Using industry 4.0 capabilities for identifying and eliminating lean wastes
dc.contributor.author | Rajab, Sulaiman | |
dc.contributor.author | Afy-Shararah, Mohamed | |
dc.contributor.author | Salonitis, Konstantinos | |
dc.date.accessioned | 2022-05-30T18:52:20Z | |
dc.date.available | 2022-05-30T18:52:20Z | |
dc.date.issued | 2022-05-26 | |
dc.description.abstract | This paper conducts a review of the literature to identify associations in operations between Industry 4.0 capabilities such as Additive Manufacturing, Augmented Reality, Autonomous Robots, Big Data, Cloud Computing, IIoT, Simulation, and Systems Integration with the commonly identified lean manufacturing wastes of Transport, Inventory, Movement, Waiting, Overproduction, Overprocessing, Defects, and Underutilized skills. The paper documents research that links various capabilities and wastes, including how IIoT can be used to reduce defects in manufacturing, and how it can mitigate overproduction across industries. There is also evidence that big data implementation in manufacturing has positive effects on reducing waiting times across the manufacturing process and delivery, and that cloud computing technologies guarantee better estimates for product and predicted inventory amounts. The research finds impacts on the social aspect of manufacturing by how augmented reality tools are increasingly used in the manufacturing sector to improve workers’ knowledge, skills, and abilities, and that simulation software applications are capable of decreasing operator motion wastes. The paper concludes that there is a clear benefit for SMEs in using Industry 4.0 in lean implementation journeys, and it supports the efforts of manufacturing organizations to become leaner using Industry 4.0 capabilities and solutions. | en_UK |
dc.identifier.citation | Rajab S, Afy-Shararah M, Salonitis K. (2022) Using industry 4.0 capabilities for identifying and eliminating lean wastes. Procedia CIRP, Volume 107, pp. 21-27. 55th CIRP Conference on Manufacturing Systems 2022, 29 June - 1 July 2022, Lugano, Switzerland | en_UK |
dc.identifier.issn | 2212-8271 | |
dc.identifier.uri | https://doi.org/10.1016/j.procir.2022.04.004 | |
dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/17975 | |
dc.language.iso | en | en_UK |
dc.publisher | Elsevier | en_UK |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Lean Manufacturing | en_UK |
dc.subject | Industry 4.0 | en_UK |
dc.subject | Wastes | en_UK |
dc.subject | Operations Management | en_UK |
dc.title | Using industry 4.0 capabilities for identifying and eliminating lean wastes | en_UK |
dc.type | Conference paper | en_UK |
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