Using fuzzy approaches to evaluate quality improvement alternative based on quality costs

Quality cost is usually considered as a means to measure the quality level in a quality system. Since the interrelationship among quality cost components is complex, a general quantitative model for describing their relationship is not easy to construct for improving the quality. In the assessments of quality cost, some hidden quality costs, such as the goodwill loss due to lost customers' reliability, are often neglected in the existing analysis methods. This may lead to reaching a sub-optimal decision. In addition, the assessments of quantitative quality cost items are usually approximated, and therefore are imprecise in nature. Based on these considerations, we propose fuzzy approaches to evaluate quality improvement alternatives because of its fuzzy nature. An evidence fusion technique, namely Choquet fuzzy integral, is employed to aggregate the quality cost information. A composite index is determined to find the best quality improvement alternative. Finally, a numerical example is used to demonstrate the applicability of the approach.