An Adaptive Neuro-Fuzzy System to Analyze the Cost of Quality

Document Type : Original Article


Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran


Many companies consider the cost of quality as a core value for promoting customers satisfaction to steer towards competitive advantages. A balance must be provided between the features of a product and its resultant quality on the one hand and the return on investment on the other hand. The desirable level of quality is assessable in terms of maintenance costs and thus pave the way for the balance to be achieved. In the industrial environment, regardless of information availability, the reasonable classification of such costs is vital to optimize the subsequent maintenance expenses which are the primary concern of our research. Here, an Adaptive Neuro-fuzzy Inference System (ANFIS) is presented to analyze the cost of quality in order to investigate the effectiveness of investment in the different types of costs. To implement the proposed methodology and demonstrate its applicability, a simulation in industrial enterprises is studied and the results are analyzed. The results show that the input component combinations that cause the minimum amount of error have proven effectiveness in the output.