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<p>Statistical control charts are essential tools for monitoring and controlling processes. They help to interpret their stability and detect deviations when the process is out of statistical control due to special causes. However, this sensitivity can be a disadvantage when we have processes that have high capacity, and some slack can be allowed. In this case, the control charts need to be “desensitized” to detect small changes that have no practical significance. The increasing availability of data has made profile monitoring an emerging area of research. The location control chart is a prominent alternative due to its applicability and simplicity. It maintains all information about the data observed in each location where the profile needs to be evaluated. This article discusses the importance of adjusting location control charts based on <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_15950_Article_IEq3.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="25" /> </InlineMediaObject> <EquationSource Format="TEX">\({C}p\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">Cp</mi> </mrow> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_15950_Article_IEq4.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="35" /> </InlineMediaObject> <EquationSource Format="TEX">\({C}pk\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">Cpk</mi> </mrow> </math></EquationSource> </InlineEquation> capability indices to ensure that signals presented in an out-of-control process, without a high risk of producing non-defective items, are ignored. The results obtained show that there was a significant reduction in the probability of signaling that the process is out of control when this is irrelevant or, at least, not of practical or economic significance. In conclusion, the location control chart with expanded control limits is a better choice for processes that have high capacity, and some slack can be allowed. This is because it significantly reduces the probability of detecting an out-of-control process when it does not represent an economic or practical threat.</p>

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Shewhart-type location control chart based on capability indices \({{\varvec{C}}}{\varvec{p}}\) and \({{\varvec{C}}}{\varvec{p}}{\varvec{k}}\) applied to profile monitoring

  • 期刊论文

  • 发表日期：

    2025年6月24日

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  • Daily Morales,
  • Pedro Carlos Oprime,
  • Damaris Chieregato Vicentin,
  • Carlos Renato Bueno

摘要

Statistical control charts are essential tools for monitoring and controlling processes. They help to interpret their stability and detect deviations when the process is out of statistical control due to special causes. However, this sensitivity can be a disadvantage when we have processes that have high capacity, and some slack can be allowed. In this case, the control charts need to be “desensitized” to detect small changes that have no practical significance. The increasing availability of data has made profile monitoring an emerging area of research. The location control chart is a prominent alternative due to its applicability and simplicity. It maintains all information about the data observed in each location where the profile needs to be evaluated. This article discusses the importance of adjusting location control charts based on \({C}p\) Cp and \({C}pk\) Cpk capability indices to ensure that signals presented in an out-of-control process, without a high risk of producing non-defective items, are ignored. The results obtained show that there was a significant reduction in the probability of signaling that the process is out of control when this is irrelevant or, at least, not of practical or economic significance. In conclusion, the location control chart with expanded control limits is a better choice for processes that have high capacity, and some slack can be allowed. This is because it significantly reduces the probability of detecting an out-of-control process when it does not represent an economic or practical threat.