Statistics and quality control in business Research Paper

Statistics and quality control in business - Research Paper Example The quality control process is used to improve the effectiveness of the producing system, therefore, reducing the number of defective products in the system. However, it is not possible to produce perfect products, so organizations resort to statistical methods to determine the number of defective products in the manufacturing process. One of the ways of ensuring the quality of a product is by introducing quality into the product that is presented to the customer (Reid and Sanders 172). The efficiency of the quality control process is determined by the perceived quality of the product to the final consumer. Therefore, organizations introduce statistical quality control process to improve the detection and reduction of defects in manufactured products. The statistical tools used by quality professionals are divided into four parts; statistical process control, designed experiments, descriptive statistics, and acceptance sampling. Statistical process control is used to determine whether the products from a production line meet the required standards of production (Grant and Leavenworth 521). The use of this statistical tool includes random selection of a product in a production line and measuring its characteristics to determine whether it meets current standards. This process is effective because it helps to determine the effectiveness of a process in a manufacturing line, therefore, if a process does not meet required standards, it can be reviewed and improved. This is the most effective statistical quality control process because it determines the effectiveness of a manufacturing process in the production center; therefore, the process can be improved. The second statistical tool used in quality control is designed experiments, which are also used in the production process to determine the effectiveness of the production process (Brue 59). This tool is useful in discovering the factors that influence process performance, after which process optimization is