An Empirical Complexity-Based Approach to Assembly Line Balancing in Manual Assembly Systems

Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production management. The quantification of manufacturing complexity and its impact on key performance indicators remains a subject of debate. To examine the relationship between assembly complexity, assembly line balance, and productivity from an operator-oriented perspective, an empirical complexity indicator for mixed-model assembly workstations is proposed. This indicator is based on experimentally collected data and analysis of working time variability. The proposed indicator is evaluated through controlled experimental case studies conducted in a learning factory environment. The results indicate that the relationship between complexity and productivity is not linear. Instead, within the investigated experimental boundaries, the observed trend suggests a turning point beyond which further increases in complexity are associated with decreased productivity, while line balance continues to improve. This finding suggests that integrating the proposed complexity indicator into production planning and management may support decision-making related to assembly line balancing and complexity management in manual assembly systems.