Every process is designed to generate output - either a product or a service. In addition to this, processes generate a lot of data as well. Statistical Process Control or SPC is a statistical method of using the data generated by a process to control and improve it continually.

## What Are SPC Charts?

A statistical process control system (SPC) is a method of controlling a production process or method utilizing statistical techniques. Monitoring process behavior, identifying problems in internal systems, and finding solutions to production problems can all be accomplished using SPC tools and procedures.

An SPC chart is used to study the changes in the process over time. All the data generated from the process are plotted in time order. The three main components of an SPC chart are - a central line (CL) for the average, a lower control line (LCL) for the lower control unit, and an upper control line (UCL) for the upper control unit.

Fig: Sample SPC Chart (Source)

SPC charts were initially developed by Dr. Walter A. Shewhart of Bell Laboratories in the 1920s. This is why they are also known as Shewhart charts. However, they were made popular by Dr. W. Edwards Deming when he introduced the concept to the Japanese industry after World War II. Nowadays, SPC charts have been incorporated by organizations around the world as one of the primary tools to monitor and improve the control of a process.

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## What Are Control Limits?

Control limits are the standard deviations located above and below the center line of an SPC chart. If the data points are within the control limits, it indicates that the process is in control (common cause variation). If there are data points outside of these control units, it indicates that a process is out of control (special cause variation).

Fig: Control limits of an SPC chart (Source)

It is best to plot the data points manually in the early stages of making an SPC chart. Once the formulas and meaning is understood, you can use statistical software to update them. There are a number of tests that are used to detect an “out of control” variation. Some of the most popular ones are Nelson tests and Western Electric tests.

## How to Implement SPC Charts?

SPC charts require organization commitment across functional boundaries. Here is a step by step process on how you can construct an effective SPC chart:

### Step 1: Determine an Appropriate Measurement Method

The first step is to decide what type of data to collect - variable or attribute. It is highly advisable to use variable data wherever possible as it provides a higher quality of information. Once you decide what type of data to collect, you can then choose the appropriate control chart for your data.

### Step 2: Determine the Time Period for Collecting and Plotting Data

Because SPC charts measure the changes in data over time, it is necessary that you maintain a frequency and time period to collect and plot the data. For example, making an SPC chart every day or every other week can help you see whether your process is reliable and improving constantly or whether you will be able to meet quality standards in time.

### Step 3: Establish Control Units

The next step in creating an SPC chart is to establish the control units. Here is how you can calculate the control units:

- Estimate the standard deviation (σ) of the sample data
- To calculate UCL,

UCL = average + 3 x σ

- To calculate LCL,

LCL = average - 3 x σ

### Step 4: Plot Data Points and Identify Out-of-Control Data Points

After establishing control limits, the next step is to plot the data points on the SPC chart. Once you’ve plotted the data points, you can start to see patterns in them. Recognizing these patterns is the key to finding the root cause of special causes. Some of these patterns depend on certain “zones”.

Fig: Sample SPC chart with zones (Source)

Here are the eight rules used to identify an out-of-control condition.

Rule |
Rule Name |
Pattern |

1 |
Beyond Limits |
One or more points beyond the control limits |

2 |
Zone A |
2 out of 3 consecutive points in Zone A or beyond |

3 |
Zone B |
4 out of 5 consecutive points in Zone B or beyond |

4 |
Zone C |
7 or more consecutive points on one side of the average (in Zone C or beyond) |

5 |
Trend |
7 consecutive points trending up or trending down |

6 |
Mixture |
8 consecutive points with no points in Zone C |

7 |
Stratification |
15 consecutive points in Zone C |

8 |
Over-control |
14 consecutive points alternating up and down |

### Step 5: Correct Out-of-Control Data Points

Whenever you find any data points lying outside the control limits, mark it on the chart and investigate the cause. Also, document what was investigated, the cause that led to it being out of control and the necessary steps taken to control it. You can use a corrective action matrix to identify responsibilities and set target dates to track the actions taken.

### Step 6: Calculate Cp and Cpk

The next step is to calculate Cp (capability) and Cpk (performance) to determine whether the process is able to meet specifications.

Cp is calculated as

And Cpk is calculated as

where,

- X = process average
- LSL = Lower Specification Limit
- USL = Upper Specification Limit
- σest = Process Standard Deviation

### Step 7: Monitor The Process

The last step is to continually monitor the process and keep updating the SPC chart. Regular monitoring of a process can provide proactive responses rather than a reactive response when it may be too late or costly.

## Uses of SPC Charts

SPC charts are used for continuous improvement of a process using a number of techniques. There are a number of ways SPC charts can help business analysts, but the most important ones are as follows:

- Find and correct problems as soon as they occur
- Predict the expected outcomes of a process
- Determine whether a process is in a stable condition
- Provide information on which areas to prioritize on to improve the process

## Here’s What You Should Do Next

SPC charts are one of the starting points for any Lean Six Sigma project. As such, it is important to understand these statistical control charts well to keep a process under control. If you are interested to learn more, you can start off with Simplilearn’s Lean Six Sigma Green Belt certification online program. This course integrates lean and the DMAIC methodology with case studies to provide you the skills required for an organization's growth.

# FAQs

### Q1: What is an example of statistical control?

Statistical control is applied to any process wherein conforming with product specifications is required, and the output can be measured. An example of statistical process control is its application in manufacturing lines.

### Q2: What is statistical control in statistics?

Statistical process control, abbreviated as SPC, is the usage of statistical approaches to regulate a process/ production method. SPC tools help monitor process behavior, find issues in internal systems, and discover solutions for production problems.

### Q3: What are the three basics of statistical process control?

The three essential components of a statistical process control chart include a central line (CL) for the average, an upper control line (UCL) for the upper control unit and a lower control line (LCL) for the lower control unit.

### Q4: What are SPC charts?

SPC or Statistical Process Control charts are simple graphical tools that assist process performance monitoring. These line graphs show a measure in chronological order, with the time/ observation number on the horizontal (x) axis and the measure on the vertical (y) axis.

### Q5. Which software is used for SPC?

One of the popular software for data analysis and quality improvement is Minitab. Real-Time SPC powered by Minitab provides real-time capabilities and trusted analysis for process monitoring in a comprehensive solution.