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Framework for collaborative practice
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Driven by cyber-physical systems, industrialization is on the edge of its fourth revolution, denominated ‘Industry 4.0’ in Germany. In the past, technological advancements were often only the starting point for productivity gains and had to be translated into organizational innovation in order to foster a pervasive improvement of productivity. Acco...
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... to accomplish a shared goal [27]. In particular, larger organizations face the challenge that knowledge and experi- ence is scattered among many employees in different disci- plines and consequently all relevant stakeholders need to be involved in many decision-making processes further assigning emphasis on collaboration [28]. As portrayed in Fig. 3, the framework for collaborative practice proposed here is detailed into three collaborative dimensions: coordination, cooperation and communication [29]. All dimensions consist of two collaborative practices each that in total do not necessarily represent a comprehensive listing, but are meant to facilitate ...
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The growing complexity of Cyber-Physical Systems (CPS), together with increasingly available par-allelism provided by multi-core chips, fosters the parallelization of simulation. Simulation speed-ups are expected from co-simulation and parallelization based on model splitting into weak-coupled sub-models, as for instance in the framework of Functio...
The Industry 4.0 vision, grounded on the integration of key technologies and Cyber-Physical-Systems (CPSs), is expected to profoundly modify the manufacturing sector. There is large consensus on the fact that work will change and different skills will be needed. However, whether the organization of work will evolve towards higher responsibility and...
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... Moreover, it is difficult to imagine that autonomy has no impact on collaboration within the team in which the operator works. The role of I5.0 employees will evolve toward that of decision-makers actively involved in a decision-making process that considers the whole context (Frazzon et al. 2013;Schuh et al. 2014). In this study, the notion of autonomy will be considered in the context of these visions. ...
The Industry 5.0 concept has placed human needs at the heart of industrial processes. This raises the question of how new technologies can enhance employee decision-making processes and influence the evolution of team autonomy. Recent studies have shown that the best way to measure these impacts is to conduct experiments in complex and realistic environmental settings. However, the main methods cannot satisfy this requirement while controlling the events and associated variables , whereas a set of use cases can. Therefore, a model should be defined to generate and structure these use cases while validating their relevance. Following the decomposition of the global research objective and case-definition recommendations, this study proposes a framework for designing complementary use cases to evaluate the impact of new technologies on emerging autonomy models in a structured, realistic, and global manner. Based on widely recognised related work, the 6-step framework helps define a coherent context specifying the business process model, agent, autonomy , technologies to be implemented, their fields of action, detailed variable collection protocol, and experimental setup. A cross-analysis of existing cases from the literature and empirical use of the framework validated the relevance of the model in designing experimental environments that are close to real-world settings. ARTICLE HISTORY
... As different needs and technologies arise, the industrial work system needs to be changed. With the increasing demand for customized products with a shorter development cycle of industry 3.0 mass production system is not the best choice economically (Schuh et al., 2014). The transition from a true physical system to a virtual system is now causing a paradigm shift in the entire production ecosystem with technological advancements happening at a fast pace. ...
The emergence of “Industry 4.0” signifies a new industrial era, where various technologies converge to provide digital solutions. Industry 4.0 (I4.0), encompasses a range of digital technologies that profoundly impact manufacturing businesses. However, there is limited knowledge regarding how businesses utilize these technologies. This paper addresses the pillars of Industry 4.0, a crucial part of the next industrial revolution for Indian manufacturing industries aiming to become competitive smart factories in the global market. A survey of 73 Indian industries assessed their current technologies. Implementation criteria for Industry 4.0 in Indian industries were established based on the survey results. The findings reveal that customer satisfaction and quality are top priorities for Indian industries. While most industries have implemented Industry 3.0 practices, awareness and adoption of I4.0 still pose challenges, particularly for SMEs. This report analyzes the technological status of Indian industries, identifies the gap, and provides a roadmap for adopting Industry 4.0. Bridging this knowledge gap and embracing Industry 4.0 can enhance competitiveness, drive innovation, and meet the evolving demands of the global market. The transition to smart factories powered by Industry 4.0 unlocks new opportunities for India’s industrial sector, propelling it towards a prosperous future.
... Within the category 'others', the competency 'cooperation' has surprisingly low mean values for the minimal as well as for the optimal competency level. This result contradicts the argumentation of several authors who highlight cooperation competencies and working collaboratively as important levers for smart manufacturing (e.g., Schuh et al., 2014) and key competencies for future requirements (e.g., Biesma et al., 2007;Frey and Osborne, 2013;Humburg and van der Velden, 2015;Letmathe and Schinner, 2017;Robles, 2012). One explanation for this is that several participants regard cooperation to be a basic requirement that does not merit additional salary (see Appendix 3). ...
Driven by technological transformation, changing competency requirements are receiving increased attention. Technological developments, such as digitization, automation, and cyber-physical systems, will change occupational requirements. Additionally, many companies are already confronted with a shortage of a skilled workforce due to demographic change. Companies can satisfy their demand for skilled labor through training or hiring. Both options are expensive and require careful planning. However, human capital is often neglected in this financial planning process. The aim of the present research is to devise and test a novel tool for companies to better plan the current and future organizational and employee competencies, and to coordinate their human and financial resources. Employing a budget-allocation approach, our exploratory study is based on a survey of 228 human resource and production managers. Respondents provide monetary valuations of competencies required by mechatronics technicians in the face of new technological challenges of smart manufacturing systems. Results show that managers are willing to allocate a relatively high budget to competencies such as complex problem-solving, analytical thinking, and troubleshooting. In addition, domain-based knowledge remains essential and valuable. We also find some differences in the monetary valuation between human resource and production managers.
... Further characteristics of CPS include the merging of physical and virtual realities. CPS are commonly referred to together with the IoT and embedded systems [20,21]. ...
... The Internet of Things (IoT) is commonly understood as a network of interconnected devices that comprise sensors, actuators and the network infrastructure, as well as tools for data collection and analysis [22]. Among Embedded Systems, the IoT is seen as one of the enablers for CPS [21]. Furthermore, the IoT offers the foundation for automation, decentralized decision making through machine to machine interaction, remote controlling and diagnostics [23]. ...
... Information sharing can be easily organized while electronics and sensors are cheaper and can connect with technology of IoT. Simulation is an example of sense-making, because alternative scenarios can be assessed [21]. Markets and customers demand flexibility and CPS can be seen a solution to increase adaptability [14]. ...
The purpose of this article is to show the main topics of Industry 4.0 and how expectations relate to experiences outlined in academic articles between 2012 and 2020. A quantitative keyword analysis is accompanied by a qualitative review of the top 10 keywords as well as expected benefits and experiences. Based on the top 10 keywords that accompany “Industry 4.0” a conceptual model is presented to show how these keywords relate to each other. Findings show that expected benefits of Industry 4.0 are efficiency gains, quicker ways to market, flexibility and significant cost savings in production processes. In contrast, the implementation in particular in small and medium enterprises is hampered by lacking expertise of new technologies and the required invest. While technology is available, companies lack strategy for its implementation. Companies that have successfully implemented Industry 4.0 benefit from efficiency, flexibility, quality and deliverability gains. It is also found that a focus on technology leaves aside other aspects such as implications on organizational culture and working conditions. This research is limited to journal and conference publications listed in the Scopus database. The use of specific search words and combinations of their synonyms and year further limits potential references. As such, some of the most cited articles about Industry 4.0 might be excluded. This article contributes to the discussion on Industry 4.0 through a condensed overview of the most prominent topics and by showing what promises of Industry 4.0 have materialized or not. As such, the value of this work is an orientation towards realistic expectations of Industry 4.0 in research and practice.
... Since the 1980s, and with the third industrial revolution, the integration of Artificial Intelligence (AI) and manufacturing made the rise of intelligent manufacturing [2]. introducing new technologies such as robots, Computer Numeric Control (CNC) machines, and industrial and electronic automation [3]. ...
The amazing growth and advancements reached in information and communication technologies in recent years allow easily the integration of intelligent components and systems into the traditional manufacturing industry. Enabling new challenges and applications in Industry 4.0 (I4.0) new systems. Cyber Physical Systems (CPSs) are a new generation of systems composed of a set of collaborative cyber and physical components with computation capabilities, generating and exchanging data in a loop between digital and physical worlds in a highly interconnected network. These enormous large amounts of data produced in or between CPSs are heterogeneous in terms of format and type due to different data sources, which leads to errors and malfunction in these systems due to the lack of interoperability between their components. As a result, sharing and exchanging data in CPSs is a challenging task to do. On other hand, modeling digital systems that reflect the current state of the physical entities and their behavior is a complex task to achieve. Especially, when communicating and processing data in real time to extract useful information. To overcome these challenges, semantic data models and knowledge representation when applied with Machine Learning (ML) techniques can enable solutions to interoperability problems in CPS, making it possible to mirror the physical reality and monitor it through cyberspace without misinterpretation and miscommunication in the system. This paper aims to provide a survey on the state of the art of available solutions to the semantic interoperability problem in CPS, integrating semantic models, ML, or both technologies combined in a reference architecture to achieve visionary Interoperable CPSs.
... The Third Industrial Revolution of the late 1990s not only digitalized and automated industrial production through extensive applications of electronics and computers but also laid the foundation blocks for Industry 4.0. Facilitated by the Industrial Internet of Things (IIoT), artificial intelligence (AI), autonomous robots, and smart digital technologies, today's Industry 4.0 creates a better interconnected, automated, and holistic manufacturing ecosystem, thereby offering higher efficiency [231], productivity [232], scalability and security [233], privacy [234], and the autonomous operation of production processes [235] by ensuring that different components including equipment, logistics systems, work-in-progress components, and others (including people) directly interact with one other to achieve collaboration. Therefore, cyber-physical systems are integral to achieving the vision of Industry 4.0, as CPS couples the physical productions and operations with the cyber systems through big data, augmented reality, cloud computing, and IoT to enable the seamless exchange of real-time information and commands. ...
With the emergence of the Internet-of-Things (IoT), artificial intelligence, and communication technologies, cyber-physical systems (CPS) have revolutionized the engineering paradigm with profound applications in many aspects of society including homes, energy, agriculture, health-care, transportation, business, and manufacturing. A CPS uses suitable computational techniques such as game theory to enable different entities to interact with one another for taking necessary actions to obtain selected objectives. Recent literature on CPS has extensively used game theory to approach a variety of technical challenges. In order to make these contributions more accessible to a broader audience, there is a need for studies that can provide readers with a comprehensive understanding of different types of CPS and their attributes, then clearly outline why game theory is relevant for modeling different aspects of CPS, and also discuss how game theory has been used in relevant literature to date. This paper bridges this gap by 1) providing a general discussion of different types of CPS and their characteristics; 2) giving an overview of different types of game-theoretic approaches; 3) explaining why game theory is appropriate for modeling different types of CPS; and 4) finally, studying how game theory has been used in different CPS types to address their challenges. Further, we also identify some key research challenges for future investigation where game theory could be applied as a potential solution.
... La cuarta Revolución Industrial (4RI), también conocida como Industria 4.0, hace referencia a un nuevo paradigma basado en la tecnología, en donde los sistemas de información y comunicación se utilizan, en gran medida, para mejorar la productividad. Convergen las tecnologías digitales, físicas y biológicas, por medio de la inteligencia artificial, el internet de las cosas, el Big Data, la computación en la nube, la biotecnología y los nuevos modelos de negocio (Schuh et al., 2014). La Industria 4.0 describe la creciente digitalización de toda la cadena de valor y la consiguiente interconexión de personas, objetos y sistemas mediante el intercambio de datos en tiempo real (Hecklau et al., 2016). ...
The main objective of this study is to identify the degree of concordance between competences demanded by Industry 4.0 and the competences that the Universidad Rey Juan Carlos (Madrid, Spain) includes in its training itineraries for the degree of business administration and management. The evolution of Industry 4.0 jobs and skills has increased the interest of both researchers and firm managers. A review of the scientific literature is performed along with a review of the university’s internal documents. The results reveal divergences between the skills demanded by the Industry 4.0 and the skills acquired by students during their university careers. It is concluded that study programs require profound changes to incorporate specific technical, methodological, and personal competences that are essential in the current Industry 4.0 environment.
... Summarising literature on SFNs, Radziwon et al. (2014Radziwon et al. ( , S. 1187) define smart factory as "a manufacturing solution that provides such flexible and adaptive production processes that will solve problems arising on a production facility with dynamic and rapidly changing boundary conditions in a world of increasing complexity". In this context, the term smart refers to the optimization of production processes through automation, which enhances productivity (Brettel et al., 2014;Schuh et al., 2014) and simultaneously enables the production of highly customized products down to lot size one at competitive costs and in ever-shorter timeto-market (Lasi et al., 2014). ...
... The cross-linking of components forms decentralized, functional divisions of information supply and processing (Brettel et al., 2014). In SFNs, divisions with similar functions are grouped to layers, building information hierarchies (Lee et al., 2015), which, in turn, are needed to coordinate decentralized components like production machines (Schuh et al., 2014). These layers range from machine control to enterprise level. ...
In manufacturing, concepts like the Internet of Things or Cyber-physical Systems accelerate the development from traditional production facilities towards smart factories. Thereby, emerging digital technologies increasingly connect information networks with production processes, forming complex smart factory networks (SFNs). Due to their reliance on information flows and the high degree of cross-linking, SFNs are, in particular, vulnerable to IT availability risks caused by attacks and errors. Against this backdrop, we present a modelling approach for analyzing the effects of IT threats on production processes. Based on Petri Nets, we provide modular SFN components for modelling SFN architectures and for simulating stochastic attack and error propagation. With this, we support the analysis and comparison of different SFN architectures regarding spreading effects, availability of information and production components, and associated effects on productivity. Our approach enables and serves as a foundation for decision support on SFN layouts from a risk perspective and the derivation of IT security mitigation measures in both research and practice. We evaluate our artefact by implementing and applying a software prototype in artificial and real-life settings.
... Our conceptual collaborative framework illustrated in Figure 3, adopted from [40], details three collaborative dimensions of coordination, cooperation, and communication. Communication provides the means to share information and interpret information to gain a shared understanding of complex situations and assess the consequences of possible joint measures [41,42]. ...
... Hosp. 2022, 2, FOR PEER REVIEW Framework for collaborative practice for tourism destination recovery (adapted from[40] Schuh et al., 2014). ...
While the COVID-19 pandemic evolves and new variants emerge, destinations and cities look to tourism recovery, cautiously rebooting and re-opening borders. Since the start of the pandemic, dramatic lockdowns have been employed, resulting in dire economic and social consequences to the tourism and hospitality industry and creating the need for a more feasible and sustainable response in the post-pandemic era. Pandemic vigilance and resilience at the societal level have become key in pandemic preparedness. However, due to the complexity of managing COVID-19, no clear cross-disciplinary collaborative framework for tourism recovery has been developed. Cross-sector collaboration to collectively integrate resources, capabilities, and experiences should be prioritised to spearhead tourism recovery plans. With insight on public health, pandemic preparedness, and community access, we hypothesised that cross-industry collaboration between the tourism industry and the pharmacist profession is relevant to the measures adopted for recovery from the COVID-19 pandemic. To examine this hypothesis, this study aimed to explore perceptions from key stakeholders in the tourism and the pharmacist sectors on cross-industry collaboration towards COVID-19 management and the “know-how” in developing, adopting, and advancing such a partnership. This exploratory study adopts and advances the ‘Four Cs’ conceptual framework of communication, cooperation, coordination, and collaboration. In terms of our hypothesis, interview responses with tourism executives and CPs confirm the framework’s suitability and the importance of an interdisciplinary collaborative approach between CPs and the tourism sector to craft a sustainable pathway to recovery from COVID-19 and future pandemic measures as borders re-open and international mobility increases. A tourism recovery strategy from this pandemic can occur more judiciously through a collaborative partnership with an extensive network of pharmacists within communities and popular tourism sites, as CPs have valuable healthcare resources and the ability to track and communicate healthcare alerts to tourism destination recovery efforts.
... A life-cycle perspective has fundamental implications for business models, namely, longlasting relations are needed, new types of contracts have to be developed and liabilities change (Schuh et al., 2014). Nevertheless, this perspective considers not only the timerelated aspects of a product but also the way it is produced and how it looks. ...
