A global conformance quality model: A new strategic tool for minimizing defects caused by variation, error, and complexity

The performance of Japanese products in the marketplace points to the dominant role of quality in product competition. Our focus is motivated by the tremendous pressure to improve conformance quality by reducing defects to previously unimaginable limits in the range of 1 to 10 parts per million. Toward this end, we have developed a new model of conformance quality that addresses each of the three principle defect sources: (1) Variation, (2) Human Error, and (3) Complexity.;Although the role of variation in conformance quality is well documented, errors occur so infrequently that their significance is not well known. We have shown that statistical methods are not useful in characterizing and controlling errors, the most common source of defects. Excessive complexity is also a root source of defects, since it increases errors and variation defects.;A missing link in the defining a global model has been the lack of a sound correlation between complexity and defects. We have used Design for Assembly (DFA) methods to quantify assembly complexity and have shown that assembly times can be described in terms of the Pareto distribution in a clear exception to the Central Limit Theorem. Furthermore, within individual companies we have found defects to be highly correlated with DFA measures of complexity in broad studies covering tens of millions of assembly operations.;Applying the global concepts, we predicted that Motorola's Six Sigma method would only reduce defects by roughly a factor of two rather than orders of magnitude, a prediction confirmed by Motorola's data. We have also shown that the potential defect rates of product concepts can be compared in the earliest stages of development.;The global Conformance Quality Model (CQM) has demonstrated that the best strategy for improvement depends upon the quality control strengths and weaknesses. Using this model, efficient improvement strategies can be defined that show: (1) what to improve, (2) how much to improve, and (3) and which quality tools to use. The study reinforces the value of continuous effort to reduce complexity across a broad spectrum of production activity, pointing to the importance of global perspectives in design and development decisions.