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The transition towards Industry 4.0 and the increasing implementation of new
digital technologies in industrial operations are creating new challenges and
opportunities concerning human work and work organization. Overcoming these
challenges and taking advantage of the emerging opportunities require new
sociotechnical and human-centered design and...
Contexts in source publication
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... Chapter 3, I will present the overall methodology of the Ph.D. project and the pertaining methodological considerations and decisions. Thus, Chapter 3 covers the applied research philosophy (Section 3.1), the research approach (Section 3.2), research design, as well as the applied research methods (Section 3.4). ...
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... to Figure 3 for an illustration showing an overview of the four industrial revolutions. Industry 1.0 refers to the first industrial revolution (or the British Industrial Revolution), which happened around the end of the 18 th century, leading to the creation of the first mechanical manufacturing facilities. ...
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... we categorized each statement coded as a challenge into these five dimensions. Refer to Figure 30 for an illustration of the SOFT work system model recreated for this paper. Hereafter, we created a two-level affinity diagram (Holtzblatt & Beyer, 2016) for each of these five dimensions, where in the first level, we grouped the challenges and, on the second level, divided them into themes. ...
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... approach for using the framework during the workshop followed a seven-step process, where each step focused on a specific element of the framework. Refer to Figure 33 for an overview of these seven steps. ...
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... addition to the financial aspects, the seven steps approach (shown in Figure 33) aims at ensuring a systematic approach for aligning humans, organizational, and technological elements of a work system, with specific objectives and measurable targets for dealing with current and upcoming challenges. Applying HCD and encouraging the users to define a vision and value proposition might both limit organizational friction as well as investments in invaluable digital solutions. ...
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... believe that it would be highly relevant, necessary, and interesting to conduct further research on the network of actors involved in the transition to Industry 4.0. Figure 35 shows an overview of the different actors that can influence and affect HF/E aspects of work systems adopted from Dul et al. (2012). However, the model from Figure 35 includes technology suppliers and differentiates between external and internal system influencers. ...
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... 35 shows an overview of the different actors that can influence and affect HF/E aspects of work systems adopted from Dul et al. (2012). However, the model from Figure 35 includes technology suppliers and differentiates between external and internal system influencers. Because of time constraints, this Ph.D. project only included considerations regarding System-actors, experts, decisionmakers, and internal system influencers. ...
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... today's cobots are very similar to traditional industrial robots, (with the additional ability to work with human workers without any enclosure) first generation cobots did not have motors, were intrinsically passive in the plane of operation, and had brakes. Figure 36 shows a modern day cobot from Universal Robots. Current research within the field of human-robot interactions is suggesting the need for further investigation and evaluation of challenges concerning performance, functionality, usability and environment conditions in the design and implementation of industrial work systems with cobots ( Djuric et al., 2016). ...
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... framework's intended users are decision makers on the three organizational levels, strategic, tactical, and operational, which we have also have specified in the framework. Refer to Figure 37 for an overview of the framework. ...
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... Chapter 3, I will present the overall methodology of the Ph.D. project and the pertaining methodological considerations and decisions. Thus, Chapter 3 covers the applied research philosophy (Section 3.1), the research approach (Section 3.2), research design, as well as the applied research methods (Section 3.4). ...
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... to Figure 3 for an illustration showing an overview of the four industrial revolutions. Industry 1.0 refers to the first industrial revolution (or the British Industrial Revolution), which happened around the end of the 18 th century, leading to the creation of the first mechanical manufacturing facilities. ...
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... we categorized each statement coded as a challenge into these five dimensions. Refer to Figure 30 for an illustration of the SOFT work system model recreated for this paper. Hereafter, we created a two-level affinity diagram (Holtzblatt & Beyer, 2016) for each of these five dimensions, where in the first level, we grouped the challenges and, on the second level, divided them into themes. ...
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... approach for using the framework during the workshop followed a seven-step process, where each step focused on a specific element of the framework. Refer to Figure 33 for an overview of these seven steps. ...
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... addition to the financial aspects, the seven steps approach (shown in Figure 33) aims at ensuring a systematic approach for aligning humans, organizational, and technological elements of a work system, with specific objectives and measurable targets for dealing with current and upcoming challenges. Applying HCD and encouraging the users to define a vision and value proposition might both limit organizational friction as well as investments in invaluable digital solutions. ...
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... believe that it would be highly relevant, necessary, and interesting to conduct further research on the network of actors involved in the transition to Industry 4.0. Figure 35 shows an overview of the different actors that can influence and affect HF/E aspects of work systems adopted from Dul et al. (2012). However, the model from Figure 35 includes technology suppliers and differentiates between external and internal system influencers. ...
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... 35 shows an overview of the different actors that can influence and affect HF/E aspects of work systems adopted from Dul et al. (2012). However, the model from Figure 35 includes technology suppliers and differentiates between external and internal system influencers. Because of time constraints, this Ph.D. project only included considerations regarding System-actors, experts, decisionmakers, and internal system influencers. ...
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... today's cobots are very similar to traditional industrial robots, (with the additional ability to work with human workers without any enclosure) first generation cobots did not have motors, were intrinsically passive in the plane of operation, and had brakes. Figure 36 shows a modern day cobot from Universal Robots. Current research within the field of human-robot interactions is suggesting the need for further investigation and evaluation of challenges concerning performance, functionality, usability and environment conditions in the design and implementation of industrial work systems with cobots ( Djuric et al., 2016). ...
Citations
... the meta-synthesis method (inductive approach). Then, the frameworks and models of digital transformation presented in the same sources were used as a pattern, employing the deductive approach to adjust the final framework based on them (Butler et al., 2018;Charmaz, 2006;Coyne, 1997;Glaser & Strauss, 2017;Kadir, 2020). ...
Digital transformation has become increasingly important in today’s world. This transformation enables governments and organizations to improve their efficiency and effectiveness by leveraging new technologies. One of the expected outcomes and results of digital transformation is the integration of information and communication technology with the knowledge-based economy. However, the process of digital transformation is complex and multifaceted, and numerous factors can impact its success. The objective of this study is to create a comprehensive framework for identifying and categorizing the factors influencing digital transformation at the national level, particularly for implementation by the Iranian government. To this end, a systematic literature review was conducted, analyzing 70 studies on digital transformation from January 2018 to December 2022 across 12 databases. The relevant data was extracted to achieve the research objectives, which ultimately led to the creation of a two-layer framework of factors affecting digital transformation. The research findings identified themes and sub-themes influencing digital transformation, including sub-themes related to knowledge, literacy, skills, and the knowledge-based economy. These sub-themes implicitly demonstrate the conceptual relationship between digital transformation and the knowledge-based economy. The final framework resulting from this research offers a comprehensive and systematic view of the factors influencing digital transformation from a national perspective. This framework provides valuable insights for governments, organizations, policymakers, and researchers. It allows them to accurately assess the current digital state of the country and implement effective strategies to accelerate digital transformation and leverage it within the knowledge-based economy.
... The pillars of Industry 4.0 encompass various technological advancements, but Rüßmann et al. (2015) identified nine pillars that are some of the most significant ones changing industrial production including big data and analytics, simulation, autonomous robots, system integration (horizontal and vertical), Internet of Things (IoT), cybersecurity, cloud computing, Additive Manufacturing (AM) and Augmented or Virtual Reality. Big data and analytics provide businesses with a deeper understanding of their operational environment (Kadir, 2020). Simulation enables real-time data analysis and seamless integration of physical and virtual worlds (Zhang et al., 2017). ...
The Fourth Industrial Revolution (4IR) is reshaping the landscape of the tourism industry, presenting both unprecedented opportunities and complex challenges. This study explores the prospects and challenges posed by 4IR for tourism through a literature review. The objective is to provide a comprehensive understanding of the interplay between technological advancements and the tourism sector. Employing a rigorous methodology, peer-reviewed academic articles, conference papers, and relevant book chapters were analyzed, focusing on key technologies such as artificial intelligence, the Internet of Things, robotics, and big data. The results highlight promising prospects, including enhanced customer experience, smart destinations, augmented and virtual reality, data-driven decision making, sustainable tourism practices and increased efficiency and productivity alongside challenges like data security and privacy concerns, digital divide, job displacement, overreliance on technology, ethical considerations and infrastructure and investment. The paper's key contribution lies in it's provision of categorical perspective of the various impacts of 4IR on tourism. Also, the study provides valuable insights for stakeholders, policymakers, and industry players to navigate the transformative era of the Fourth Industrial Revolution in the tourism sector. Moreover, the study identified gaps in existing literature and provides direction for future works.
... Disruptive technologies can be defined by their market effects [45] Machines 2024, 12, 320 5 of 31 and customer implications [46]; they have the potential to create new markets and aim to transform companies and businesses [47]. A study identified 35 of these disruptive technologies, recognizing 13 as highly relevant and considering 9 of them the pillars of Industry 4.0 [48]: Big Data, Cybersecurity, additive manufacturing, Horizontal and Vertical Systems, augmented reality, simulation, Cloud Computing, the Internet of Things and Autonomous Robots [49][50][51]. ...
Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world and is impacting work and education in general. In fact, the disruptive technologies that frame the Fourth Industrial Revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which could lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example, Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on mechanics. However, the specialized literature on Industry 4.0 says little about the importance of mechanics in the new industrial era, and more importance is placed on the evolution of Information and Communication Technologies and artificial intelligence. This article presents a frame of reference for the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between mechanics and artificial intelligence. To analyze Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined mechanics in the Third Industrial Revolution were used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes to the educational models of engineering education is described.
... Other authors point out that the fourth industrial revolution is based on nine disruptive technological pillars, which are: Big Data, Cybersecurity, Additive Manufacturing, Horizontal and Vertical Systems, Augmented Reality, Simulation, Cloud Computing, Internet of Things and Autonomous Robots [53,54]. Figure 3 shows the technological pillars of Industry 4.0 [55]. ...
... Technology pillars of Industry 4.0[55]. ...
Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world, and is impacting work and education in general. In fact, the disruptive technologies that frame the fourth industrial revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which can lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on Mechanics. However, the specialized literature on Industry 4.0 says little about the importance of Mechanics in the new industrial era and more importance is given to the evolution of Information and Communication Technologies and Artificial Intelligence. This article presents a frame of reference about the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between Mechanics and Artificial Intelligence. For the analysis of Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined Mechanics in the third industrial revolution was used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes in the educational models of engineering education is described.
... Organizational ergonomics studies the optimization and efficiency of sociotechnical systems, including organizational structures, policies, and processes. This paper is grounded in the field of cognitive ergonomics, which focuses on the characteristics of human psychological activity and mental processes, and investigates their impact on the interaction between humans and systems [4]. Cognitive ergonomics explores various aspects such as perception, memory, reasoning, motor response, and human-computer interaction. ...
With the advancement of computer hardware and communication technologies, deep learning technology has made significant progress, enabling the development of systems that can accurately estimate human emotions. Factors such as facial expressions, gender, age, and the environment influence human emotions, making it crucial to understand and capture these intricate factors. Our system aims to recommend personalized images by accurately estimating human emotions, age, and gender in real time. The primary objective of our system is to enhance user experiences by recommending images that align with their current emotional state and characteristics. To achieve this, our system collects environmental information, including weather conditions and user-specific environment data through APIs and smartphone sensors. Additionally, we employ deep learning algorithms for real-time classification of eight types of facial expressions, age, and gender. By combining this facial information with the environmental data, we categorize the user’s current situation into positive, neutral, and negative stages. Based on this categorization, our system recommends natural landscape images that are colorized using Generative Adversarial Networks (GANs). These recommendations are personalized to match the user’s current emotional state and preferences, providing a more engaging and tailored experience. Through rigorous testing and user evaluations, we assessed the effectiveness and user-friendliness of our system. Users expressed satisfaction with the system’s ability to generate appropriate images based on the surrounding environment, emotional state, and demographic factors such as age and gender. The visual output of our system significantly impacted users’ emotional responses, resulting in a positive mood change for most users. Moreover, the system’s scalability was positively received, with users acknowledging its potential benefits when installed outdoors and expressing a willingness to continue using it. Compared to other recommender systems, our integration of age, gender, and weather information provides personalized recommendations, contextual relevance, increased engagement, and a deeper understanding of user preferences, thereby enhancing the overall user experience. The system’s ability to comprehend and capture intricate factors that influence human emotions holds promise in various domains, including human–computer interaction, psychology, and social sciences.
... From Industry 1.0 to 4.0[6]. ...
The publication analyzes aspects of energy efficiency of various types and several technological concepts of facade washing devices. The conducted analyses and tests answered the most essential question of this stage: which, from the technical point of view of solving the problem of stabilizing the track of the washing machine, gives the highest guarantee of effective stabilization of this track in unfavorable wind conditions. The literature analysis showed several solutions to the problem of track stabilization of facade washing machines on the market, of which suction cups stabilize the machine device, a system not attached to the wall of the building, and fans or propellers have been commercialized. However, it pointed out that there are no universal solutions. Detailed analysis of solutions under many criteria led to finding the solution with the fewest defects at this stage of analysis and potentially the greatest chance of success. Thanks to the results of work and research on the effectiveness of technology, it was possible to implement a number of solutions leading to the improvement of work efficiency, safety, and the development of Industry 4.0.
... Nevertheless, it provides an easy solution for both engineers and workers giving a highly collaborative perspective. A key advantage of AR usage in the context of the design phase is the ability to invite several stakeholders and share the same understanding of a design and work situation in a collaborative mindset ( Figure 5) (Kadir, 2020).This innovative approach is in line with the new working methods deployed in many industries in recent years (Khalek et al., 2019). In the context of a new development project, we had to specify, verify and validate a stepping concept definition to allow access to the engine and rotor area of a helicopter during a preflight check. ...
... Similarly to other human factors and ergonomics frameworks related to HRC, such as the one developed by Kadir (2020), the guidelines presented and evaluated in the current study could benefit from the integration with other human factors frameworks and techniques such as Cognitive Work Analysis (CWT) (Vicente, 1999), Cognitive Tasks Analysis (CTA) (Crandall et al., 2006), and Hierarchical Task Analysis (HTA) (Hollnagel, 2003). As it was suggested by Kadir (2020), the developed guidelines could be further improved and refined through consultations and involvement with experts in CTA, CWT and HTA. ...
... Similarly to other human factors and ergonomics frameworks related to HRC, such as the one developed by Kadir (2020), the guidelines presented and evaluated in the current study could benefit from the integration with other human factors frameworks and techniques such as Cognitive Work Analysis (CWT) (Vicente, 1999), Cognitive Tasks Analysis (CTA) (Crandall et al., 2006), and Hierarchical Task Analysis (HTA) (Hollnagel, 2003). As it was suggested by Kadir (2020), the developed guidelines could be further improved and refined through consultations and involvement with experts in CTA, CWT and HTA. Similarly, those methods could draw important suggestions in their applications by the guidelines here presented. ...
Industry 4.0 is the concept used to summarize the ongoing fourth industrial revolution, which is profoundly changing the manufacturing systems and business models all over the world. Collaborative robotics is one of the most promising technologies of Industry 4.0. Human-robot interaction and human-robot collaboration will be crucial for enhancing the operator's work conditions and production performance. In this regard, this enabling technology opens new possibilities but also new challenges. There is no doubt that safety is of primary importance when humans and robots interact in industrial settings. Nevertheless, human factors and cognitive ergonomics (i.e. cognitive workload, usability, trust, acceptance, stress, frustration, perceived enjoyment) are crucial, even if they are often underestimated or ignored. Therefore, this work refers to cognitive ergonomics in the design of human-robot collaborative assembly systems. A set of design guidelines has been developed according to the analysis of the scientific literature. Their effectiveness has been evaluated through multiple experiments based on a laboratory case study where different participants interacted with a low-payload collaborative robotic system for the joint assembly of a manufacturing product. The main assumption to be tested is that it is possible to improve the operator's experience and efficiency by manipulating the system features and interaction patterns according to the proposed design guidelines. Results confirmed that participants improved their cognitive response to human-robot interaction as well as the assembly performance with the enhancement of workstation features and interaction conditions by implementing an increasing number of guidelines.
... Es un concepto que refiere a un conjunto de transformaciones tecnológicas productivas e institucionales, que permiten la optimización de procesos productivos, otorgando una nueva economía tecnificada y conectada a gran profundidad. En su tesis doctoral Kadir afirma que la introducción e integración de estas nuevas tecnologías en las cadenas productivas tendrá un gran impacto socio-tecno-económico, (Kadir, 2020). ...
Este trabajo fue una propuesta de proyecto de tecnologías 4.0 para usinas lácteas presentado en concurso federal de ideas proyectos (Argentina).
Resumen:
La industria láctea Argentina (usinas lácteas) vive en un entorno de constante cambio y evolución, actualmente enfrentándose a importantes tendencias de los consumidores y retos tecnológicos. Las tendencias del mercado y la demanda apuntan hacia una mayor personalización de los productos, variedad de envases, comidas más saludables, mayores exigencias regulatorias en calidad y procesos, mayor planificación de la producción, entre otras. Sin embargo, una de las grandes preocupaciones del sector quesero es la seguridad alimentaria y la reinvención a alimentos más saludables. Los consumidores comienzan a preocuparse tanto por los ingredientes utilizados como toda información proveniente de los ciclos de producción. Esto obliga a las industrias a replantearse y recrearse para seguir siendo competitivas en el campo, tanto en la innovación en investigación y desarrollo de nuevos alimentos, como en la producción y tecnologías de conservación y aseguramiento de la calidad. En este sentido, las tecnologías de Industria 4.0 han irrumpido con fuerza en el sector y vienen a dar respuestas a estas necesidades. Sin embargo, existe una dificultad marcada en su implementación, principalmente porque el sector está formado en gran parte por pequeñas y medianas empresas (PyMEs), cuyo margen de beneficios son bajos. Generalmente las empresas queseras asocian su puesta en práctica con grandes inversiones que no pueden permitir. En este sentido, nuestro grupo de trabajo conoce que el paradigma 4.0 es un proceso progresivo. Por este motivo, planteamos la idea de comenzar con una restructuración interna basadas principalmente en la realidad de cada PyME, priorizando el principal aspecto: su producción. Por lo tanto, la propuesta presentada tiene que ver con una arquitectura de IIoT adaptada para las usinas lácteas, con una etapa de implementación inicial que tienen que ver con la propuesta de arquitectura, instalación de tecnologías de sensado, registro y monitoreo en tiempo real de las principales variables fisicoquímicas en las tinas de producción de queso, variables ambientales en las áreas de maduración y almacenamiento, consumo eléctrico y de las principales parámetros de funcionamiento de las máquinas de soporte de los procesos productivos.
... ▪ Capacidad: Carga útil; Ejes; Alcance; Velocidad; Movilidad; Actuación ▪ Entrada de partes ▪ Operación: Alimentación de elemento; Manejo del elemento; Montaje del elemento ▪ Salida del sub-elaborado Respecto de esta situación de análisis, el autor [34] considera que la Capacidad es la clave de la evaluación, mientras que el investigador [44], en su trabajo "Capability-based task allocation in humanrobot collaboration" asegura que la calve está en el tipo de material a manipular, el autor [27] agrega que el factor crítico es en realidad el agarre. Si se consideraran sistemas de ensamblaje colaborativo multi-robot multi-operador, el conjunto Entrada Salida es particularmente importante [25]. ...
... Conforme estudios realizados, los especialistas[24] aseguran que existe una brecha entre las unidades de fabricación existentes y la necesaria para la I4.0. La introducción de estas nuevas tecnologías y la integración de otros habilitadores de la I4.0 están teniendo un importante impacto sociotécnico[25]. La I4.0, la cual comenzó como resultado de la era de la información y la tecnología, incluye sistemas que reciben, transmiten, evalúan y administran datos. ...
The aim of this work was the development of a reference framework and methodology for the technical feasibility study of the use of collaborative robots in manufacturing SMEs.
According to our previous study, the technical factor is one of the five evaluation variables for decision-making for the incorporation of Cobots in workstations, being them: the
technical factor, the ergonometric factor, the quality, the economic-financial factor. and the regulatory one. This work has been carried out in three phases: (1) a systematic analysis of the literature regarding the use of Cobots in SMEs and its corresponding technical evaluation was carried out, (2) the evaluation method was selected, and (3) the applied to a case of incorporation of a Cobots in a workstation of a small company that manufactures electrical products in Argentina. This methodology collaborated with the decision-making of senior management to understand whether the activities of feeding, handling and assembling electrical parts and components could be carried out efficiently and effectively by means of a robotic arm.
