SUNDAY, DECEMBER 21, 2014
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Control Charts

A Guide to Control Charts

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.

A Roadmap for Using Time-weighted Control Charts

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.

From Quality Control to Quality Improvement

This article presents a case study in applying control charts and experimental design.

Go Beyond Control Chart Limitations to Predict and Improve Processes

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.

Integrating SPC and SQC to Overcome Weakness in Either

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.

Make Valid Control Chart and Subgroup Assumptions

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.

Manage Control Limits When Implementing Statistical Process Control

Learn how best to introduce statistical process control in an existing production environment where not all processes are in control and fully capable.

Manage Project Performance with EVM and Control Charts

This article describes how earned value management (EVM) indexes and control charts may be used in tangent to capture more insight from project performance.

Multivariate Control Charts: T2 and Generalized Variance

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.

Non-normal Data Needs Alternate Control Chart Approach

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.

Recalculating Control Limits

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.

Should You Use a Mean or Individuals Control Chart?

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.

Specification Limits: Proceed with Caution

A true control strategy should be aligned with the process’ statistically based control limits.

The Impact of Control Strategies on Z Shift Values

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.

When to Recalculate Control Limits

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.

Why Control Chart Your Processes?

Control charts help you identify and understand process variation. Learn the major elements and control charts and how to construct a control chart.

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