Enhancing the adaptability: Lean and green strategy towards the Industry Revolution 4.0

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F20%3APU139935" target="_blank" >RIV/00216305:26210/20:PU139935 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0959652620329152" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0959652620329152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jclepro.2020.122870" target="_blank" >10.1016/j.jclepro.2020.122870</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing the adaptability: Lean and green strategy towards the Industry Revolution 4.0

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Enhancing the adaptability: Lean and green strategy towards the Industry Revolution 4.0

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20701 - Environmental and geological engineering, geotechnics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Svazek periodika

273

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

000572927800007

EID výsledku v databázi Scopus

2-s2.0-85088397153