Data-driven sustainability evaluation and manufacturing system enhancement from economic, environmental, social, and sustainability perspectives

Xiaoxiao Si; Cuixia Zhang; Cui Wang; Fan Liu; Conghu Liu

doi:10.1007/s11356-024-33463-y

Data-driven sustainability evaluation and manufacturing system enhancement from economic, environmental, social, and sustainability perspectives

Environ Sci Pollut Res Int. 2024 Apr 29. doi: 10.1007/s11356-024-33463-y. Online ahead of print.

Authors

Xiaoxiao Si¹, Cuixia Zhang², Cui Wang³, Fan Liu³, Conghu Liu⁴

Affiliations

¹ School of Electrical and Architectural Engineering, Huaibei Institute of Technology, Huaibei, 235000, China.
² School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, China. cuixiazhang@126.com.
³ Business School, Suzhou University, Suzhou, 234000, China.
⁴ School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, 234000, China.

PMID: 38684611
DOI: 10.1007/s11356-024-33463-y

Abstract

Sustainable manufacturing is crucial to achieving carbon neutrality targets. However, research on the sustainability of manufacturing systems is limited, and high consumption, low efficiency, and high emissions have resulted in high resource consumption and rapid environmental degradation. Therefore, it is of great importance to establish an evaluation and improvement indicator system conducive to sustainable development. To this end, this study developed a data-driven methodology for evaluating and enhancing the sustainability of manufacturing systems. Manufacturing system production process data, with data dimensions unified via the emergy method, were used to construct a sustainable development evaluation model that includes four perspectives: economy, environment, society, and sustainability. The model was applied to a flange production workshop in China to analyze the interrelation mechanisms among energy consumption, resource consumption, and environmental pollution, and identify optimization schemes to improve sustainability. After implementing these optimization schemes, the emergy yield rate (EYR) of the flange increased by 23.40%, the environmental load rate (ELR) decreased by 19.03%, the per capita emergy (EPP) increased by 6.88%, and the emergy-based sustainability index (ESI) increased by 52.76%. The method presented herein offers a novel and effective tool to analyze and visualize sustainable development in manufacturing systems and identify the relationship between technology and management in the manufacturing industry; however, this method is based on historical data and rules, and lacks of flexible response to unknown situations. The results provide a reference for enterprises to achieve sustainable and lean manufacturing.

Keywords: Data-driven; Emergy theory; Manufacturing system; Sustainability.