An intelligent decision-making model for accelerating green manufacturing adoption in automotive, electronics, and textile industries

Abstract Manufacturing industry is being increasingly forced to go green without compromising on the efficiency and effectiveness of their operations. This paper discusses an artificial intelligence based approach for encouraging the adoption of green practices in the manufacturing industries. Green manufacturing is an essential way of attaining environmental sustainability. However, the adoption of green manufacturing is still a challenge owing to the problem of reconciling the economic objectives with the environmental requirements. The integration of machine learning and green manufacturing results into a decision support system. The AI model designed utilizes predictive analytics, neural networks, and optimization algorithms for the identification of green manufacturing opportunities and for the formulation of strategies for their implementation. The data used comes from 85 manufacturing plants of the automotive, electronics, and textile industries, which proves the effectiveness of the model. The results show that AI-assisted green manufacturing reaches up to 27% less energy use, 22% material waste reduction, and 25% lower carbon footprint so far comparing with the traditional methods. The model is going to be a source of support and practical advice for the manufacturers, who are willing to be environmentally responsible and perform operatively, thus, the research will add to and share the existing academic knowledge of sustainable manufacturing along with the industrial part of the practice.