# What is SPC | SPC Tools

# What is SPC | SPC Tools

What is SPC ? SPC is the Statistical process Control.

**History and Definition:-**

Statistical Process control is a technique of quality control which services statistical methods to monitor and control the processes. Which ensure the process stability and consistency, producing more conforming products with less waste (Defects free). SPC helps us to indicate the common and special cause’s presence in process. SPC is generally focused on continuous improvement.

Statistical Process Control was established by Walter A. Shewhart at Bell Laboratories in the year 1920 and he developed the control chart in 1924.

Control chart is the key tools to statistical process control. Control chart is used on both variable and attribute type data.

Here is the full description of What is SPC ?

**Benefits of Statistical Process control:-**

- Optimise the productivity
- Reduced scrap, rework, warranty and defects
- Increased efficiency
- Improved customer satisfaction
- Improved the Process capability
- Reduced COPQ.

**Control Chart-**

** **

- A line on a control chart used as a basis for judging the stability of a process. If the Measure points are beyond a control limit(UCL, LCL) then it evidence that special causes are affecting the process.
- Control Chart can be used to evaluate a process.
- There are basically two types of control charts, those for variable data and attributes data.

** **

The use of statistical techniques such as control chart to analyze a process, so as to take appropriate actions to improve the process capability.

**Nomenclature of Control chart:**

**Selection of Control Charts based on Attribute / Variable Type Data-**

** ****Variables Control chart:**

Average and Range Chart (X͞ and R):

Subgroup Average:

X͞ =(x1+x2+x3+…+xn)/n

n= number of samples in subgroup

Subgroup Range:

R= Xmax-Xmin (Within each subgroup)

Grand Average:

X͞͞ ͞ = (͞x1+x͞2+…+x͞k)/k

k=number of subgroups used to determine the grand average and average range.

Average Range:

R͞ =(R͞1+ R͞2+…R͞k)/k

Estimate of the standard deviation of X:

Standard Deviation =R͞ /d2

Estimate of the standard deviation of X͞: =(R͞ /d2 )/√n

Centre line:

CLx͞ = X͞ ͞

Centerline of Range =R͞

Control Limits:

UCLx͞ = X͞ ͞ +A2R͞

Upper control limit of Range = D4R͞

Lower Control limits of X͞ = X͞ ͞ -A2R͞

Lower Control limit of Range=D3R͞.

(For Subgroup size 5, A2=0.577, d2=2.326, D4=2.114)

**Attributes control Chart:**

**P chart (**for proportions of units in a category)

Centre line = P͞

**Control limits:**

*Samples not necessarily of constant size*

Upper control limits: P͞ +3x √(p͞(1-p͞)/√n)

Lower Control limits: P͞ -3x √(p͞(1-p͞)/√n)

*If the sample size is constant (n):*

Upper control limits: P͞ +3x √(p͞(1-p͞)/√n)

Lower Control limits: P͞ -3x √(p͞(1-p͞)/√n)

**Different types of Attributes type control chart:**

[P-Chart]

[np-Chart]

[u-chart]

[c-chart]

In the Above c-types Control chart, two numbers observes points are fall outside of upper control limits, its means control chart giving alarm that process has some special cause that we need to take immediate action to control the process in stable conditions.

**Interpretation of Result (Both Attributes and Variable types Data Control chart):**

The Control Charts would indicate that a process is out of Control if either one of these is true-

- One or More points fall outside the Control Limits
- When the Control Charts is divided into 3 sigma zones –

- Nine Consecutive points are on one side of the average
- There are six Consecutive points, increasing or decreasing.
- There are fourteen consecutive points that alternate up or down.

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