PRODUCT AND PROCESS COST ESTIMATION WITH FUZZY MULTI-ATTRIBUTE UTILITY THEORY

Abstract

The need to provide cost-effective manufacturing technologies is critical in today's globally competitive environment. The ability to make sound decisions about the cost effectiveness of new technologies is the key to achieving the affordability goals of future products and systems. Effective assessments of manufacturing technologies for future investment and applications must reflect the total financial impact in trade-offs between cost and performance.

Most of the traditional tools for cost modeling, reasoning, and computing are crisp, deterministic, and precise in character. By crisp we mean dichotomous, that is, yes-or-no types rather than more-or-less types. In the real manufacturing environment, many parameters have an uncertain nature. The structures and parameters of the model are not definitely known in early design stages of, in particular, products that do not have a manufacturing base. The goal of this study is to develop a set of efficient procedures for providing the manufacturing industry with the ability to assess and evaluate the cost of new technologies and affordability at early product engineering design stages. This model, termed “fuzzy multi-attribute utility (FMAU),” can be applied to evaluate the cost index for each product. In addition, an optimization method is introduced for fine-tuning the cost model with historical data. The relationship between real cost and cost index can be determined by the empirical function. Also, this method can reduce the estimating error from subjective judgments by the tuning process. The proposed method is illustrated with an example of cost estimation for an ingot process in propulsion part manufacturing.