Integrating quality costs and real time data to define quality control

Abstract

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) The control of critical to quality (CTQ) parameters can be done in a given process or in a downstream process. Companies must decide which CTQ parameters will be controlled, in which process, and define the control method: statistical process control (SPC) or 100% inspection. However, operational constraints can influence its definition. Overall, the control for a given process can be excessive or insufficient, resulting in a non-optimal quality cost. This paper discusses the relevance of different factors that can influence the selection of a quality control method. Then, it assesses the likelihood of companies having reliable data on such factors and it is proposed a model to minimize the total quality costs of a given process. The model uses information like SPC efficiency in detecting potential process variations, false alarms, measurement system error, inspection cost, repair cost and the cost of passing defective units to the next process. The quality control method can be updated whenever recent data on the 18 parameters are available. Through an application example, quality control mechanisms are selected to minimize quality costs.