In today's highly competitive automotive markets manufacturers must provide high quality products to survive. Manufacturers can achieve higher levels of quality by changing or improving their manufacturing process and/or by product inspection where many strategies with different cost implications are often available. Cost of Quality (CoQ) reconciles the competing objectives of quality maximization and cost minimization and serves as a useful framework for comparing available manufacturing process and inspection alternatives.
In this paper, an analytic CoQ framework is discussed and some key findings are demonstrated using a set of basic inspection strategy scenarios. A case of a welded automotive assembly is chosen to explore the CoQ tradeoffs in inspection strategy selection and the value of welding process improvement. In the assembly process, many individual components are welded in series and each weld is inspected for quality. As in series connections any weld failure will lead to either a scrapped unit or an in-field failure, modeling and understanding CoQ tradeoffs in welding and inspection is particularly important.
CoQ tradeoffs in welding process improvement and inspection strategy selection are examined through a probabilistic CoQ model. From an expected value perspective, parametric sensitivity analyses reveal that complex tradeoffs between the welding process, internal failure costs, and external failure costs determine the optimal inspection strategy and the value of welding process improvement. It is demonstrated that welding process improvement may be accompanied by a change in optimal inspection strategy. A key result is that welding process improvement and inspection strategy selection must be performed simultaneously.
This paper also demonstrates that comparing CoQ distributions only by their expected values may be misleading since in many cases the cost distributions are asymmetric. High internal and high external failure costs, welding process non-conformance rates, and inspection method error rates are contributing factors. The alternative metric of expected utility, which captures the decision maker's risk aversion to high cost, low probability events, changes the criteria for optimality to favor process improvement and inspection strategies that minimize external failure events.
