Quality improvement has always been one of the main objectives of a business irrespective of its size, origin, or speciality. Furthermore, statistical quality control (SQC) is a significant phenomenon when it comes to quality management. Jacobs and Chase (2014) argue that SQC is a set of techniques that are used to identify how well a company is meeting its initial specifications as to manufacturing or rendering service. In addition to that, Leiva et al. (2019) explain that SQC “is the quantitative tool for quality improvement” (p. 21). When it comes to a consumer products company, the given phenomenon can be applied to a few areas. According to Jacobs and Chase (2014), they are service processes, manufacturing, and logistics. That is why one can say that this phenomenon presents insight into how a company works and distributes its products or services.
When it comes to the use of SQC, one draws significant attention to variation. According to Jacobs and Chase (2014), this variation can be either assignable or common. Assignable one is caused by factors that can be easily managed, while common one is inherent. SQC is a useful way to identify kinds of this variation. For example, this phenomenon can provide businesses with meaningful information on how many manufacturing defects they have or how long customers should wait to be served. Consequently, the effectiveness of the business process will be higher when the two types of variation are decreased. Kulkarni et al. (2019) explain that statistical process control techniques are used to make sure that automobile parts meet the specifications, which increases business effectiveness. In conclusion, one can say that statistical quality control is an essential phenomenon that allows companies to improve their performance.
References
Jacobs, F. R., & Chase, R. (2014). Operations and supply chain management (14th ed.). McGraw-Hill Education.
Kulkarni, S., Kulkarni, C., Vimal, K. E. K., & Jayakrishna, K. (2019). Statistical quality control of torque wrenches used in automotive assembly department. In S. Hiremath, N. Shanmugam, N., & B. Bapu (Eds.), Advances in manufacturing technology: Lecture notes in mechanical engineering (pp. 199-208). Springer.
Leiva, V., Marchant, C., Ruggeri, F., & Saulo, H. (2019). Statistical quality control and reliability analysis using the Birnbaum-Saunders distribution with industrial application. In Y. Lio et al. (Eds.), Statistical quality technologies. ICSA book series in statistics (pp. 21-53). Springer.