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An implementation model for digitisation of visual management to develop a smart manufacturing process

Authors :
Trubetskaya, Anna
Ryan, Alan
Murphy, Frank
Publication Year :
2023
Publisher :
University of Limerick, 2023.

Abstract

Purpose This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs). Design/methodology/approach This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform. Findings The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream. Research limitations/implications The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance. Originality/value The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....4ad7342c3062e75062a90465746a1088
Full Text :
https://doi.org/10.34961/researchrepository-ul.23212133.v1