Integration of Analytic Network Process in Adaptive Lean and Green Processing

The manufacturing and processing industry have been an important part of the global economy. Many industry players are constantly looking for an alternative to improve their operation and environmental performance to remain competitive in the market. The lean and green approach aims to reduce operat...

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Veröffentlicht in:Chemical engineering transactions 2019-10, Vol.76
Hauptverfasser: Wei Dong Leong, Bing Shen How, Sin Yong Teng, Sue Lin Ngan, Wendy Pei Qin Ng, Chun Hsion Lim, Hon Loong Lam, Chee Pin Tan, S.G. Ponnambalam
Format: Artikel
Sprache:eng
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Zusammenfassung:The manufacturing and processing industry have been an important part of the global economy. Many industry players are constantly looking for an alternative to improve their operation and environmental performance to remain competitive in the market. The lean and green approach aims to reduce operation and environmental waste within an organisation. In this study, a lean and green framework is proposed to evaluate the industrialist performance to achieve higher performance efficiency and reduce environmental impact. Three main clusters are incorporated in the framework such as environment, machine and resources. The analytic network process (ANP) method is used to establish the relationship between the three clusters with the input from industry expert from the respective field. A lean and green index is developed from the ANP model as a benchmarking for the industrialist. Backpropagation method is utilized as the continuous analysis tools to analyse the performance of the organization accordingly to the time step. The adaptive characteristic of backpropagation method is reflected from the ability for continuous improvement with time. In this study, the lean and green index will be further optimized with the adaptive approach. This paper proposes an adaptive model that can improve the industry’s performance and practise continuous improvement through establishing the adaptive approach.
ISSN:2283-9216
DOI:10.3303/CET1976094