Papers

In the endeavor to advance industrial engineering and management (IEM) education, this research underscores the imperative of supporting a dynamic and responsive adaptation of a competency-based curriculum (CBC) to meet the demands of an ever-evolving industrial landscape and job market. Our study contributes to competency-based education (CBE) by demonstrating how Artificial Intelligence (AI) can inform the definition of a CBC in the IEM field, thus initiating the pioneering steps towards a collaborative human-AI approach in CBC design. Through a stepwise methodology based on semantic analysis, text mining, natural language processing (NLP) models, informetrics approaches, and clustering algorithms, we provide data-driven insights to inform the curriculum development process. This approach enabled us to identify educational gap, particularly in domains such as digital twin engineering and human-centric IEM. Moreover, this study advocates for higher education institutions (HEIs) to embrace a more structured and collaborative approach to continuously developing competency-based curricula. In this perspective, AI (including generative AI) emerges as a valuable ally in curriculum design. This approach proves instrumental in crafting competitive and appealing curricula, especially at peripheral universities. This study culminates in an updated WING model showing how to build Industry 5.0 related curricula and a series of recommendations for engineering educators.

This article explores the transformative potential of learning factories in mechatronic systems development. Learning factories offer a dynamic, collaborative environment that bridges the gap between academia and industry, creating a mutu- ally beneficial ecosystem. The LEONARDO project aims to develop innovative teaching methods, materials and tools for human-centric industrial engineering and management education leveraging on an industry 5.0 replica of a brewing system. Brewing as a process can be considered as highly complex, while brewing as a procedure serves as a ‘sexy vehicle’ for appealing student’s interest in industry5.0 applications and human-centric production. The brewing process is and will increasingly be more automated and highly supervised. For the latter, modern implementations of sensors such as electronic nose, electronic tongue, and infrared spectroscopy are required to be installed on the brewing equipment. To efficiently use the sensor outputs, the produced signals need to be merged locally and processed adequately, researched and investigated deeply by the authors up-front with the results to be summarized. Furthermore, to enable the physical bridging of various involved institutions across Europe, connecting the relevant sites virtually presents another technological challenge. Adequate IoT equipment needs to be selected and included in the whole setup as well. Furthermore, an emphasis needs to be made on the human-centric approach, as well as data visualization. Each of the aforementioned pieces of technology need a thorough investigation along with a decent focus in integrating the puzzle pieces into the big picture which is the brewing plant. In this paper we describe the interaction along with the system integration strategies of the listed fields to enable a future proof industry 5.0 ready brewing plant, focusing on the human-centric approach demanded in the industry5.0 feature description.

This article explores the transformative potential of learning factories in mechatronic systems development. Learning factories offer a dynamic, collaborative environment that bridges the gap between academia and industry, creating a mutu- ally beneficial ecosystem. The LEONARDO project aims to develop innovative teaching methods, materials and tools for human-centric industrial engineering and management education leveraging on an industry 5.0 replica of a brewing system. Brewing as a process can be considered as highly complex, while brewing as a procedure serves as a ‘sexy vehicle’ for appealing student’s interest in industry5.0 applications and human-centric production. The brewing process is and will increasingly be more automated and highly supervised. For the latter, modern implementations of sensors such as electronic nose, electronic tongue, and infrared spectroscopy are required to be installed on the brewing equipment. To efficiently use the sensor outputs, the produced signals need to be merged locally and processed adequately, researched and investigated deeply by the authors up-front with the results to be summarized. Furthermore, to enable the physical bridging of various involved institutions across Europe, connecting the relevant sites virtually presents another technological challenge. Adequate IoT equipment needs to be selected and included in the whole setup as well. Furthermore, an emphasis needs to be made on the human-centric approach, as well as data visualization. Each of the aforementioned pieces of technology need a thorough investigation along with a decent focus in integrating the puzzle pieces into the big picture which is the brewing plant. In this paper we describe the interaction along with the system integration strategies of the listed fields to enable a future proof industry 5.0 ready brewing plant, focusing on the human-centric approach demanded in the industry5.0 feature description.

In the endeavor to advance industrial engineering and management (IEM) education, this research underscores the imperative of supporting a dynamic and responsive adaptation of a competency-based curriculum (CBC) to meet the demands of an ever-evolving industrial landscape and job market. Our study contributes to competency-based education (CBE) by demonstrating how Artificial Intelligence (AI) can inform the definition of a CBC in the IEM field, thus initiating the pioneering steps towards a collaborative human-AI approach in CBC design. Through a stepwise methodology based on semantic analysis, text mining, natural language processing (NLP) models, informetrics approaches, and clustering algorithms, we provide data-driven insights to inform the curriculum development process. This approach enabled us to identify educational gap, particularly in domains such as digital twin engineering and human-centric IEM. Moreover, this study advocates for higher education institutions (HEIs) to embrace a more structured and collaborative approach to continuously developing competency-based curricula. In this perspective, AI (including generative AI) emerges as a valuable ally in curriculum design. This approach proves instrumental in crafting competitive and appealing curricula, especially at peripheral universities. This study culminates in an updated WING model showing how to build Industry 5.0 related curricula and a series of recommendations for engineering educators.

The transition from Industry 4.0 towards Industry 5.0 marks a paradigm shift, emphasising human-centricity in industrial settings. Industry 5.0 focuses on improving the future role of people in addition to merely technological progress. While “human centricity” gains recognition, ambiguity surrounds its definition and application. The literature lacks clear consensus on the concept and its industrial implications. This paper provides clarity on human-centricity by analysing viewpoints and public opinions based on posts published on LinkedIn in the last five years regarding human-centricity. The analysis involved text mining techniques, including semantic clustering to discover distinct clusters of discussions related to human-centricity and keywords extraction to tag the different clusters. The findings reveal that public opinion predominantly centres on the skills required by future workers, encompassing both hard and soft skills, as well as social themes such as gender equity and workplace comfort. This research underscores the critical relevance of these components in the transition towards Industry 5.0, offering valuable insights for industrial practitioners and researchers alike.