Optimizing Waste Reduction in Manufacturing Processes Utilizing IoT Data with Machine Learning Approach for Sustainable Production

Abstract

Sustainable manufacturing with the Internet of Things (IoT) reduces environmental impacts, conserves natural resources, saves energy, and improves worker, community, and consumer safety while maintaining economic viability. IoT’s network of sensors and intelligent devices collects and analyzes data throughout the production lifecycle, enabling organizations to fulfil sustainability objectives and adopt more efficient, less wasteful operations. Waste management and reduction measures are the
focus of sustainable manufacturing research. Improvements are needed to simplify waste management and reduce production waste. Thus, in this study, we introduce an innovative machine learning technology called ”EcoEfficientNet”, developed to tackle this problem. Our study addresses the issue of waste in manufacturing processes. EcoEfficientNet employs sophisticated deep learning algorithms to analyze complex production data, allowing it to identify significant patterns and determine specific areas where waste can be significantly minimized. EcoEfficientNet’s approach to waste reduction in manufacturing processes revolves around three main strategies: data-driven analysis, optimization recommendations, and adaptable learning for continual enhancement. EcoEfficientNet’s success lies in its capacity for perpetual learning, enabling it to adapt to novel data and evolve alongside production settings. An extensive case study of a particular manufacturing process is carried out to assess the efficiency of EcoEfficientNet and provide helpful perspectives into the model’s effectiveness. By incorporating this method into the manufacturing process, organizations have seen a decrease in waste generation of up to 30%, demonstrating the applicability and efficacy of machine learning in improving sustainable manufacturing processes. The key to EcoEfficientNet’s success is its ability to engage in continuous learning, allowing it to adjust to new data and develop in tandem with operational environments.