A framework for variation visualization and understanding in complex manufacturing systems

Abstract

This paper provides a framework that allows industrial practitioners to visualize the most significant variation patterns within their process using three-dimensional animation software. In essence, this framework complements Phase I statistical monitoring methods by enabling users to: (1) acquire detailed understanding of common-cause variability (especially in complex manufacturing systems); (2) quickly and easily visualize the effects of common-cause variability in a process with respect to the final product; and (3) utilize the new insights regarding the process variability to identify opportunities for process improvement. The framework is illustrated through a case study using actual dimensional data from a US automotive assembly plant.