The Fourth Industrial Revolution, known as Industry 4.0, has transformed the manufacturing landscape by integrating advanced digital technologies, fostering automation, interconnectivity, and data-driven decision-making. Among these innovations, Digital Twins (DTs) have emerged as a pivotal tool, enabling real-time monitoring, simulation, and optimization of production processes. This paper provides a comprehensive exploration of DT technology, offering a strategic framework for its effective implementation within Industry 4.0 environments to enhance operational efficiency. The proposed methodology integrates key enabling technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning to create accurate digital replicas of manufacturing systems. Through a detailed case study, this work demonstrates how DTs can optimize production processes, reduce downtime, and improve maintenance strategies. The findings highlight DTs’ transformative potential in achieving continuous improvement, competitiveness, and operational excellence. This research aims to provide organizations with actionable insights and a roadmap to leverage DT technology for sustainable industrial innovation.

This study develops a strategic framework to guide the adoption of Industry 4.0 technologies across various business sectors. Using a mixed-methods approach, the research combines an extensive literature review with empirical investigations, including case studies and surveys with industry practitioners. The framework integrates advanced digital technologies, such as artificial intelligence and immersive solutions, and provides specific indicators to measure adoption progress. Key findings highlight critical business areas benefiting from digital transformation, such as production, operations management, and IT, showcasing improvements in resource management, operational efficiency, and innovation capabilities. The study’s originality lies in its comprehensive and actionable framework, validated through empirical data and aligned with international standards, offering practical tools for businesses navigating digital transformation.

Purpose: The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.Design/methodology/approach: The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.Findings: As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.Research limitations/implications: The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.Practical implications: The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.Originality/value: The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM.

The educational landscape is undergoing a transformative shift from conventional teaching methodologies towards experiential approaches, such as flipped classrooms, case-based learning, and university challenges. This paradigm change spurred our investigation to evaluate the influence of university challenges on students’ attitudinal development, alignment with future roles, and job satisfaction, aligning with Sustainable Development Goal 4: Quality Education. To achieve this, we devised a questionnaire and a personality test administered to two datasets of Engineering and Management students commencing in 2022. The first questionnaire integrated 249 items from the International Personality Item Pool (IPIP) and dimensions from the O*NET workstyles database, focusing on psychological constructs and job profile characteristics, contributing to the advancement of SDG 4’s goal for inclusive and quality education. The second questionnaire covered various occupational dimensions. Our findings revealed a positive correlation between participation in university challenges and analytical thinking and innovation, demonstrating the potential impact of experiential learning on crucial skill development. However, job satisfaction seemed to be influenced by multifaceted factors, with no discernible impact stemming from contest participation during academic studies. This study quantitatively underscores the influence of experiential teaching methods, particularly challenge-based learning, within the context of SDG 4, shedding light on how these approaches significantly shape students’ attitudes and perspectives. In the realm of education, the adoption of diverse teaching methodologies, such as collaborative teaching methods, learning factories, and active learning, has been on the rise, enriching the learning experience in university classrooms. Our research delves into the impact of integrating optional university challenges within Engineering and Management courses and their correlation with improved academic trajectories and enhanced job prospects. These findings carry significant implications for the evolution of university teaching methodologies and the definition of occupational profiles in the field of Industrial Engineering, offering valuable insights for business assessments in line with SDG 4.

In the realms of operations management (OM) and supply chain management (SCM), the significance of the human factor (HF) is increasingly recognised as a pivotal determinant of corporate performance. This burgeoning interest aligns with the recognition that individual characteristics—spanning personality traits, attitudes, and soft skills—play a critical role in enhancing organisational outcomes. Despite growing scrutiny, the discourse is hampered by terminological ambiguity and the conflation of critical human-centric concepts within the OSCM context. Addressing this gap, our study embarks on a mission to dissect and delineate the nuanced distinctions among “soft skills”, “attitudes”, and “personality traits”. By proposing a clear and actionable taxonomy, this paper aims to facilitate the practical application and understanding of these terms within organisational settings. Leveraging the “Knowledge Development Process-Constructivist” (ProKnow-C), we conducted a systematic examination of the existing scientific literature to unearth and critically review pertinent bibliometric and content analyses. Our work not only illuminates the path for future research but also underscores the necessity of clarity and precision in the conceptualisation and application of human-factor considerations in OM and SCM.