Quality control charts are limited to controlling one characteristic of a production process, and it needs a large amount of data to determine control limits to control the process. Another limitation of the traditional control chart is that it doesn’t deal with the vague data environment. The fuzzy control charts work with the uncertainty that exists in the data. Also, the fuzzy control charts investigate the random variations found between the samples. In modern industries, productivity is often of different designs and a small volume that depends on the market need for demand (short-run production) implemented in the same type of machines to the production units. In such cases, it is difficult to determine the control limits for the operations carried out on the same machines. This work aims to compare the traditional control charts and the fuzzy control charts for short-run production. In the traditional case, the data collected were processed using the (Minitab 21) software. It was found that the fuzzy control charts were more flexible and accurate in determining the control limits of the machine under study. The traditional deviation from nominal control charts showed false alarm of observation (15) as out-of-control, while the fuzzy (DNOM) showed that these observations were under control. Also, the standard deviation of the process was dropped from (σ =0.209041) to (σ =0.204401) after using the fuzzy control chart.