Appropriate Smart Factory for SMEs: Concept, Application and Perspective

Jung, Woo‐Kyun; Kim, Dong-Ryul; Lee, Hyunsu; Lee, Tae-Hun; Yang, Insoon; Youn, Byeng D.; Zontar, Daniel; Brockmann, Matthias; Brecher, Christian; Ahn, Sung‐Hoon

doi:10.1007/s12541-020-00445-2

Cited by 43 publications

(18 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rauch and Vickery (2020) investigated that self-control is the key requirement and crucial for SM. Jung et al (2021) identified that productivity is the key performance that needs to be more focused on implementing SM. Dynamic scheduling is the significant critical performance measure of SM (Yang et al , 2021).…”

Section: Resultsmentioning

confidence: 99%

Evaluation of smart manufacturing performance using a grey theory-based approach: a case study

Malaga

Vinodh

2021

View full text Add to dashboard Cite

PurposeThe purpose of the article is to report a study on evaluation of smart manufacturing (SM) performance using a grey theory-based approach.Design/methodology/approachIn total, 30 criteria and 79 attributes for SM performance have been developed. A grey theory-based approach has been used for SM performance evaluation. The grey index has been calculated, and weaker areas have been derived. Performance level of SM has been evaluated using the Euclidean distance approach.FindingsThe SM performance index is found to be (3.036, 12.296). The ideal grey performance importance index (GPII) is obtained as (3.025, 4.875). The level of visibility and traceability, vertical integration, lead time and configuration data espionage and control ability are strong performing attributes. Integration abilities of services and manufacturing systems, ability of self-control, worker and raw material productivity, collaboration among buyers and suppliers and dynamic scheduling are identified as weaker areas, and suggestions for improvement have been derived. SM performance level has been identified as “Good.”Research limitations/implicationsAdditional performance measures could be included as a part of evaluation. Practitioners can overcome weaker areas in the early phase. Management achieves confidence and practitioners attain success in implementation of SM in industry through the developed SM performance indexing system.Originality/valueIdentification of SM performance measures and analysis of SM performance is the original contribution of the authors. The developed approach assists practitioners and managers to focus more on specific areas for performance improvement.

show abstract

Section: Resultsmentioning

confidence: 99%

Evaluation of smart manufacturing performance using a grey theory-based approach: a case study

Malaga

Vinodh

2021

View full text Add to dashboard Cite

show abstract

“…, 2020a; Frank et al. , 2019a), the adoption of Industry 4.0 technologies by manufacturing industries (Zheng et al , 2019) could follow three main directions strictly related to the main improvements linked to the concept of smart factory (Jung et al , 2021), that technolgies allow to ahcieve: (1) efficiency and productivity, (2) flexibility and customization and (3) data processing.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Scholars recently have showed that such technologies could be grouped into main clusters based on the use and the impacts of the different technologies on business and manufacturing processes (Zheng et al, 2021). Although different clusters emerged (Culot et al, 2020a;Frank et al, 2019a), the adoption of Industry 4.0 technologies by manufacturing industries (Zheng et al, 2019) could follow three main directions strictly related to the main improvements linked to the concept of smart factory (Jung et al, 2021), that technolgies allow to ahcieve: (1) efficiency and productivity, (2) flexibility and customization and (3) data processing.…”

Section: Introductionmentioning

confidence: 99%

Disentangling the link between ICT and Industry 4.0: impacts on knowledge-related performance

Bettiol

Capestro

Maria

et al. 2021

IJPPM

View full text Add to dashboard Cite

PurposeIndustry 4.0 technologies are promising to increase manufacturing companies' performance through the new knowledge that such digital technologies allow to create and manage within the firm boundaries and through customer interactions. Despite the great attention on the Industry 4.0 adoption paths, little is known about the relationships with previous waves of digital technologies, namely, information and communication technologies (ICTs), and how different groups of both types of technologies link to knowledge and its related performances.Design/methodology/approachThe study employed a quantitative research design using a survey method. Submitting the questionnaire to entrepreneurs, chief operation officers or managers in charge of the operational and technological processes of Italian manufacturing firms, 206 respondents stated that their firm has adopted at least one of the seven Industry 4.0 technologies investigated.FindingsThe findings of the study highlight the positive relationship between ICT and Industry 4.0 technologies in terms of both intensity and groups of technologies (Web-based, Management and Manufacturing ICT; Operation, Customization and Data-processing 4.0), and how technologies affect knowledge-related performances in terms of products and processes, job-learning, product-related services and customer involvement.Originality/valueThis study is one of the first attempts to link groups of ICT to groups of Industry 4.0 technologies and to explore the effects in terms of knowledge-related performances as a measure of technology use. The study shows strong path dependency among ICT, Industry 4.0 and knowledge performance, enriching the literature on technological innovation and knowledge management.

show abstract

“…In this section, we look at the maturity level of smart fashion factories. Most of the fashion industry belongs to small and medium-sized enterprises (SMEs), and some studies confirm maturity in some SMEs [23,24]. This suggests future directions for improvement after examining the low maturity level of smart fashion factories compared to other industries.…”

Section: Maturity Of Smart Fashion Factorymentioning

confidence: 99%

Visioning the Future of Smart Fashion Factories Based on Media Big Data Analysis

Lee

2021

Applied Sciences

View full text Add to dashboard Cite

Recently, many companies have adopted smart factories to increase productivity and efficiency. However, the fashion industry is one of the industries that have been relatively slow at embracing automation and switching to a smart factory. The purpose of the study is to suggest the future direction of the low-maturity smart factory in the fashion industry through newspaper analysis. In this study, semantic network analysis and convergence of iterated correlation (CONCOR) analysis were performed on 15,523 news articles. The analyses revealed that the smart fashion factory was developing to incorporate automated, unmanned, and intelligent operation. The problem of job loss owing to the smart factory was also heavily addressed in the news articles. In the newspaper articles, the view that the smart factory is efficient, fast, and innovative, and concerns regarding the possible damages that will result from hacking and machine malfunction were simultaneously expressed. Therefore, if news about security improvement emerges in the future, negative public opinion will be reduced, positively influencing the government’s support for smart factories and policy making.

show abstract

Appropriate Smart Factory for SMEs: Concept, Application and Perspective

Cited by 43 publications

References 50 publications

Evaluation of smart manufacturing performance using a grey theory-based approach: a case study

Evaluation of smart manufacturing performance using a grey theory-based approach: a case study

Disentangling the link between ICT and Industry 4.0: impacts on knowledge-related performance

Visioning the Future of Smart Fashion Factories Based on Media Big Data Analysis

Contact Info

Product

Resources

About