Can Quality Improvement Efforts Be Successful Without Statistical Process Control?
The results enabled a framework to apply control charts based on CSF.
About one out of every eight patients, or 12-13 percent who received a certain brand of metal hip have had to face corrective surgery five years after their initial procedure. This is due to a buildup of metallic particles in the bloodstream, caused by the friction of metal rubbing against metal. While not everyone is affected, some patients experience fluid buildup in surrounding joints and muscles, which can lead to bone and nerve damage. When it comes to product recalls that damage our health and well being, it further heightens the call for companies to use statistical process control to monitor the quality of production output. Statistical process control refers to the collection and analysis of manufacturing data with the intention of improving product quality. By implementing statistical process control, the goal of eliminating or greatly reducing costly product recalls is realized. This is done by analyzing manufacturing data as it happens so that problems are stopped as they happen—instead of being caught after deployment. By stabilizing a production process and reducing the amount of variations in productivity, both the consumer and the company benefit. The consumer benefits by receiving a safe and tested product, and the company benefits by avoiding the costs and embarrassment associated with a recall. Additionally, statistical process control reduces the amount of money that your company is wasting on excess material during production, whether it is scrap, giveaway, rework or warranties.
Put simply, it means ‘getting better all the time’. DMAIC (an abbreviation for Define, Measure, Analyze, Improve and Control) refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. The DMAIC improvement cycle is the core tool used to drive Six Sigma projects. However, DMAIC is not exclusive to Six Sigma and can be used as the framework for other improvement applications (Oakland, J., 2003).
This is due to several factors: the burden of frequent data collection, the fact that outcome metrics often require an extended time to show the impact of the intervention, a lack of good short-term process metrics, and a lack of knowledge regarding tools to differentiate true change from random noise.
Montgomery, D.: Introduction to Statistical Quality Control. Hoboken, New Jersey: John Wiley & Sons, Inc..2005. pp. 148. ISBN 97-804-716- 5631-9.
Nenadal, J., Plura, J., 2008. Moderní management jakosti, management press, 2008, ISBN 978-80-7261-186-7, s.348-354
Oakland, J., 2003. Statistical process control. MPG Books Limited, Bodmin, Cornwall, 2003, ISBN 0 7506 5766 9
Pande, P., Neuman,R., Cavanagh, R., 2002. Zavádíme Metodu Six Sigma, TwinsCom s.r.o., ISBN 80-238-9289-4