Industry 4.0: A Survey on Technologies, Applications and Open Research Issues

Rapid advances in industrialisation and informatisation methods have spurred tremendous progress in developing the next generation of manufacturing technology. Today, we are on the cusp of the Fourth Industrial Revolution. In 2013, amongst one of 10 ‘Future Projects’ identified by the German government as part of its High-Tech Strategy 2020 Action Plan, the Industry 4.0 project is considered to be a major endeavour for Germany to establish itself as a leader of integrated industry. In 2014, China’s State Council unveiled their ten-year national plan, Made-in-China 2025, which was designed to transform China from the world’s workshop into a world manufacturing power. Made-in-China 2025 is an initiative to comprehensively upgrade China’s industry including the manufacturing sector. In Industry 4.0 and Made-in-China 2025, many applications require a combination of recently emerging new technologies, which is giving rise to the emergence of Industry 4.0. Such technologies originate from different disciplines ...

Industry 4.0: state of the art and future trends

Over the last few years, the fourth industrial revolution has attracted more and more attentions all around the world. In the current literature, there is still a lack of efforts to systematically review the state of the art of this new industrial revolution wave. The aim of this study is to address this gap by investigating the academic progresses in Industry 4.0. A systematic literature review was carried out to analyse the academic articles within the Industry 4.0 topic that were published online until the end of June 2016. In this paper, the obtained results from both the general data analysis of included papers (e.g. relevant journals, their subject areas and categories, conferences, keywords) and the specific data analysis corresponding to four research sub-questions are illustrated and discussed. These results not only summarise the current research activities (e.g. main research directions, applied standards, employed software and hardware), but also indicate existing deficiencies and potential re...

Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal

The expected contribution of Industry 4.0 technologies for industrial performance

The purpose of this paper is to conduct a state-of-the-art review of the ongoing research on the Industry 4.0 phenomenon, highlight its key design principles and technology trends, identify its architectural design and offer a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition.,The study performs a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0. The study further benefits from a comprehensive content analysis of the 178 documents identified, both manually and via IBM Watson’s natural language processing for advanced text analysis.,Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Industry 4.0 is no longer a hype and manufacturers need to get on board sooner rather than later.,The strategic roadmap presented in this study can serve academicians and practitioners as a stepping stone for development of a detailed strategic roadmap for successful transition from traditional manufacturing into the Industry 4.0. However, there is no one-size-fits-all strategy that suits all businesses or industries, meaning that the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets.,The first step for transitioning into the Industry 4.0 is the development of a comprehensive strategic roadmap that carefully identifies and plans every single step a manufacturing company needs to take, as well as the timeline, and the costs and benefits associated with each step. The strategic roadmap presented in this study can offer as a holistic view of common steps that manufacturers need to undertake in their transition toward the Industry 4.0.,The study is among the first to identify, cluster and describe design principles and technology trends that are building blocks of the Industry 4.0. The strategic roadmap for Industry 4.0 transition presented in this study is expected to assist contemporary manufacturers to understand what implementing the Industry 4.0 really requires of them and what challenges they might face during the transition process.

https://www.emerald.com/insight/content/doi/10.1108/JMTM-02-2018-0057/full/pdf

The future of manufacturing industry: a strategic roadmap toward Industry 4.0

The increasing integration of the Internet of Everything into the industrial value chain has built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie 4.0 is currently a top priority for many companies, research centers, and universities, a generally accepted understanding of the term does not exist. As a result, discussing the topic on an academic level is difficult, and so is implementing Industrie 4.0 scenarios. Based on a quantitative text analysis and a qualitative literature review, the paper identifies design principles of Industrie 4.0. Taking into account these principles, academics may be enabled to further investigate on the topic, while practitioners may find assistance in identifying appropriate scenarios. A case study illustrates how the identified design principles support practitioners in identifying Industrie 4.0 scenarios.

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Design Principles for Industrie 4.0 Scenarios

Implications of market and environmental changes have always influenced the industrial world. Combined with new technologies, the current changes are summarized under the term Industrie 4.0. Since the first announcement, Industrie 4.0 is one of the most discussed topics in research and industry. However, for companies in the industrial sector, it is a challenge to assess the implications of Industrie 4.0 for their organizations, and to decide whether and how to respond. Therefore, a methodology to transform an organization towards Industrie 4.0 is required. This paper provides a metamodel for the transformation of organizations towards Industrie 4.0 as well as the first technique of this method, a framework, to structure the implications of Industrie 4.0 and to identify Industrie 4.0 action fields as a first step towards Industrie 4.0 transformation. Furthermore, it provides an outlook how to implement the identified action fields systematically in existing process change management.

Towards a Methodology for Industrie 4.0 Transformation

Over the past several decades, digital technologies have evolved from supporting business processes and decision-making to becoming an integral part of business strategies. Although the IS discipline has extensive experience with digitalization and designing sociotechnical artifacts, the underlying design knowledge is seldom systematically accumulated across different settings and projects, and thus cannot be transferred and reused in new contexts. Motivated by this gap in the research, we turn to the data management field, where reference models have become important sources of descriptive and prescriptive domain knowledge. To study knowledge accumulation in reference models, we analyze the revelatory and extreme case of a longitudinal DSR process involving more than 30 European companies and 15 researchers from three universities over 12 years. The insights into reference model development allow us to theorize about knowledge accumulation mechanisms from both a process perspective and an artifact perspective: First, we observe that knowledge accumulation occurs in stages in response to technology’s evolving roles in business (problem space) and as a result of maturing design knowledge (solution space). Second, we find that reference models act as design boundary objects; they explicate and integrate knowledge from different disciplines and allow for the development of design knowledge over time—from descriptive (conceptual) models to prescriptive (capability or maturity) ones. To cope with fundamental changes in the problem space, these models require continuous updating as well as transfer/exaptation to new problem spaces. Our findings inform the IS community about the fundamental logic of knowledge accumulation in longitudinal DSR processes.

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1948&context=jais

Accumulating Design Knowledge with Reference Models: Insights from 12 Years' Research into Data Management

The digital and data-driven economy requires enterprises from all industries to revisit their existing data management approaches. To address the changing and broader scope of data management activities in the digital economy, this research in progress paper proposes a reference model, that describes the design areas of data management.

Towards a reference model for data management in the digital economy

Reference models are important research results with practical relevance that support companies in their digitalization initiatives. One of the challenges in developing reference models is the access to “implicit” practical knowledge that is created when technologies are implemented and used. This article illustrates how consortium research—as a multilateral, institutionalized collaboration between researchers and practitioners—facilitates knowledge transfer and the rigorous development of reference models. For the case of the Data Excellence Model (DXM), we illustrate the iterative (re)design of a reference model, which was developed by 15 companies and researchers from three universities. Based on these experiences, we derive recommendations for developing reference models in research collaborations between academics and practitioners.

https://serval.unil.ch/resource/serval:BIB_5132C6BB9C29.P001/REF.pdf

Tobias Pentek

Papers

Design Principles for Industrie 4.0 Scenarios

Towards a Methodology for Industrie 4.0 Transformation

Accumulating Design Knowledge with Reference Models: Insights from 12 Years' Research into Data Management

Towards a reference model for data management in the digital economy

Konsortialforschung zur Entwicklung von Referenzmodellen für die Digitalisierung von Unternehmen – Erfahrungen aus dem Datenmanagement