors and suggest improvements for more reliable bibliometric indicators.
