Industry 4.0 – How will the nonwoven production of tomorrow look like?

Cloppenburg, Frederik; Münkel, Andreas; Gloy, Yves-Simon; Gries, Thomas

doi:10.1088/1757-899x/254/13/132001

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…Therefore, the Industry 4.0 developments in the nonwoven industry should concentrate on reducing these shares by using the workforce e ciently, increasing rst-rate quality productivity, and decreasing waste production and downtimes. [19] 3 Research Methods Based on this study, this article uses mixed methods with qualitative methods and quantitative methods. A qualitative method with a research approach aims to understand phenomena in depth by emphasizing interpretation, context, and subjective experience.…”

Section: Manufacturingmentioning

confidence: 99%

Assessing the Impact of Big Data Analytics on Manufacturing Transformation Services in the Fourth Industrial Revolution

Younus,

Nurmandi,

Mutiarin

et al. 2024

Preprint

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The invention of new technologies has enabled the development of the manufacturing industry, from mechanical systems to highly automated assembly lines. The Industry 4.0 Concept requires self-sufficient factories, self-prediction, self-comparison, self-reconfiguration, and self-maintenance. Service innovation and industrial big data are receiving more attention from academia and industry. This article discusses managing manufacturing transformation services in the era of the Industrial Revolution 4.0 in the big data environment and the readiness of intelligent predictive informatics tools. This paper focuses on the basic concepts of Industry 4.0 and the current state of the manufacturing system. It also identifies research gaps between existing systems and Industry 4.0 requirements. The main contribution is the Industry 4.0 implementation structure, which consists of a multi-layered framework that can help people understand and achieve the requirements of Industry 4.0. The most cited members in this cluster are ten life cycles, eight construction industries, and eight learning systems. This study found that the number of publications on Big Data and Industry has increased overall in the last six years, with China being the most powerful country. It also found several other sub-topics related to Big Data and Industry, such as big data Analytics, Risk Management, and Industrial Revolution.

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Section: Manufacturingmentioning

confidence: 99%

Assessing the Impact of Big Data Analytics on Manufacturing Transformation Services in the Fourth Industrial Revolution

Younus,

Nurmandi,

Mutiarin

et al. 2024

Preprint

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show abstract

“…The first step for self-optimisation was carried out by the Institute of Technology Assessment (ITA) with the "EasyNonwoven" model [61], presented in Figure 15, which focuses on the machine part called Card as the machine is mainly to determine the quality of the product. In order to develop a self-optimised card, electronic quality control must be made directly to the non-integrated web.…”

Section: Asselin and Dilo Machinesmentioning

confidence: 99%

Industry 4.0 Case Study in Plastics Manufacturing: Bridging the Gap Between the Current State and Future Trends

Zioudas

Kourmpetis

Chamalidou

et al. 2022

Digital Manufacturing Technology

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The paper presents detailed research on bridging the gap between the theoretical and practical approach of Industry 4.0, considering a use case of the manufacturing company working in plastics production. In particular, we study an established Greek company in the textiles and plastics industry to present the current industry's technological status. Initially, we focus on the industrial process of a plastic manufacturing plant presenting the processes, methods, and equipment classification of such a factory. Subsequently, we focus on a novice method for developing or upgrading factories specializing in textiles and plastics and showcase its operational procedures. Lastly, after identifying the current state of affairs, we further expand on the key points that need change to develop the technological operations of an industry plan from Industry 3.0 to Industry 4.0. Specifically, to achieve this, we provide a detailed techno-economic proposal that suggests a trade-off between optimal quality and continuity of operations for an Industry 4.0 level factory. The technical novelty of this article lies in taking a step back and providing a top-down technological approach to the current state of affairs in machinery, supply chain, and factory operations level while suggesting cost-effective methods to increase productivity, reduce production errors and pave the way for smart manufacturing techniques.

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“…The application areas of big data technology in the textile segment may contemplate the entire value chain, in which, supported by IoT and cloud computing technologies, it transforms the data generated throughout the chain into information that helps the factories in the search for more competitive strategies, in addition to offering an incentive to the creation of new business models (Cloppenburg et al, 2017;Sharma and Kumar, 2019). In an example of application in the fashion segment, data collected on the Internet (social networks, advertisements, virtual catalogs) are processed and transformed into statistical reports with valuable information such as trends and client profiles and behaviors (Ghani et al, 2015;Schwab, 2017).…”

Section: Big Datamentioning

confidence: 99%

Maturity model toll to diagnose Industry 4.0 in the clothing industry

Forno

Bataglini

Steffens

et al. 2022

JFMM

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PurposeThe purpose of this paper is to present a diagnostic instrument of the maturity of Industry 4.0 technologies adapted to the textile and clothing sector and constructed based on the technological references that support this industrial evolution process.Design/methodology/approachThe proposed methodology began with the systematic literature review using the SciMAT software and then a questionnaire was developed with 49 questions divided into 5 categories – demography, technologies, strategy, digital skills and benefits of the deployment.FindingsThe application in 19 different sectors and 72 people in a textile industry showed this sector is still traditional. The diagnosis results pointed that the level of implementation of Industry 4.0 is considered the first level, later advancing in this order to levels of connectivity, visibility, predictability and adaptability of the processes.Research limitations/implicationsOnly in one company was applied the developed instrument, in this case study, a factory that makes children's and adult clothing items located in Santa Catarina, Brazil.Practical implicationsThrough the use of this assessment instrument, it is possible to perform an internal benchmarking at the company detecting the strong and weak points, as well as involve a multidisciplinary collaborator team.Social implicationsThe comparisons and improvements may be carried out per dimension or sector, which motivates the constant application and assessment of improvements. The instrument to diagnose the maturity of Industry 4.0 technologies in the textile and clothing sector is considered an effective tool for application in a single company or to have its use expanded in a new piece of research to the entire industrial textile segment.Originality/valueThe developed diagnostic tool may be used for both the internal assessment of the company and its placement relative to its competitors, involving several companies from the same sector. It is possible to know about what level of Industry 4.0 maturity the company is and after benchmarking by others and improve.

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Industry 4.0 – How will the nonwoven production of tomorrow look like?

Cited by 8 publications

References 1 publication

Assessing the Impact of Big Data Analytics on Manufacturing Transformation Services in the Fourth Industrial Revolution

Assessing the Impact of Big Data Analytics on Manufacturing Transformation Services in the Fourth Industrial Revolution

Industry 4.0 Case Study in Plastics Manufacturing: Bridging the Gap Between the Current State and Future Trends

Maturity model toll to diagnose Industry 4.0 in the clothing industry

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