Benefits of a Product's Industry 4.0 Compliancy

The latest industrial revolution, the fourth one labeled Industry 4.0, has been ongoing for over a decade. Still, the topic seems to be surrounded by ambiguity with lacking some of the details defining what it really means, what is the purpose of the Indsutry 4.0? Inspired by a cloud integration project implemented by Cryotech Nordic for an Italian company in autumn 2021, the aim of this thesis is to answer some of the questions that arose during the project related to Industry 4.0 by looking at the issue from the perspective of product features, while discussing the benefits that different stakeholders seek and obtain from the implementation of Industry 4.0 technologies and systems. The thesis utilises two methods for its two research questions, one where national Industry 4.0 initiatives and known Industry 4.0 products are studied and another where a literature review is conducted to find answers from group of articles. The outcome of the thesis is a construction of a Minimum Viable Product model and a categorisation for the benefits and for stakeholders receiving the benefits from the Industry 4.0 implementations with a statistical distribution of the found benefits into these categories

Specification of the PERFoRM architecture for the seamless production system reconfiguration

The world is assisting to the fourth industrial revolution, with several domains of science and technology being strongly developed and, specially, being integrated with each other, allowing to build evolvable complex systems. Data digitization, big-data analysis, distributed control, Industrial Internet of Things, Cyber-Physical Systems and self-organization, amongst others, are playing an important role in this journey. This paper considers the best practices from previous successful European projects addressing distributed control systems to develop an innovative architecture that can be industrially deployed. For this purpose, a particular design process has to be addressed in order to consider the requirements and functionalities from various use cases. To investigate the known practices, four use cases are enlighted in this paper, which cover a wide spectrum of the European industrial force, as well as industrial standards to support a smooth migration from traditional systems to the emergent distributed systems.info:eu-repo/semantics/publishedVersio

Understanding how additive manufacturing influences organizations’ strategy in knowledge economy

Industry 4.0 constituted a trigger to a new phase in the Industrial Revolution, heavily focused in the interconnectivity of the systems, bringing disruptive technologies such as Additive Manufacturing (AM). On top of that, the shift from an industrial economy to a knowledge-based economy, where knowledge is the actual raw material, is implicating changes on the labor market, as new jobs strongly rely on knowledge-intensive activities. This is forcing organizations to rethink their way to operate, since markets are getting even more competitive and susceptible to greater volatility. Herewith organizations are resorting to AM as way to strengthen competitive position, as this technology allows to seize new opportunities. As response to that, this paper presents an industry analysis to AM based on Porter’s Five forces model, where forces such as threat of new entrants, bargaining power of customers, threat of substitutes, bargaining power of suppliers and rivalry among the existent competitors will be discussed under a knowledge perspective. The compiled evidence show that AM industry will plausibly suffer from a high rivalry in the next few years, as consequence of the increased power of customers and suppliers, low entry barriers for new entrants and due to pressures for a more sustainable society. Although these forces will not be totally controllable, organizations can plan their business strategy according to the knowledge they have on them. This type of approach will allow organizations to influence these forces more closely and at the same time to predict possible scenarios, identify tendencies and map the sector. In the present paper is proposed a conceptual model based on Porter’s five forces to analyze the impact of AM on firms’ strategy. For future development this model will be extended to organizations operating with AM in Portugal for validating its practical applicability, which will be performed through questionnaire and/or case study.info:eu-repo/semantics/publishedVersio

Knowledge Management in the Fourth Industrial Revolution: Mapping the Literature and Scoping Future Avenues

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the interconnectedness of machines and their ability to learn and share data autonomously. For this reason, the present study investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal 6 clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences

Implementing Industry 4.0: Exploring the literature in a systematic way using text mining

The increasing popularity of digitisation practices and methods by scholars and practitioners alike has been paving the way for industrial transformation. Industry 4.0 has become an accepted trend across various industries, yet despite the increasing number of articles on this topic the complexities of implementation at the firm level remains largely vague and undefined. Therefore, the research presents a review of the social, operational and strategic aspects following the full-text mining of 116 selected articles. The study reveals that digital transformation requires stakeholders and investors to consider implementation through a multi-level and multidisciplinary lens. On this basis the study identifies the social, operational and strategic gaps within the literature and provides recommendations for future studies on implementation

Surveying the sense of urgency of the tactical-level management to adopt industry 4.0 technologies: Ranking of three sister plants based on BWM-CRITIC-TOPSIS

Purpose:Although the decision to adopt Industry 4.0 technologies is commonly strategical, the selection and implementation of technology are the responsibilities of the tactical level management. The tactical level management will also directly experience the impact of adopting the technology towards the organizational performances in their functional areas. The comparative survey study aims to measure the tactical level management’s sense of urgency of the nine pillars in three plants of a single manufacturing organization. Design/methodology/approach: The research methodology starts with a literature review to collect the criteria appertaining to the pillars. Based on the 95 constituting criteria, the second step prepares and conducts a questionnaire survey with 32 participants on three sister plants. Next, rough BWM-CRITIC-TOPSIS ranks these plants at the pillar and criteria levels. The ranking method integrates Best-Worst Method (BWM), Criteria Importance Through Intercriteria Correlation (CRITIC), and technique for order performance by similarity to ideal solution (TOPSIS). The top management discussed and rendered insights into the results. Findings: Results show that the high-mix and labor-intensive plant (Plant 1) has the highest urgency, whereas the largely automated plant (Plant 3) has the lowest urgency to adopt the nine pillars. The findings provide empirical evidence of the effect of the recent Industry 4.0 awareness programs in Plant 1 and advanced infrastructure would lead to organization inertia (Plant 3) to aggressively pursue technological change. The most urgent pillar is cybersecurity, and the least urgent pillar is additive manufacturing (AM), outlining the concern over cyber threats when product information is increasingly integrated into the supply chain and technology immaturity of AM in production. Research limitations/implications: A limitation of this study is that the comparative survey only focused on three plants and the tactical level management of an organization. Originality/value: This study contributes to the knowledge of Industry 4.0 readiness by being the first to show different levels in the sense of urgency of the tactical level managements on the relevant technologies, which potentially affect the direction and the pace of Industry 4.0 adoptionPeer Reviewe

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition

Cloud Computing, Big Data y las Arquitecturas de Referencia de la Industria 4.0

The Industry 4.0 promotes the use of Information and Communication Technologies (ICT) in manufacturing processes to obtain customized products satisfying demanding needs of new consumers. The Industry 4.0 approach transforms the traditional pyramid model of automation to a network model of interconnected services, combining operational technology (OT) with Information Technology (IT). This new model allows the creation of ecosystems enabling more flexible production processes through connecting systems and sharing data. In this context, cloud computing and big data are critical technologies for leveraging the approach. Thus, this paper analyzes cloud computing and big data under the lenses of two leading reference architectures for implementing Industry 4.0: 1) the Industrial Internet Reference Architecture (IIRA), and 2) the Reference Architecture Model Industrie 4.0 (RAMI 4.0). A main contribution of this paper is to present a comparative analysis of IIRA and RAMI 4.0, discussing needs, benefits, and challenges of applying cloud computing and big data in the Industry 4.0.La Industria 4.0 promueve el uso de las Tecnologías de la Información y la Comunicación (TIC) en los procesos de fabricación para obtener productos personalizados que satisfagan las necesidades más exigentes de los nuevos consumidores. El enfoque de Industria 4.0 transforma el modelo tradicional piramidal de automatización en un modelo de red de servicios interconectados, combinando la tecnología operacional (OT, en inglés) con la tecnología de la información (TI). Este nuevo modelo permite la creación de ecosistemas para hacer el proceso de producción más flexible mediante la conexión de sistemas y el intercambio de datos. En este contexto, la computación en la nube y el big data (grandes volúmenes de datos) son tecnologías fundamentales para implementar la Industria 4.0. Por lo tanto, este documento analiza la computación en la nube y grandes volúmenes de datos bajo las lentes de dos arquitecturas de referencia líderes para la implementación de Industria 4.0: 1) la Arquitectura de Referencia de Internet Industrial (IIRA), y 2) el Modelo de Arquitectura de Referencia Industrie 4.0 (RAMI 4.0). La contribución principal de este artículo es presentar una guía comparativa de IIRA y RAMI 4.0 y discutir las necesidades, los beneficios y los desafíos de la aplicación de computación en la nube y grandes volúmenes de datos en Industria 4.0.Facultad de Informátic

Cloud Computing, Big Data y las Arquitecturas de Referencia de la Industria 4.0

The Industry 4.0 promotes the use of Information and Communication Technologies (ICT) in manufacturing processes to obtain customized products satisfying demanding needs of new consumers. The Industry 4.0 approach transforms the traditional pyramid model of automation to a network model of interconnected services, combining operational technology (OT) with Information Technology (IT). This new model allows the creation of ecosystems enabling more flexible production processes through connecting systems and sharing data. In this context, cloud computing and big data are critical technologies for leveraging the approach. Thus, this paper analyzes cloud computing and big data under the lenses of two leading reference architectures for implementing Industry 4.0: 1) the Industrial Internet Reference Architecture (IIRA), and 2) the Reference Architecture Model Industrie 4.0 (RAMI 4.0). A main contribution of this paper is to present a comparative analysis of IIRA and RAMI 4.0, discussing needs, benefits, and challenges of applying cloud computing and big data in the Industry 4.0.La Industria 4.0 promueve el uso de las Tecnologías de la Información y la Comunicación (TIC) en los procesos de fabricación para obtener productos personalizados que satisfagan las necesidades más exigentes de los nuevos consumidores. El enfoque de Industria 4.0 transforma el modelo tradicional piramidal de automatización en un modelo de red de servicios interconectados, combinando la tecnología operacional (OT, en inglés) con la tecnología de la información (TI). Este nuevo modelo permite la creación de ecosistemas para hacer el proceso de producción más flexible mediante la conexión de sistemas y el intercambio de datos. En este contexto, la computación en la nube y el big data (grandes volúmenes de datos) son tecnologías fundamentales para implementar la Industria 4.0. Por lo tanto, este documento analiza la computación en la nube y grandes volúmenes de datos bajo las lentes de dos arquitecturas de referencia líderes para la implementación de Industria 4.0: 1) la Arquitectura de Referencia de Internet Industrial (IIRA), y 2) el Modelo de Arquitectura de Referencia Industrie 4.0 (RAMI 4.0). La contribución principal de este artículo es presentar una guía comparativa de IIRA y RAMI 4.0 y discutir las necesidades, los beneficios y los desafíos de la aplicación de computación en la nube y grandes volúmenes de datos en Industria 4.0.Facultad de Informátic

Cloud Computing, Big Data and the Industry 4.0 Reference Architectures

La Industria 4.0 promueve el uso de las Tecnologías de la Información y la Comunicación (TIC) en los procesos de fabricación para obtener productos personalizados que satisfagan las necesidades más exigentes de los nuevos consumidores. El enfoque de Industria 4.0 transforma el modelo tradicional piramidal de automatización en un modelo de red de servicios interconectados, combinando la tecnología operacional (OT, en inglés) con la tecnología de la información (TI). Este nuevo modelo permite la creación de ecosistemas para hacer el proceso de producción más flexible mediante la conexión de sistemas y el intercambio de datos. En este contexto, la computación en la nube y el big data (grandes volúmenes de datos) son tecnologías fundamentales para implementar la Industria 4.0. Por lo tanto, este documento analiza la computación en la nube y grandes volúmenes de datos bajo las lentes de dos arquitecturas de referencia líderes para la implementación de Industria 4.0: 1) la Arquitectura de Referencia de Internet Industrial (IIRA), y 2) el Modelo de Arquitectura de Referencia Industrie 4.0 (RAMI 4.0). La contribución principal de este artículo es presentar una guía comparativa de IIRA y RAMI 4.0 y discutir las necesidades, los beneficios y los desafíos de la aplicación de computación en la nube y grandes volúmenes de datos en Industria 4.0.The Industry 4.0 promotes the use of Information and Communication Technologies (ICT) in manufacturing processes to obtain customized products satisfying demanding needs of new consumers. The Industry 4.0 approach transforms the traditional pyramid model of automation to a network model of interconnected services, combining operational technology (OT) with Information Technology (IT). This new model allows the creation of ecosystems enabling more flexible production processes through connecting systems and sharing data. In this context, cloud computing and big data are critical technologies for leveraging the approach. Thus, this paper analyzes cloud computing and big data under the lenses of two leading reference architectures for implementing Industry 4.0: 1) the Industrial Internet Reference Architecture (IIRA), and 2) the Reference Architecture Model Industrie 4.0 (RAMI 4.0). A main contribution of this paper is to present a comparative analysis of IIRA and RAMI 4.0, discussing needs, benefits, and challenges of applying cloud computing and big data in the Industry 4.0.Fil: Velasquez, Nancy. Universidad Nacional de La Plata. Facultad de Informática; ArgentinaFil: Estevez, Elsa Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Pesado, Patricia Mabel. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentin