A Bibliometric Multicriteria Model on Smart Manufacturing from 2011 to 2018

Felice, Fabio De; Petrillo, Antonella; Zomparelli, Federico

doi:10.1016/j.ifacol.2018.08.221

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…Therefore, we further refer to the structured modeling process in F. De Felice's ( De Felice et al, 2018 ), use analysis hierarchy process (AHP) for corresponding analysis and discussion. Structured modeling is mainly divided into the following four steps (see Fig.…”

Section: Methodsmentioning

confidence: 99%

The impact of COVID-19 pandemic on sustainable development goals – A survey

Wang

Huang

2021

Environmental Research

185

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COVID-19 pandemic is the biggest challenge facing humanity after the 1918 Flu pandemic. The pandemic also poses a massive challenge to the achievement of Sustainable Development Goals (SDGs). Meeting this challenge requires a comprehensive investigation of the impact of the pandemic on sustainability. In this work, publications related to the impact of COVID-19 on sustainability in the Web of Science database were explored systematically by using bibliometrics techniques and meta-analysis approach. The results show the research scope is extensive, covering many subjects, whereas the research depth is not enough. Research in developed countries is dominant, although the pandemic poses more significant challenges to the sustainable development of developing countries than of developed countries. Developed countries are committed to studying education sustainability, while developing countries have shown greater attention to economic sustainability during the epidemic. The cluster analysis also shows that the COVID-19 pandemic has brought negative effects on 17 SDGs goals, whereas the pandemic may also bring opportunities to another 14 SDGs goals. At the end of the article, we put forward relevant suggestions for achieving sustainable development goals in the post-epidemic era.

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Section: Methodsmentioning

confidence: 99%

The impact of COVID-19 pandemic on sustainable development goals – A survey

Wang

Huang

2021

Environmental Research

185

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“…At the same time, despite the increasing interest in this novel topic, scholarly inquiry on the economic and managerial effects of the digital transformation of manufacturing as well as its impact on business models' innovation has transpired in the literature only recently, resulting in a limited understanding of this phenomenon [19,[27][28][29][30][31][32][33][34][35].…”

Section: Literature and Implementation Gapmentioning

confidence: 99%

Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review

et al. 2019

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The digital transformation of manufacturing (a phenomenon also known as “Industry 4.0” or “Smart Manufacturing”) is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field.

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“…The least research work has been carried out in countries such as India, France, Brazil, Sweden, and Finland. As per the WoS database, most research works were carried out in the Republic of China ( 47), followed by the USA (32), China (26), Germany (25) and Italy (17). The countries with the lowest contributions are England, Sweden, France, India, and Singapore.…”

Section: Documents By Geographic Locations In Scopus and Wosmentioning

confidence: 99%

“…The present industrial scenario in the context of Industry 4.0 drives the industries towards smart and intelligence. Additionally, with technological advancements in the Fourth Industrial Revolution and market competitiveness, industries face challenges to improve their position in an exponentially growing market [26]. As such, the industries thrive on making their product, process, or shop floor intelligent and smart, which involves self-decision-making and parameter optimization [27,28].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

et al. 2021

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The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

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A Bibliometric Multicriteria Model on Smart Manufacturing from 2011 to 2018

Cited by 11 publications

References 16 publications

The impact of COVID-19 pandemic on sustainable development goals – A survey

The impact of COVID-19 pandemic on sustainable development goals – A survey

Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

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