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Abstract
This study investigates the factors influencing machine utilization in manufacturing operations by analyzing key operational parameters such as Labor Hours, Material Quality Score, and Production Yield. Using algorithmic analysis, the research identifies the relationships between these inputs and machine utilization, providing insights into optimizing equipment performance. The study aims to support data-driven decision-making to improve manufacturing efficiency and resource allocation.
Research Significance: Efficient machine utilization is critical for maximizing production output, reducing downtime, and lowering operational costs. Understanding how labor allocation, material quality, and production yield impact machine performance enables managers to implement targeted strategies to enhance operational efficiency. This research highlights the importance of leveraging data analytics for proactive monitoring and optimization of manufacturing processes.
Methodology: Algorithm Analysis The study employs algorithmic analysis, combining regression and predictive modeling techniques, to quantify the influence of Labor Hours, Material Quality Score, and Production Yield on Machine Utilization. Both linear and non-linear models are evaluated to capture complex interactions and provide accurate predictions for decision-making. This approach allows for identifying the most significant factors affecting machine performance.
Alternative Input-Output Structure Input Parameters: Labor Hours, Material Quality Score, Production Yield. Output Parameter (Evaluation): Machine Utilization (%), This structure facilitates quantitative analysis and helps in understanding the operational drivers of machine efficiency. Result: The analysis reveals that variations in Labor Hours and Material Quality Score significantly influence Machine Utilization, while Production Yield also contributes moderately depending on operational conditions. These results suggest that optimizing workforce scheduling and maintaining high material quality can improve equipment utilization, reduce idle time, and enhance overall production efficiency.
