A fuzzy set theory in the development and construction of control charts (Comparative study)

Abstract

        Statistical process control (SPC) is an approach that uses statistical techniques to monitor the process. Shewhart in 1920's developed statistical control charts that are one of the most important techniques of quality control to detect if assignable cause exists. The widely used control charts are X-bar and R charts. These are called traditional variable control chart with center line, upper control limit and lower control limit are represented by numeric values. A process is either "in control" or "out of control" depending on numeric observation values. For many problems control limits could not be so precise. Uncertainty comes from the measurement system including operators and gauges, and environmental conditions. In this context fuzzy set theory is useful tool to handle this uncertainty. Numeric control limits can be transformed to fuzzy control limits by using membership function. Fuzzy control limits provide a more accurate and flexible evaluation. In this paper through a real illustrative data from Ala Company for soft drinks in the city of sulaimani, shows the designing of fuzzy control chart for process average of variable quality.