# SPC Charts: Overview, When to Use Them, and How to Create Them

Dr. Walter Shewhart of Bell Laboratories originated the idea of Statistical Process Control (SPC) in the 1920s. They were elaborated upon by Dr. W. Edwards Deming, who brought SPC to Japanese industry after WWII. Following the early success of Japanese enterprises, Statistical Process Control has now been adopted by organizations worldwide as a fundamental method for improving product quality by minimizing process variance.

Dr. Shewhart discovered two sources of process variation− inherent in process variation that is stable over time, and assignable, or uncontrolled variation that is unstable over time − the outcome of particular events outside the system. Chance variation was renamed Common Cause variation by Dr. Deming, while assignable variance was renamed Special Cause variation.

Dr. Shewhart developed control charts to display data over time and detect both Common Cause and Special Cause variation based on his knowledge of numerous types of process data and the principles of statistics and probability.

## What Are SPC Charts?

A statistical process control system (SPC) is a statistically based approach to controlling a manufacturing process or procedure. SPC tools and techniques may monitor process behavior, detect faults in internal systems, and develop solutions to production difficulties.

An SPC chart is used to analyze how a process evolves. All of the process data is shown in chronological sequence. 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 are the three essential components of an SPC chart.

## Why Is Dispersion So Important?

We frequently focus on average values, but knowing dispersion is crucial for industrial process management. Consider the following two examples−

• If you put one foot in a bucket of freezing water (33° F) and one foot in a bucket of boiling water (127° F), you'll feel all right (80° F) but not very comfortable!

• You should know more if you're asked to go through a river and informed that the usual water depth is 3 feet. You should reconsider your trip if you are told that the range is from 0 to 15 feet.

## What Are Control Limits?

The control limits of an SPC chart are the standard deviations above and below the center line. The process is under control if the data points are inside the control limits (common cause variation). If data points are found outside these control units, a process is out of control (particular cause variation).

Plotting the data points manually in the early phases of creating an SPC chart is better. Once you understand the formulae and their meaning, you may utilize statistical tools to update them. Various tests are used to detect an "out of control" variation. Nelson tests and Western Electric tests are two of the most common.

### Why Do SPC Control Charts Help with Quality Control?

SPC control charts are fundamental quality control tools that play an essential role in Lean manufacturing and Six Sigma activities. Manage charts can be used in various ways, but on the shop floor, they are used to analyze, control, and ensure the uniformity of production operations. By managing operations, operators can limit substantial process changes that might result in low−quality goods.

## How Do SPC Charts Work?

SPC charts necessitate cross−functional organizational commitment. Here is a step−by−step guide to creating an excellent SPC chart−

### Step 1: Select an Appropriate Measuring Method

The first step is to select whether to gather variable or attribute data. Variable data should be used whenever feasible since it delivers higher−quality information. After determining the data to collect, you may select the suitable control chart for your data.

### Step 2: Establish the Period for Data Collection and Plotting

Because SPC charts analyze changes in data over time, you must keep frequency and period in mind when collecting and plotting data. Making an SPC chart, for example, every day or every other week can help you understand whether your process is dependable and constantly improving or can fulfill quality requirements on time.

### Step 3: Create Control Units

The following step in producing an SPC chart defines the control units. Here's how to figure out the control units−

• Estimate the standard deviation (σ) of the sample data

• To calculate UCL,

$$\mathrm{UCL \:=\: average \:+ \:3 \:x\: σ}$$
• To calculate LCL,

$$\mathrm{LCL \:=\: average \:+ \:3 \:x\: σ}$$

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

The data points are plotted on the SPC chart after establishing control limits. Once the data points have been planned, you may begin to discern patterns. Recognizing these patterns is essential for determining the fundamental cause of unusual causes. Some of these patterns are dependent on specific "zones."

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

Mark any data points outside the chart's control boundaries and explore the cause. Document what was researched, the reason that caused it to go out of control, and the measures taken to control it. A corrective action matrix can be used to designate responsibilities and set target dates to track the activities done.

### Step 6: Calculate Cp and Cpk

The next step is to compute Cp (capability) and Cpk (performance) to see if the process can meet specifications.

### Step 7: Monitor The Process

The final step is constantly monitoring the process and updating the SPC chart. Regular process monitoring can provide proactive rather than reactive responses that may be too late or too costly.

## Conclusion

SPC charts are an excellent place to start with any Lean Six Sigma project. As a result, understanding these statistical control charts is critical for controlling a process.