Integrated and objective-oriented statistical process control

• Main Author: E. Von Collani
• Format: Article
• Language: English
• Published: Operations Research Society of South Africa (ORSSA) 2014-01-01
• Series: ORiON
• Online Access: http://orion.journals.ac.za/pub/article/view/410

Global competition and increased environmental concern have emphasized the significance of process quality for an economic success. Statistical Process Control (SPC) is considered as one of the major tools for better quality. However, there are many opinions about the nature, the role and the results of SPC in industry. If SPC is understood and implemented correctly, then it should not only lead to "a reduction of waste, shorter throughput times and greater reliability of supply, but also in a better understanding of the processes and their variation" as Does, Roes and Trip formulated it. SPC should ultimately lead to "an operational management of the continuous improvement of processes." The aim of an industrial production process is to make profit, thus quality should be measured by process profitability. From a more operational point of view highest quality, i.e. highest profitability, is reached by producing with high conformance to an appropriately selected target value, and quality improvement means further reduction of variation around the target value. Thus, understanding the inherent variation represents the key for controlling "profitability" and for being able to implement a system of continuous "quality improvement". The inherent variations reflect the complex relations between everything concerning the production process and results in uncertainty about the future development. Exactly at this point stochastics, the science of uncertainty, enters the stage. For controlling profitability the relations and the resulting variations have to be taken appropriately into account by an integrated and objective-oriented stochastic approach.