Statistical method using Principal Component Analysis to determine high variability parameters affecting the balancing of an assembly line

Modern assembly lines face numerous challenges when it comes to satisfying client expectations.  The challenges discussed include increasing customization demands, maintaining quality standards, managing lead times, addressing sustainability concerns, and effectively utilizing advanced technologies.  This challenges impact assembly lines efficiency and effectiveness in other word balancing of the line.  This research aims to identify the essential components that significantly influence the balance of assembly lines.  To achieve this objective, a novel approach is proposed using a 3D matrix interpretation and statistical method, Principal Component Analysis (PCA).  The research leverages the MATLAB tool to analyze the interactions between various parameters and identify highly changeable factors that impact assembly line balance.  By employing this methodology, the study aims to provide valuable insights into identifying the parameters of an assembly line balancing and enhancing overall operational efficiency.  The finding of this approach, reveal a significant influence of altering the piloting parameters on assembly line balancing.  This result underscores the importance of dynamically balancing the assembly line to achieve optimal performance.