This study proposes an optimization framework aimed at enhancing both operational and environmental performance in semi-automated manufacturing processes, addressing a critical gap in existing research.
The framework comprises five stages: Identify, Data Collection, Investigate, Optimization and Monitor. It integrates insights from the literature and applies these to a case study in a semi-automated semiconductor plant. Key tools include a Smart Andon Dashboard and a Manufacturing Execution System, as well as the Analytic Hierarchy Process (AHP) for improvement selection.
The implementation of the framework resulted in a 32% improvement in operational efficiency and a 70% reduction in machine idle time. These changes significantly reduced energy consumption and improved the plant's carbon footprint. Notably, the study highlights the long-term benefits of optimizing non-bottleneck processes for resource conservation and CO2 emission reduction.
This research provides a practical methodology for manufacturers to achieve dual objectives of operational efficiency and sustainability. The use of AHP enhances decision-making in the improvement selection process, making it adaptable to various manufacturing environments.
This study bridges the gap between operational and environmental performance improvements in semi-automated manufacturing. It demonstrates the potential of targeted optimizations to yield substantial benefits for both efficiency and sustainability.
