Hasil untuk "Industry"

D. Horváth, Roland Zs. Szabó

The Fourth Industrial Revolution poses significant challenges to manufacturing companies from the technological, organizational and management points of view. This paper aims to explore how top executives interpret the concept of Industry 4.0, the driving forces for introducing new technologies and the main barriers to Industry 4.0. The authors applied a qualitative case study design involving 26 semi-structured interviews with leading members of firms, including chief digital officers and chief executive officers. Company websites and annual reports were also examined to increase the reliability and validity of the results. The authors found that management desire to increase control and enable real-time performance measurement is a significant driving force behind Industry 4.0, alongside production factors. Organizational resistance at both employee and middle management levels can significantly hinder the introduction of Industry 4.0 technologies, though these technologies can also transform management functions. Multinational enterprises have higher driving forces and lower barriers to industry 4.0 than small and medium-sized companies, but these smaller companies have good opportunities, too.

Tiago Zonta, C. Costa, R. Righi et al.

Abstract Industry 4.0 is collaborating directly for the technological revolution. Both machines and managers are daily confronted with decision making involving a massive input of data and customization in the manufacturing process. The ability to predict the need for maintenance of assets at a specific future moment is one of the main challenges in this scope. The possibility of performing predictive maintenance contributes to enhancing machine downtime, costs, control, and quality of production. We observed that surveys and tutorials about Industry 4.0 focus mainly on addressing data analytics and machine learning methods to change production procedures, so not comprising predictive maintenance methods and their organization. In this context, this article presents a systematic literature review of initiatives of predictive maintenance in Industry 4.0, identifying and cataloging methods, standards, and applications. As the main contributions, this survey discusses the current challenges and limitations in predictive maintenance, in addition to proposing a novel taxonomy to classify this research area considering the needs of the Industry 4.0. We concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.

L. Badano, T. Kolias, D. Muraru et al.

Saurabh Vaidya, P. Ambad, S. Bhosle

Abstract Digitization and intelligentization of manufacturing process is the need for today’s industry. The manufacturing industries are currently changing from mass production to customized production. The rapid advancements in manufacturing technologies and applications in the industries help in increasing productivity. The term Industry 4.0 stands for the fourth industrial revolution which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer requirements. Industry 4.0 is still visionary but a realistic concept which includes Internet of Things, Industrial Internet, Smart Manufacturing and Cloud based Manufacturing. Industry 4.0 concerns the strict integration of human in the manufacturing process so as to have continuous improvement and focus on value adding activities and avoiding wastes. The objective of this paper is to provide an overview of Industry 4.0 and understanding of the nine pillars of Industry 4.0 with its applications and identifying the challenges and issues occurring with implementation the Industry 4.0 and to study the new trends and streams related to Industry 4.0.

J. Müller, Oana Buliga, K. Voigt

The article analyzes how Industry 4.0 triggers changes in the business models of manufacturing SMEs (small and medium-sized enterprises), by conducting a qualitative research with a sample of 68 German SMEs from three industries (automotive suppliers, mechanical and plant engineering, as well as electrical engineering and ICT). As SMEs play an essential role in industrial value creation, the article examines significant, yet at present understudied implications of Industry 4.0 along industrial value chains. First, the results show that Industry 4.0 encompasses three dimensions, namely high-grade digitization of processes, smart manufacturing, and inter-company connectivity. Second, the article shows how Industry 4.0 affects the three business model elements of manufacturing SMEs – value creation, value capture, and value offer – by giving specific examples for business model innovation in each of the three elements. Third, it shows that both the role as a user and/or provider of Industry 4.0 and whether a company is internally motivated and/or externally pressured towards implementation have an impact on which business model elements are innovated. Fourth, the study delineates four SME categories, designed to help managers to evaluate their own company's positioning towards Industry 4.0: craft manufacturers, preliminary stage planners, Industry 4.0 users, and full-scale adopters.

Andreas Schumacher, Selim Erol, W. Sihn

Abstract Manufacturing enterprises are currently facing substantial challenges with regard to disruptive concepts such as the Internet of Things, Cyber Physical Systems or Cloud-based Manufacturing – also referred to as Industry 4.0. Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop them. In this paper we propose an empirically grounded novel model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing. Our main goal was to extend the dominating technology focus of recently developed models by including organizational aspects. Overall we defined 9 dimensions and assigned 62 items to them for assessing Industry 4.0 maturity. The dimensions “Products”, “Customers”, “Operations” and “Technology” have been created to assess the basic enablers. Additionally, the dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for including organizational aspects into the assessment. Afterwards, the model has been transformed into a practical tool and tested in several companies whereby one case is presented in the paper. First validations of the model's structure and content show that the model is transparent and easy to use and proved its applicability in real production environments.

Thuy Duong Oesterreich, Frank Teuteberg

Michael Lieder, A. Rashid

A. Frank, G. H. Mendes, N. F. Ayala et al.

Abstract Servitization and Industry 4.0 are considered two of the most recent trends transforming industrial companies. Servitization is mainly focused on adding value to the customer (demand-pull) while Industry 4.0 is frequently related to adding value to manufacturing process (technology-push). Although some scholars address them as complementary concepts, the literature lacks evidences about what are the interfaces and connection between the two trends. Thus, we aim to develop a conceptual framework that connects Servitization and Industry 4.0 concepts from a business model innovation (BMI) perspective. Our framework is based on three Servitization levels (i.e. smoothing, adapting and substituting) and three levels of digitization (i.e. low, moderate and high levels). We show that matching these levels results in nine possible configurations classified in manual, digital and industry 4.0-related services, which can focus on smoothing, adapting or substituting services. We use reported cases from the literature to support and illustrate these configurations. We also discuss different levels of complexity for the implementation of these configurations. The study hence provides a foundation for the growing research on the interface between Servitization and Industry 4.0.

A. Raj, Gourav Dwivedi, Ankit Sharma et al.

This paper examines barriers to the implementation of Industry 4.0 technologies in the manufacturing sector in the context of both developed and developing economies. A comprehensive literature review, followed by discussions with industry experts, identifies 15 barriers, which are analyzed by means of a Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The ‘lack of a digital strategy alongside resource scarcity’ emerges as the most prominent barrier in both developed and developing economies. The influencing barriers identified suggest that improvements in standards and government regulation could facilitate the adoption of Industry 4.0 technologies in developing country case, whereas technological infrastructure is needed to promote the adoption of these technologies in developed country case. This study is one of the first to examine the implementation of Industry 4.0 in both developing and developed economies. This article highlights the difficulties in the diffusion of technological innovation resulting from a lack of coordinated national policies on Industry 4.0 in developing countries, which may prevent firms from fully experiencing the Industry 4.0 revolution. The results of this study may help decision makers and practitioners to address the barriers highlighted, paving the way for successful implementation of Industry 4.0 across the manufacturing sector.

V. Alcácer, V. Cruz-Machado

Abstract Industry 4.0 leads to the digitalization era. Everything is digital; business models, environments, production systems, machines, operators, products and services. It’s all interconnected inside the digital scene with the corresponding virtual representation. The physical flows will be mapped on digital platforms in a continuous manner. On a higher level of automation, many systems and software are enabling factory communications with the latest trends of information and communication technologies leading to the state-of-the-art factory, not only inside but also outside factory, achieving all elements of the value chain on a real-time engagement. Everything is smart. This disruptive impact on manufacturing companies will allow the smart manufacturing ecosystem paradigm. Industry 4.0 is the turning point to the end of the conventional centralized applications. The Industry 4.0 environment is scanned on this paper, describing the so-called enabling technologies and systems over the manufacturing environment.

Sofiat Abioye, Lukumon O. Oyedele, L. Àkànbí et al.

The growth of the construction industry is severely limited by the myriad complex challenges it faces such as cost and time overruns, health and safety, productivity and labour shortages. Also, construction industry is one the least digitized industries in the world, which has made it difficult for it to tackle the problems it currently faces. An advanced digital technology, Artificial Intelligence (AI), is currently revolutionising industries such as manufacturing, retail

M. Perkmann, Valentina Tartari, M. McKelvey et al.

A considerable body of work highlights the relevance of collaborative research, contract research, consulting and informal relationships for university–industry knowledge transfer. We present a systematic review of research on academic scientists’ involvement in these activities to which we refer as ‘academic engagement’. Apart from extracting findings that are generalisable across studies, we ask how academic engagement differs from commercialisation, defined as intellectual property creation and academic entrepreneurship. We identify the individual, organisational and institutional antecedents and consequences of academic engagement, and then compare these findings with the antecedents and consequences of commercialisation. Apart from being more widely practiced, academic engagement is distinct from commercialisation in that it is closely aligned with traditional academic research activities, and pursued by academics to access resources supporting their research agendas. We conclude by identifying future research needs, opportunities for methodological improvement and policy interventions.

A. Rojko

Industry 4.0 is a strategic initiative recently introduced by the German government. The goal of the initiative is transformation of industrial manufacturing through digitalization and exploitation of potentials of new technologies. An Industry 4.0 production system is thus flexible and enables individualized and customized products. The aim of this paper is to present and facilitate an understanding of Industry 4.0 concepts, its drivers, enablers, goals and limitations. Building blocks are described and smart factory concept is presented. A Reference Architecture Model RAMI4.0 and role of standardization in future implementation of Industry 4.0 concept are addressed. The current status of Industry 4.0 readiness of the German companies is presented and commented. Finally it is discussed if Industry 4.0 is really a disruptive concept or simply a natural incremental development of industrial production systems.

N. Plechkova, K. R. Seddon

G. Linden, Brent Smith, J. York

R. Ericson, A. Pakes

Giovanna Culot, G. Nassimbeni, G. Orzes et al.

Abstract The many scholars approaching Industry 4.0 today need to confront the lack of an agreed-upon definition, posing serious limitations to theory building and research comparability. Since its initial German conceptualization in 2011, both the technological landscape and the understanding of the Industry 4.0 have evolved significantly leading to several ambiguities. In parallel, similar concepts often used as synonyms − such as “smart manufacturing”, “digital transformation”, and “fourth industrial revolution” − have increased the sense of confusion around the scope and characteristics of the phenomenon. This study approaches the issue through an analysis of almost 100 definitions of Industry 4.0 and related concepts. The review of academic publications has been complemented by a selection of the most influential non-academic sources, including governmental bodies and consulting companies. Each definition has been broken down into its underlying technological and non-technological definitional elements. This categorization will serve as a basis for future research to approach the phenomenon in its multiple facets.

Massimo Regona, Tan Yigitcanlar, Bo Xia et al.

Artificial intelligence (AI) is a powerful technology with a range of capabilities, which are beginning to become apparent in all industries nowadays. The increased popularity of AI in the construction industry, however, is rather limited in comparison to other industry sectors. Moreover, despite AI being a hot topic in built environment research, there are limited review studies that investigate the reasons for the low-level AI adoption in the construction industry. This study aims to reduce this gap by identifying the adoption challenges of AI, along with the opportunities offered, for the construction industry. To achieve the aim, the study adopts a systematic literature review approach using the PRISMA protocol. In addition, the systematic review of the literature focuses on the planning, design, and construction stages of the construction project lifecycle. The results of the review reveal that (a) AI is particularly beneficial in the planning stage as the success of construction projects depends on accurate events, risks, and cost forecasting; (b) the major opportunity in adopting AI is to reduce the time spent on repetitive tasks by using big data analytics and improving the work processes; and (c) the biggest challenge to incorporate AI on a construction site is the fragmented nature of the industry, which has resulted in issues of data acquisition and retention. The findings of the study inform a range of parties that operate in the construction industry concerning the opportunities and challenges of AI adaptability and help increase the market acceptance of AI practices.

Anbesh Jamwal, R. Agrawal, Monica Sharma et al.

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.