Control charts

Many manufacturing industries and huge businesses use control charts to figure out whether their processes happen in a controlled statistical state or not. Control charts are process-behavior charts, commonly known as Shewhart charts, plotted against time. They show the progress of a process over time.

The future outcomes are predicted from the data applied on these control charts. These charts have a central line of average, a lower control limit line, and an upper limit line to track the performances of the business processes. If any changes or corrections are needed to control the parameters of a process, they can be tracked via control charts. If no such correction is needed, this implies that the work progress is stable.

Types of control charts

Chart	Process observation	Process observations relationships	Process observations type	Size of shift to detect
x̄ and R chart	Quality characteristic measurement within one subgroup	Independent	Variables	Large (≥ 1.5σ)
x̄ and s chart	Quality characteristic measurement within one subgroup	Independent	Variables	Large (≥ 1.5σ)
Shewhart individuals control chart (ImR chart or XmR chart)	Quality characteristic measurement for one observation	Independent	Variables	Large (≥ 1.5σ)
Three-way chart	Quality characteristic measurement within one subgroup	Independent	Variables	Large (≥ 1.5σ)
p-chart	Fraction nonconforming within one subgroup	Independent	Attributes	Large (≥ 1.5σ)

Chart	Process observation	Process observations relationships	Process observations type	Size of shift to detect
x̄ and R chart	Quality characteristic measurement within one subgroup	Independent	Variables	Large (≥ 1.5σ)
x̄ and s chart	Quality characteristic measurement within one subgroup	Independent	Variables	Large (≥ 1.5σ)
Shewhart individuals control chart (ImR chart or XmR chart)	Quality characteristic measurement for one observation	Independent	Variables	Large (≥ 1.5σ)
Three-way chart	Quality characteristic measurement within one subgroup	Independent	Variables	Large (≥ 1.5σ)
p-chart	Fraction nonconforming within one subgroup	Independent	Attributes	Large (≥ 1.5σ)
np-chart	Number nonconforming within one subgroup	Independent	Attributes	Large (≥ 1.5σ)
c-chart	Number of nonconformances within one subgroup	Independent	Attributes	Large (≥ 1.5σ)
u-chart	Nonconformances per unit within one subgroup	Independent	Attributes	Large (≥ 1.5σ)
EWMA chart	Exponentially weighted moving average of quality characteristic measurement within one subgroup	Independent	Attributes or variables	Small (< 1.5σ)
CUSUM chart	Cumulative sum of quality characteristic measurement within one subgroup	Independent	Attributes or variables	Small (< 1.5σ)
Time series model	Quality characteristic measurement within one subgroup	Autocorrelated	Attributes or variables	N/A
Regression control chart	Quality characteristic measurement within one subgroup	Dependent of process control variables	Variables	Large (≥ 1.5σ)

Features of control charts

1. The points in the control charts represent statistical measurements of the data like mean, proportion, range, etc.
2. Mean is calculated for all the samples of the data in control charts for analysis.
3. At the mean, a centre line is drawn.
4. Find the standard deviation for all the samples.
5. Upper and lower limits in the control charts indicate favorable and unfavorable situations of the processes for which the standard deviation was found.

Uses of control charts

1. Controlling limits with the help of control charts is more accessible in engineering tolerance.
2. Specification limits can also be controlled to focus on the main operation with maximum outputs.
3. Control charts help to detect events that change whenever an actual process changes.
4. It helps to eliminate harmful elements that hinder the progress of the work during manufacturing. v 5. Engineers can detect the systematic patterns and suggest introduction in a process known as special-cause variation. This is done to add warning limits in the control charts that can improve the quality of a product.