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What Is the Role of SPC in Quality Improvement?

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Statistical process control (SPC) is the application of statistical methods to the monitoring and control of a process to ensure that it operates at its full potential to produce conforming product. Under SPC, a process behaves predictably to produce as much conforming product as possible with the least possible waste. While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process with a measurable output. Key tools in SPC are control charts, a focus on continuous improvement and designed experiments.

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Statistical Process Control (SPC) is not new to industry. In 1924, a man at Bell Laboratories developed the control chart and the concept that a process could be in statistical control. His name was William A. Shewart. He eventually published a book titled “Statistical Method from the Viewpoint of Quality Control” (1939). The SPC process gained wide usage during World War II by the military in the munitions and weapons facilities. The demand for product had forced them to look for a better and more efficient way to monitor product quality without compromising safety. SPC filled that need. The use of SPC techniques in America faded following the war. It was then picked up by the Japanese manufacturing companies where it is still used today. In the 1970s, SPC started to gain acceptance again due to American industry feeling pressure from high quality products being imported from Japan. Today, SPC is a widely used quality tool throughout many industries. SPC is method of measuring and controlling quality by monitoring the manufacturing process. Quality data is collected in the form of product or process measurements or readings from various machines or instrumentation

The data is collected and used to evaluate, monitor and control a process. SPC is an effective method to drive continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential. One of the most comprehensive and valuable resources of information regarding SPC is the manual published by the Automotive Industry Action Group (AIAG). Manufacturing companies today are facing ever increasing competition. At the same time raw material costs continue to increase. These are factors that companies, for the most part, cannot control. Therefore companies must concentrate on what they can control: their processes. Companies must strive for continuous improvement in quality, efficiency and cost reduction. Many companies still rely only on inspection after production to detect quality issues. The SPC process is implemented to move a company from detection based to prevention based quality controls. By monitoring the performance of a process in real time the operator can detect trends or changes in the process before they result in non-conforming product and scrap. Before implementing SPC or any new quality system, the manufacturing process should be evaluated to determine the main areas of waste. Some examples of manufacturing process waste are rework, scrap and excessive inspection time. It would be most beneficial to apply the SPC tools to these areas first. During SPC, not all dimensions are monitored due to the expense, time and production delays that would incur. Prior to SPC implementation the key or critical characteristics of the design or process should be identified by a Cross Functional Team (CFT) during a print review or Design Failure Mode and Effects Analysis (DFMEA) exercise. Data would then be collected and monitored on these key or critical characteristics.

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No search strategy is perfect, and we may well have missed some studies where SPC was applied to healthcare QI. There are no SPC specific keywords (eg, Medical Subject Headings, MeSH) so we had to rely on text words. Studies not containing our search terms in the title or abstract could still be of potential interest although presumably we found most of the articles where SPC application was a central element. We believe the risk that we systematically missed relevant studies to be small. Therefore, our findings would probably not have changed much due to such studies that we might have missed. The review draws on our reading, interpretation and selection of predominantly qualitative data—in the form of text and figures—in the included articles to answer the questions in our data abstraction form. The questions we addressed, the answers we derived from the studies, and the ways we synthesised the findings are not the only ways to approach this dataset. Furthermore, each member of the review team brought different knowledge and experiences of relevance to the review, potentially challenging the reliability of our analysis (Benneyan J C, Lloyd R C). An attempt was made to reduce that risk by having one investigator read all data abstraction forms, and obtain clarifications or additional data from the original articles when needed

That investigator also conducted the initial data synthesis, which was then reviewed by the entire team and the two outside experts. Although other interpretations and syntheses of these data are possible, we believe that ours are plausible and hope they are useful. The methods for reviewing studies based primarily on qualitative data in healthcare are less well developed than the more established methods for quantitative systematic reviews, and they are in a phase of development and diversification.Among the different methods for synthesising evidence, our approach is best characterised as an interpretive (rather than integrative) review applying thematic analysis—it “involves the identification of prominent or recurrent themes in the literature, and summarising the findings of different studies under thematic headings”.86 There is no gold standard for how to conduct reviews of primarily qualitative studies (Wilcock P M, Thomson R G.). Our response to this uncertainty has been to use the best ideas we could find, and to be explicit about our approach to allow readers to assess the findings and their provenance.

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Thus, the base of individual blocks (on the x axis) corresponds to the width of interval, and the height of the blocks (on the y axis) expresses the frequency of variables of the monitored variable at appropriate intervals. In quality management, it mainly refers to the frequency distribution of quality values or values related to production factors influencing the quality of the products.

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Benneyan J C, Lloyd R C, Plsek P E. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care 200312458–464.

Plsek P E. Quality improvement methods in clinical medicine. Pediatrics 1999103(1 Suppl E)203–214.

Wilcock P M, Thomson R G. Modern measurement for a modern health service. Qual Health Care 20009199–200.

Grimshaw J, McAuley L M, Bero L al Systematic reviews of the effectiveness of quality improvement strategies and programmes.

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