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Can Quality Improvement Efforts Be Successful Without Statistical Process Control?

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Despite the Statistical Process Control (SPC) be extensively explored in the literature, there are still difficulties in the implementation and maintenance, usually due to lack of attention to Critical Success Factors (CSF). This paper identified by literature review, critical factors that contribute to the success of the SPC and through an action research was implemented a control chart in a chemical plant using these factors. The attention to CSF during planning, implementation and the discussion with the process team resulted in the implementation and acceptance of control charts by the operational teams of the company, with continuing indications of use

The results enabled a framework to apply control charts based on CSF.

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Imagine this scenario: a patient undergoes hip surgery, only to be told that the replacement product that was installed inside of his or her body must be recalled due to a defective feature. Months, or perhaps years, of grueling rehab are compromised due of a lack of statistical data that could have prevented such instances from occurring

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.

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A continual improvement process, also often called a continuous improvement process (abbreviated as CIP or CI), is an ongoing effort to improve products, services, or processes. These efforts can seek "incremental" improvement over time or "breakthrough" improvement all at once. Delivery (customer valued) processes are constantly evaluated and improved in the light of their efficiency, effectiveness and flexibility. Some see CIPs as a meta-process for most management systems (such as business process management, quality management, project management, and program management). W. Edwards Deming, a pioneer of the field, saw it as part of the 'system' whereby feedback from the process and customer were evaluated against organisational goals (Pande, P., Neuman,R., 2002). The fact that it can be called a management process does not mean that it needs to be executed by 'management'; but rather merely that it makes decisions about the implementation of the delivery process and the design of the delivery process itself. A broader definition is that of the Institute of Quality Assurance who defined "continuous improvement as a gradual never-ending change which is: '... focussed on increasing the effectiveness and/or efficiency of an organisation to fulfil its policy and objectives. It is not limited to quality initiatives. Improvement in business strategy, business results, customer, employee and supplier relationships can be subject to continual improvement

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).

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Altogether, one issue in evaluating PCMH models is that reporting of changes in process and outcome measures is typically infrequent and often lags significantly after the start of the intervention

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.

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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

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