OPTIMIZING SUSTAINABLE MATERIAL SELECTION FOR AIR CONDITIONERS IN A SUPPLY CHAIN

IF 0.6 4区 材料科学 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY
P. Sivaraman, Santhosh S.
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Abstract

In order to cut their carbon footprint and promote environmental sustainability, the majority of businesses have now turned towards sustainable practises in their manufacturing processes and supply networks. The use of sustainable materials has drawn a lot of attention recently as a crucial step in accomplishing these goals. Choosing the material that is most suited for a product can be difficult, despite the fact that there are many sustainable materials available. This study uses machine learning – a random forest algorithm and multi-criteria decision analysis (MCDA) to optimise the use of sustainable materials in supply-chain operations. The study uses machine learning algorithms to analyse data on different sustainable materials, their characteristics and their effects on the environment. The study also investigates how an optimised material selection affects the whole supply chain, including the production, packing and shipping operations. The research offers a complete strategy for reducing the environmental effect of industrial processes by combining approaches from material engineering, supply chain management and machine learning. The novelty of this work resides in its integration of material engineering and machine learning strategies to enhance the supply chain choice of sustainable materials. As a notable example, the study highlights the potential of mycelium as a sustainable material for air conditioner components. Mycelium’s unique properties, such as its biodegradability, lightweight nature and adaptability position it as a promising candidate, enhancing the environmental profile of air conditioners. By incorporating mycelium-based components, manufacturers can significantly reduce carbon emissions, resource consumption and waste generation throughout a product’s lifecycle. This investigation underscores both the viability of mycelium and the broader significance of innovative material choices in reshaping industries towards a more sustainable future. Through such advances, this research not only contributes to the air conditioning sector but also establishes a paradigm for sustainable material adoption with far-reaching positive implications.
优化供应链中空调可持续材料的选择
为了减少碳足迹和促进环境的可持续发展,大多数企业现在都在生产流程和供应网络中采用可持续的做法。作为实现这些目标的关键一步,可持续材料的使用最近引起了广泛关注。尽管有许多可持续材料可供选择,但选择最适合产品的材料却很困难。本研究利用机器学习--随机森林算法和多标准决策分析(MCDA)来优化供应链运营中可持续材料的使用。该研究使用机器学习算法分析不同可持续材料的数据、特性及其对环境的影响。研究还探讨了优化材料选择如何影响整个供应链,包括生产、包装和运输操作。这项研究结合了材料工程、供应链管理和机器学习的方法,为减少工业流程对环境的影响提供了一套完整的策略。这项工作的新颖之处在于它整合了材料工程和机器学习策略,以加强供应链对可持续材料的选择。作为一个显著的例子,这项研究强调了菌丝体作为空调组件可持续材料的潜力。菌丝体具有生物可降解性、轻质和适应性等独特特性,是一种很有前途的候选材料,可提高空调的环保性能。通过采用以菌丝体为基础的组件,制造商可以在产品的整个生命周期内大幅减少碳排放、资源消耗和废物产生。这项调查强调了菌丝体的可行性,以及创新材料选择在重塑工业、实现更可持续未来方面的广泛意义。通过这些进展,这项研究不仅为空调行业做出了贡献,还为可持续材料的采用建立了一个范例,具有深远的积极意义。
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来源期刊
Materiali in tehnologije
Materiali in tehnologije 工程技术-材料科学:综合
CiteScore
1.30
自引率
0.00%
发文量
73
审稿时长
4-8 weeks
期刊介绍: The journal MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY is a scientific journal, devoted to original papers and review scientific papers concerned with the areas of fundamental and applied science and technology. Topics of particular interest include metallic materials, inorganic materials, polymers, vacuum technique and lately nanomaterials.
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