Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

Event-driven IT-architectures as enabler for Industry 4.0

Originating in 2011, Industry 4.0 describes the digital revolution of industry and has since become a collective term for smart, mutable and data driven factories. During the last decade systemic and methodical solutions were designed and implemented that enable corresponding data driven use cases for producers. Today's system providers offer complex data ecosystems in which data-driven use cases are built-in and implementers offer focused digitalisation projects to rapidly address quick wins. While an assessment of expectations around Industry 4.0 results in requirements within the domains of modifiability, connectivity, data and organisation for an IT-architecture, many such solutions are found to be violating essential requirements as systemic flexibility and data-availability. Not only is this a relevant matter for architectural purists, but it highlights real problems that industry is still facing while applying digitalisation measures in pursuit of Industry 4.0. While event-driven architectures go back to the design of modern operating systems, the emergence of powerful, resilient and cheap broker-technologies has risen the polarity of event-driven IT-architectures for businesses in the last decade. Although its occurrence is predominantly represented in ecommerce, finance and insurance, many prominent manufactures have since begun their transformation into an event-driven IT-architecture. Reasons for this architectural adaptation include exceptional data availability, resilience, scalability and especially data sovereignty. An assessment of event-driven IT-architecture's properties and implications reveals an excellent fit for the architectural requirements of Industry 4.0. In this work the subject of Industry 4.0 is analysed along literature to derive a collective understanding of expectations from a factory implementing Industry 4.0. Subsequently, IT-architectural requirements are derived that describe an architecture capable of satisfying these expectations. Then event-driven IT-architectures are analysed regarding their structural composition and capabilities. Finally, the fit of event-driven IT-architecture is evaluated against the architectural requirements of Industry 4.0, discussing congruence and divergence

Learning Factory: The Path to Industry 4.0

Abstract Nowadays, there are plenty of studies that seek to determine which are the skills that should be met by an engineer. Communication and teamwork are some of the most recurrent ones associated with a knowledge of the engineering sciences. However, their application is not straight forward, due to the lack of educational approaches that contributes to develop experience-based knowledge. Learning Factories (LF) have shown to be effective for developing theoretical and practical knowledge in a real production environment. This article describes the transformation process of a training-addressed manufacturing workshop, in order to structure a Learning Factory for the production engineering program at EAFIT University. The proposed transformations were based on the definition of three pillars (didactic, integrative and engineering) for the development of an LF. We argue that a proper transformation process may contribute to ease the path towards new manufacturing trends such as industry 4.0 into an academic context that strengths the engineering training process

A Competency Model for Industrie 4.0 Employees

This paper analyzes employee competencies for employees with higher education in Industry 4.0. An Industry 4.0 competency model based on a behavioral oriented approach concerning three variants, namely Information Systems, Information Technology and Engineering is developed by extending the SHL Universal Competency Framework through a structured literature review and focus groups with academic staff. The presented study contributes to research by providing a starting-point for further research regarding employee competencies for Industry 4.0. It contributes to practice as the provided competency model can be applied to Industry 4.0 job descriptions

Renewing a University to Support Smart Manufacturing Within a Region

This chapter focuses on the topic of renewing a university in order to be able to support the adaptation of smart manufacturing and Industry 4.0 within a region. The chapter introduces Industry 4.0 as a framework for regional development. Factors related to Industry 4.0 related renewal in the region are identified and discussed further. An idea of how to apply Industry 4.0 as a framework for renewal of a multidisciplinary university’s structure and curricula is introduced. Also, a case study for applying Industry 4.0 as a framework for increasing competitiveness in the region is introduced

Cyber risk at the edge: Current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks

A STUDY ON SUSTAINABLE BUILDING DEVELOPMENT IN THE CONTEXT OF TRANSITION FROM CONSTRUCTION 4.0 TOWARDS 5.0

The aim of this study is to to analyse the theoretical framework of Industry 4.0 and Industry 5.0 and their implications for the construction industry, as well as explaining their possible effects, by using an empirical research based on the existing scientific literature. Industry 4.0 is an under-researched area in the construction industry, even though this field has a high benefit for the parties involved. Industry 5.0, on the other hand, stands for direct cooperation between robots or intelligent machines and humans. According to the ideas of the European Commission, Industry 5.0 consists of a triad: human orientation, sustainability and resilience. Since this paper represents an outcome of an early PHD research, the methodology chosen was examining other similar research in the literature of Industry 4.0 and Industry 5.0 in the construction industry, new established concepts (such as Building Information Modeling, Product Lifecycle Management, sustainability)

Framework For The Successful Set-up Of A Common Data Model In The Context Of An Industry 4.0-ready Plant Design Process

The production plant design process consists of a multitude of individual engineering disciplines, which rely on a variety of digital models. The individual tasks build up on each other, while each discipline consumes information from the previous processes. However, sharing relevant data across multiple companies is challenging and susceptible to miscommunication and delays. Furthermore, integrating diverse software systems, tools, and technologies create compatibility issues and hinder seamless integration. As a result, a heterogeneous, non-automated data and information landscape is created, characterized by a high level of manual data transfer. This represents a major problem on the way towards Industry 4.0. The goal of this paper is to provide a framework for the successful set-up of a common data model in the context of an Industry 4.0-ready plant design process across and along the value chain. For this purpose, a literature review of current problems in the cross-company and cross-departmental collaboration in the plant design process is provided and requirements for the framework are derived. Existing solutions and research projects are compiled and evaluated against the requirements, from which the framework's structure is concluded. The framework itself is intended to be holistic and must therefore not only include technical aspects (e.g. data interfaces, semantics), but also enable the entire organization and value chain to implement the common data model as part of the digital transformation process (e.g. employee skills, business strategy, legal conditions). Based on this, the framework is further elaborated by deducing calls for action for a successful set-up of a common data model within the research project DIAMOND (Digital plant modeling with neutral data formats). The focus should be on employees and their competencies, as these are prerequisites for shaping digital transformation. Future research must prioritize these actions to enhance technology readiness and organizational Industry 4.0 preparation

Industry 4.0 creating a buzz in western hemisphere: But watch out for China pulling into the fast lane

Industry 4.0 is a subject with global implications. Essentially, the concept comes down to the reorganisation and automation of value chains. If successfully implemented, industry 4.0 sets out to revolutionise the way goods and services are created and distributed, reshaping the industrial landscape on a national and global scale. Thus, China is determined to seize the opportunities of the digital evolution. Chinas government actively promotes the transformation towards an innovation-led growth model through large policy programs. The scope of funding for seven Strategic Emerging Industries is placed in the Five-Year-Plan. Thus, the government unveiled its “Made in China 2025” program. Here, China is considered the most mature adopter of industry 4.0 worldwide. Aside from world leaders such as Huawei and ZTE, myriads of Chinese small and medium enterprises create an updated version of German Mittelstand for Far East. Notwithstanding, the rank and file of China’s companies did still not embrace the benefits of previous industrial stages. The country is and will remain highly heterogeneous. Therefore, industry 4.0 is realised locally and in an evolutionary fashion. Overall, China’s implementation of industry 4.0 is still in its infancy. Nevertheless, by managing to extrapolate the momentum, China is pulling into the fast lane