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

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journal contribution
posted on 2023-05-26, 07:48 authored by Anna TrubetskayaAnna Trubetskaya, Alan RyanAlan Ryan, Frank Murphy

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 



International Journal of Lean Six Sigma


Emerald Publishing Limited

Other Funding information

SOLAS (SOLAS Apprentice Registration Numbers 3753030920).

Department or School

  • School of Engineering

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