A Bayesian networks (BNs) approach for modeling cost of quality (COQ)

Cost of quality (COQ) is an essential element of Total Quality Management (TQM) system. It is usually understood as the sum of conformance plus non-conformance costs, where cost of conformance is the price paid for prevention of poor quality, and cost of nonconformance is the cost of poor quality caused by product and service failure. The accurate assessment of such cost of quality can materially affect the reliability of quality-oriented, results-based improvements. The prevention–appraisal–failure (PAF) model is the most widely used model for determining the cost of quality. Cost of quality is generally affected by many parameters. Any attempt to assess the cost of quality should take the different cost of quality parameters into consideration. The main objective of this paper is to incorporate PAF approach into Bayesian networks (BNs) and establish a probabilistic model that will enable the manufacturing companies to assess cost of quality. This will improve the company’s performance and its ability to compete with other companies through the identification of important COQ parameters. BNs provide a framework for presenting inherent uncertainties, formal use of experts’ judgments and probabilistic inference among a set of variables. The capabilities of proposed approach are explained by a case study.

Keywords:

component , Total quality management , Cost of quality , PAF model , Bayesian networks