Statistical Process Control (SPC)

Statistical Process Control (SPC) is a quality control process used in the manufacturing chain to monitor and regulate product manufacturing using statistical methodologies (Oakland 2007). The scheme is divided into two phases: initial establishment and regular production utilization. The initial creation process stage entails inspecting the manufacturing process in question and determining the applicability of the SPC application to it (Qui 2013). The regular production usage phase, on the other hand, relates to the process’s establishment and use of its provisions in everyday production. The vital components used in SPC include control charts, a focus on continuous improvement and designing of experiments (Bersimis et al. 2007). These elements merged to constitute the entire process that sees to the production of quality goods.

The utilization of SPC in the production comes with some advantages. To start with, the exploitation of the process to its full potential guarantees conforming products from waste material since it aims and minimizing wastes (Wheeler & Chambers 2010). It also offers a perfect means of evaluating the wearing out rate of the parts of the machines and materials used and replacing them promptly. Therefore the process advocates for early detection and prevention of problems (Qui 2013). Moreover, through the use of proper working machines and early curbing of challenges, the time required for the manufacture of a product is significantly reduced. SPC also uses statistical methods to detect variations in the manufacturing of goods before they result in a poor quality product (Oakland 2007). For this reason, the quality of the products can be assured with little need for post-manufacture inspection.

However, SPC also has some limitations, and these include the process not being suitable in all manufacturing chains, difficulty in elucidating its appropriateness in the application and the skills required (Bersimis et al. 2007). Consequently, control charts are used in the evaluation and detection of the standard and unique sources of variation and are crucial in laying the strategies for minimizing and eliminating them (Qui 2013). That helps to analyze the production chain and enable the manufacturing of quality goods. Other tools employed are Ishikawa diagram, designed experiments and Pareto charts (Wheeler & Chambers 2010). Designed experiments are a means of quantifying the relative importance of the sources of variation a process that ensures strategies are put in place to eliminate errors of production.

References

Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability engineering international, 23(5), 517-543.

Oakland, J.S. (2007). Statistical process control. Routledge

Qui, P. (2013). Introduction to statistical process control. CRC Press.

Wheeler, D.J., & Chambers, D.S. (2010). Understanding statistical process control. Knoxville, Tenn: SPC Press.