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Continuous improvement in product and process capability is universally accepted as necessary for survival as an American Instrument Panel manufacturer. If the desired quality, productivity, and cost levels necessary for viability are to be achieved, variability reduction in the manufacturing process must be pursued. Thorough process understanding is the essential first step in the pursuit of variability reduction. This study demonstrates one attempt at taking the first step on the road to continuous improvement. Variability levels and relationships are quantified in this study through the use of correlational inference to provide a baseline of process understanding and capability description. This paper is organized such that similar approaches from a conceptual standpoint can be evaluated for the reader's application.

Undesirable levels of scrap and rework have been a chronic problem in the manufacturing environment. This study was conducted to determine the relationship of part dimension, and its process variability to manufacturing performance. Because manufacturing variability is usually evaluated in detection and attribute terms, a quantifiable and early prediction performance measure was desired. This study utilized a variety of statistical methods to establish a dimensional link to manufacturing performance so that a quantifiable indicator (dimension) could be evaluated and controlled for better manufacturing performance and yield. Additionally, the relationships understood from this study will provide significant design input for future product manufacturability of automotive instrument panels.