Control charts have two general uses in an improvement project. This article provides an overview of the different types of control charts to help practitioners identify the best chart for any monitoring situation.
Selecting the correct type of control chart is vital. And with the wide range of options available, the selection of a chart to suit a particular process and data set can be difficult, especially when using time-weighted control charts.
This article presents a case study in applying control charts and experimental design.
Control chart stability (CCS) is not always needed for either predictions or baseline estimates for process improvement. By recognizing the limitations of CCS, better prediction methods can be used that require less effort.
SQC and SPC are two powerful tools, which have different goals and requirements for successful application. By using a methodology that combines the strengths of both approaches, it is possible to overcome the individual weaknesses of each one.
Four real world case studies that display how incorrect assumptions can lead to invalid control charts and improper statistical process control (SPC). Don't make these same control chart and subgrouping mistakes.
Learn how best to introduce statistical process control in an existing production environment where not all processes are in control and fully capable.
This article describes how earned value management (EVM) indexes and control charts may be used in tangent to capture more insight from project performance.
Applying univariate control charts is possible but inefficient and can lead to erroneous conclusions when working with more than one process variable. Instead, use multivariate control charts.
Some people believe that it is not necessary to transform non-normal data before creating an individuals control chart. The chart, however, is not robust to non-normally distributed data. Therefore, it is important to use an alternate approach.
The tedious task of analyzing control charts for validity of the limits can be eliminated and the resulting report can be used as a tool to keep the process of continuous improvement on track.
To plot individual data or to group the data and plot the mean on a control chart, that is the question. This article helps you understand how to make the most of your data.
A true control strategy should be aligned with the process statistically based control limits.
When special control strategies are applied to typical process data, the observed relationship between Z(short-term) and Z(long-term) is reproduced, and a relationship to estimate Z(short-term) from Z(long-term, discrete) from defect counts is derived.
A problem that has often confronted practitioners using control charts is when to consider recomputing the control limits. A simple decision matrix and explanation is included in this article.
Control charts help you identify and understand process variation. Learn the major elements and control charts and how to construct a control chart.