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To maintain standards, all manufactured automotive items are subject to strict quality control. Quality control processes test items against agreed upon specification limits. Items that “pass” these tests are "good enough" for commercial use, while items that “fail” are discarded. Quality control testing for large numbers of manufactured products can be costly; as a result, manufacturers might implement inexpensive test processes. Less expensive test processes may result in lower accuracy measurements. Utilizing lower accuracy measurements can increase both the number of discarded items and the number of returned (or recalled) items. We have created a multi-dimensional statistical ellipse model, formed from both lower and higher accuracy measurements, with which the impact of decisions based on lower accuracy test procedures can be assessed. A common method used to improve production yield is to shift the specification limits, increasing the process’s capability index.