The impact of Industry 4.0 on bottleneck analysis in production and manufacturing: Current trends and future perspectives
2022 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 174, article id 108801Article, review/survey (Refereed) Published
Abstract [en]
Bottleneck analysis, known as one of the essential lean manufacturing concepts, has been extensively researched in the literature. Recently, there has been a move towards using new Industry 4.0-based concepts and technologies in the development of bottleneck analysis. However, the interrelations between bottleneck analysis and Industry 4.0 have not been studied thoroughly. The present study addresses this gap and performs a systematic literature review on articles available in major scientific databases (i.e., Web of Science and Scopus) to investigate the impact of Industry 4.0 on the advancement of bottleneck analysis in production and manufacturing. Bibliometric analysis and content review were performed to extract the quantitative and qualitative data. Results revealed that only five out of 15 design principles and five out of eleven technologies of Industry 4.0 were addressed previously in developing bottleneck analysis methods. In addition to highlighting the existing gaps in the literature and proposing topics for future research, several potential development streams are proposed by studying the design principles and technologies of Industry 4.0, which have not been considered in bottleneck analysis before.
Place, publisher, year, edition, pages
Elsevier, 2022. Vol. 174, article id 108801
Keywords [en]
Bottleneck, Industry 4.0, Design principles, Technologies, Review, Production, Manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-22054DOI: 10.1016/j.cie.2022.108801ISI: 000899531700003Scopus ID: 2-s2.0-85141775244OAI: oai:DiVA.org:his-22054DiVA, id: diva2:1711065
Projects
ACCURATE 4.0
Funder
Knowledge Foundation, 20200181
Note
CC BY 4.0
Corresponding author at: Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, 54128 Skövde, Sweden. E-mail addresses: masood.fathi@his.se, fathi.masood@gmail.com (M. Fathi).
This study was funded by the Knowledge Foundation (KKS), Sweden, through the ACCURATE 4.0 project, under grant agreement No. 20200181.
2022-11-152022-11-152024-12-09Bibliographically approved