Mapping challenges in developing sustainable small and medium industries: Integrating Lean and green principles

Uly Amrina, Akhmad Hidayatno, Teuku Yuri M. Zagloel

Abstract


Purpose: This study explores the complexity of developing a sustainable small and medium industry (SMI) through a conceptual model that integrates lean and green manufacturing principles.

Design/methodology/approach: This model includes a systems diagram that illustrates the structure of the interconnected variable and the interrelationships between goals, inputs, processes, and outputs required to achieve a system’s goals by using both lean and green principles. The diagram uses causal loop diagram logic from systems thinking based on the literature review, multi-actor analysis, observations, and focus group discussions.

Findings: Combining the efficiency benefits from lean principles and better environmental effectiveness from green principles would help to achieve sustainability. A conceptual structural map could support the integration processes by illustrating the variables, multi-actors, and multi-indicators required to achieve sustainability.

Research limitations/implications: The conceptual model highlights the necessary linkage between lean and green practices that would help SMI researchers and decision-makers to navigate their activities in achieving sustainability.

Originality/value: There is currently little discussion at the SMI level from a multi-actor perspective. The systems diagram offers a comprehensive picture that is the basis for further discussion of the complexity of integrating lean and green principles.

Keywords


Systems thinking, sustainability, lean, green, small and medium industry (SMI), cosmetics SMI

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DOI: http://dx.doi.org/10.3926/jiem.3401


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2021

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience