Publication detail
Enhancing the adaptability: Lean and green strategy towards the Industry Revolution 4.0
LEONG, W.D. TENG, S.Y. HOW, B.S. NGAN, S.L. RAHMAN, A.A. TAN, C.P. PONNAMBALAM, S.G. LAM, H.L.
English title
Enhancing the adaptability: Lean and green strategy towards the Industry Revolution 4.0
Type
journal article in Web of Science
Language
en
Original abstract
Industry 4.0 has brought forth many advantages and challenges for the industry players. Many organizations are strategizing to take advantage of this industrial paradigm shift, thus improving the sustainability of the enterprise. However, there are many factors such as talent development, machinery advancement and infrastructure development which involve huge investment that need to be considered. This paper presents an enhanced adaptive model for the implementation of the lean and green (L&G) strategy in processing sectors to solve dynamic industry problems associated with Industry 4.0. A feature of this enhanced adaptive model is that it combines experts’ experience and operational data as input in dealing with real industry application. A lean and green index is coupled in the model to serve as a benchmark and process improvement tracking indicator. This allows the industrialists to set a lean and green index (LGI) target for effective process improvement. From this integrated model, an ensemble of backpropagation optimizers is then used to identify the best-optimized strategy. This ensemble optimizer is formulated to perform operation improvement and update the targeted LGI automatically when a higher index is achieved for continuous improvement. A case study on a combined heat and power plant is performed and reflects an improvement of 18.25% on the LGI. This work serves as a practical transition strategy for the industrialist desiring to improve the sustainability of the facility with Industry 4.0 elements at minimum investment cost. (C) 2020 Elsevier Ltd. All rights reserved.
English abstract
Industry 4.0 has brought forth many advantages and challenges for the industry players. Many organizations are strategizing to take advantage of this industrial paradigm shift, thus improving the sustainability of the enterprise. However, there are many factors such as talent development, machinery advancement and infrastructure development which involve huge investment that need to be considered. This paper presents an enhanced adaptive model for the implementation of the lean and green (L&G) strategy in processing sectors to solve dynamic industry problems associated with Industry 4.0. A feature of this enhanced adaptive model is that it combines experts’ experience and operational data as input in dealing with real industry application. A lean and green index is coupled in the model to serve as a benchmark and process improvement tracking indicator. This allows the industrialists to set a lean and green index (LGI) target for effective process improvement. From this integrated model, an ensemble of backpropagation optimizers is then used to identify the best-optimized strategy. This ensemble optimizer is formulated to perform operation improvement and update the targeted LGI automatically when a higher index is achieved for continuous improvement. A case study on a combined heat and power plant is performed and reflects an improvement of 18.25% on the LGI. This work serves as a practical transition strategy for the industrialist desiring to improve the sustainability of the facility with Industry 4.0 elements at minimum investment cost. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords in English
Lean and green manufacturing, Lean and green index, Machine learning, Process optimization, Adaptive model, Industry revolution 4.0
Released
10.11.2020
Publisher
Elsevier
Location
Oxford, England
ISSN
0959-6526
Volume
273
Number
1
Pages from–to
1–20
Pages count
20
BIBTEX
@article{BUT170183,
author="Sin Yong {Teng},
title="Enhancing the adaptability: Lean and green strategy towards the Industry Revolution 4.0",
year="2020",
volume="273",
number="1",
month="November",
pages="1--20",
publisher="Elsevier",
address="Oxford, England",
issn="0959-6526"
}