OPTIMIZING SUSTAINABLE MATERIAL SELECTION FOR AIR CONDITIONERS IN A SUPPLY CHAIN
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.
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2. Contributions in electronic form/online:
• https://www.javatpoint.com/machine-learning-random-forest-algorithm
