Failure modes and effects analysis (FMEA) is a methodology for prioritizing actions to mitigate the effects of failures in products and processes. Although originally used by product designers, FMEA is currently more widely used in industry in Six Sigma quality improvement efforts. Two prominent criticisms of the traditional application of FMEA are that the risk priority number (RPN) used to rank failure modes is an invalid measure according to measurement theory, and that the RPN does not weight the three decision criteria used in FMEA. Various methods have been proposed to mitigate these concerns, including many using fuzzy logic. We develop a new ranking method in this article using a data-elicitation technique. Furthermore, we develop an efficient means of eliciting data to reduce the effort associated with the new method. Subsequently, we conduct an experimental study to evaluate that proposed method against the traditional method using RPN and against an approach using fuzzy logic.

Failure modes and effects analysis (FMEA) is one of the most frequently used tools in process and product design: it is used in quality and reliability planning, and event and failure mode analysis. It has a long history of use and is a formally prescribed procedure by a number of prominent standards organizations. In addition, it's popular use has evolved as a less formal and widely interpreted tool in the area of Lean/Six Sigma (LSS) process improvement. This paper investigates one of the most important issues related to FMEA practice—the quality of individual vs. group performance in ranking failure modes. In particular, we compare FMEA rankings generated by: (i) individuals, (ii) group consensus, and (iii) non‐collaborative aggregation of group input (a synthesized group ranking). We find that groups outperform individuals and that synthetic groups perform as well as group consensus. We explain the implications of this result on the coordination of the design of products and processes amongst distributed organizations. The increasing distribution of product design efforts, both in terms of geography and different organizations, presents an opportunity to improve coordination using distributed synthetic group‐based FMEA.

Life‐cycle mismatch occurs when the life cycles of parts end before the life cycles of the products in which those parts are used. Lifetime buys are one tactic for mitigating the effect of part obsolescence, where a quantity of parts is purchased for the remaining life of a product. We extend prior work that determines optimal lifetime buy quantities for one product with one obsolete part by providing an analytic solution and two simple heuristic policies for the optimal lifetime buy quantities when many parts become obsolete over a product's life cycle. We determine which of our two heuristics is most accurate for different product life cycles, which yields a metaheuristic with increased accuracy. That analysis also reveals critical perspectives in making lifetime buy decisions with nonstationary life‐cycle demand patterns.

L ife-cycle mismatch occurs when the life cycle of a product does not coincide with the life cycles of the parts used in that product. This is particularly a problem with products that contain electronic components that sometimes have life spans of only two years. The cost of mitigating component obsolescence, which may require redesigning the product, is often considerable. Thus, prudent product design necessitates the selection of electronic components and product architecture, considering the cost of mitigating an obsolete design and other costs related to the design and manufacture of a product. Accordingly, we develop and analyze a model that shows how a product design can be effectively tailored to a particular product's life cycle.