Quality 4.0: using text mining to reveal trending topics
Quality 4.0: using text mining to reveal trending topics
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Skill set needed for quality professionals to embrace novel approaches, as well as the absence of clearly defined implement ation roadmaps. The use of a powerful and data-oriented technique to explore Quality 4.0 perfectly fits with the essence of this novel concept. In conclusion, through the utilization of a state-of-the-art text mining technique to explore an emergent concept within the realm of Quality and its practical applications, this chapter provides readers with The pursuit of quality and operational excellence is an ongoing journey for organisations seeking sustainable success in today’s dynamic business environment. This chapter explores the latest trends shaping the field of quality management and operational excellence by focusing on Quality 4.0, a concept that embodies the pervasive integration of digital technology into quality management practices. To understand the most prominent topics surrounding Quality 4.0 within the literature spanning from January 2016 to December 2023, this study employs a text mining technique known as Latent Dirichlet Allocation (LDA), which, to the best of the authors’ knowledge, has not been previously utilized for this purpose. The LDA algorithm identified twenty-two distinct topics, which were broadly grouped into four main categories: Historical Aspects, Human Factors, Implementation, Literature Analysis, and Technological Applications. These subjects highlight several facets of this subject, including the uncertainties regarding the invaluable insights to effectively navigate the dynamic landscape of Quality 4.0.
Hermilio Vilarinho is a Researcher at INESC TEC at the Faculty of Engineering (FEUP) of the University of Porto (Portugal). He holds a PhD in Industrial Engineering and Management from FEUP (2023) and a degree in Mechanical Engineering from the Federal University of Bahia, Brazil and Business Administration from the Catholic University of Salvador, Brazil. He has a master’s degree in management and a specialisation in Environmental Management from the Federal University of Bahia. He has worked as an Engineering professional in leading positions in Brazil, USA, Angola and Japan, with emphasis on Quality Management and Project Management. He has also worked as a university professor.
Miguel Rodrigues holds a PhD in Industrial Engineering and Management from FEUP (2024) and a Master degree in Management from Católica Porto Business School. He has been involved with quantitative and qualitative research, with a focus on machine learning forecasts, and is currently following a career as an MLOps engineer.
Henriqueta Nóvoa is an Associate Professor at the Faculty of Engineering (FEUP) of the University of Porto(Portugal). She holds a Ph.D. in Engineering Sciences from FEUP (2000) and a first degree in Electrical Engineering from FEUP. She has worked as a production test engineer at Texas Instruments for 6 years. She has been teaching courses in Quality Management, Statistics and Strategic Planning of Information Systems since 1990. She has been involved in research projects in diverse areas, such as wine industry, construction industry and packaging. She published more than 30 refereed papers in international conferences and scientific journals.
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Quality, operational excellence, Latent Dirichlet Allocation, quality 4.0.
