1. An Overview of Coupled Exergetic and Life Cycle Manufacturing Performance Metrics for Assessing Sustainability.
- Author
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Selicati, Valeria and Cardinale, Nicola
- Subjects
SUSTAINABILITY ,KEY performance indicators (Management) ,PRODUCT life cycle assessment ,POWER resources ,MANUFACTURING processes ,PUBLIC opinion - Abstract
The journey to sustainability is becoming increasingly crucial in manufacturing, particularly in resource and energy intensive industries. In recent years, the matter of sustainability in industrial contexts has become an important point on the legislative agendas of many governments as well as in public opinion, with the latter becoming increasingly sensitive to enterprises' dedication to this problem. As a result, companies have started to implement sustainable approaches into their manufacturing processes and increased the amount of information about these practices to customers and stakeholders. The purpose of this work is to evaluate manufacturing sustainability using coupled exergetic and life cycle performance indicators, which are used to comprehend the outcomes of the studies as well as to qualitatively and quantitatively evaluate the models developed. The purpose of this study is to first present an overview of the metrics associated with the integrated or combined implementation of Exergetic Analysis and Life Cycle Assessment, their significance, and their application in specific use scenarios. Some illustrative indicators are then compared in an actual industrial case study, and the outcomes are discussed. Among the highlights, it is not yet able to determine a metric that measures the degree of manufacturing sustainability individually and thoroughly owing to the multifaceted character of the manufacturing processes. The overall conclusion is that combining Exergy with Life Cycle Thinking is indeed a beneficial solution for enhancing industrial processes, although the accuracy in measuring environmental, but primarily social and economic, concerns is not obvious. he assessment's interpretation is typically troublesome due to a lack of full and up-to-date data and confidence analysis, as well as a lack of scientific coherence. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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