What Is Statistical Process Control or SPC

SPC stands for statistical process control, and it is a systematic approach for monitoring and maintaining quality in the manufacturing process. Monitoring process behavior, detecting internal system flaws, and addressing production difficulties can all be aided by SPC tools and methodologies. Quality control and statistical process control are frequently used interchangeably (SQC).

Quality data, such as product or process measurements, are collected in real-time throughout manufacturing. The data is then plotted on a graph with pre-determined control boundaries. The process' capabilities determine control restrictions, but the customer's desires determine specification limits.

Statistical Process Control (SPC) is one of the primary methods used in six sigma. For a better understanding of the process, it is important to comprehend the process and technique of SPC when taking up a six sigma green belt certification online. 

Why Use Statistical Process Control (SPC)?

Manufacturing companies are facing rising competition nowadays. Raw material costs are continuing to climb. These are factors over which most firms have no influence. The importance of SPC Software is that it allows you to discover and address particular causes of variation by monitoring the process and bringing it under statistical control. SPC helps optimize total profit by enhancing product quality, efficiency, simplifying processes, and improving customer service.

In a firm, the SPC technique moves quality controls from detection to prevention. The operator can see trends or changes in the process before they result in non-conforming items or scrap by monitoring its performance in real-time.

How to Use Statistical Process Control (SPC)?

• Plan: Identify the problem's primary cause and take remedial action to address it. When an issue is recognized, a remedial measure is taken right away.
• Experiment / Study: When a problem arises, SPC software assists you in analyzing the situation and empowering you to make better engineering decisions more quickly and simply throughout the product development life cycle.
• Analyze: Using digitalized test data, get a thorough picture of the manufacturing parameters and spell out any potential concerns before sending the data to R&D to improve product quality and customer satisfaction.
• Act: If the outcome is satisfactory, work on further improvements. If the outcome isn't satisfactory, seek alternative methods to improve the process.

Application of Statistical Process Control (SPC)

In statistical process control applications, it is crucial to understand and identify key product qualities that are significant to consumers and major process variance. The solution is simple.

The following are the steps for installing SPC: [1,2]

• Identify processes that have been defined.
• Determine the process's quantifiable characteristics.
• Describe the qualities' natural variation.
• Keep track of changes in the process.
• If the process is under control, monitor it.
• If the process is not under control

If the procedure is uncontrollable:

• Determine if there is a reason that can be assigned.
• Get rid of assignable causes.
• Go back to the 'Track process variation' page.

Collecting and Recording Data For Statistical Process Control (SPC)

Product dimension/feature measurements or process instrumentation readings are used to collect data for SPC. The data is then logged and tracked on several control charts, depending on the data collected. To acquire value and get significant data, choosing the right chart type is vital.

The data might be presented as a continuous variable or an attribute. Individual values and the average of a set of readings can be gathered and recorded. Here are some suggestions and examples to get you started. This is not an exhaustive list, and it should only be used as a starting point.

Variable Data

• Individual – Moving Range Chart: Use this chart if your data comprises single numbers.
• If you're capturing data in subgroups of 8 or less, use the Xbar – R chart.
• If your subgroup size is more than 8, use the Xbar – S chart.

Attribute Data

• P chart - Used to keep track of how many faulty components there are in a batch of parts.
• The amount of flaws in each part is recorded using a U chart.

Control Charts in Statistical Process Control (SPC)

Control charts for variable data include the X-bar and R chart. The X-bar represents the average or "mean" value of the variable x. The X-bar chart shows how the sample means or averages differ. The range chart depicts the subgroup's variance. A range is a difference between the highest and lowest value.

Analyzing the Data in Statistical Process Control (SPC)

If only common causes and no special causes have been discovered, the data points reported on a control chart should fall between the control boundaries. Special causes are often outliers or fall outside the control limits, whereas common causes will fall inside. 

There should be no unexpected causes in any charts for a process to be termed statistically controlled. No specific causes will be uncovered in a regulated procedure, and the data should fall inside the control bounds.

Some examples of frequent cause variation are as follows:

• Within a standard, there is a variation in material quality.
• Throughout the year, there are changes in the ambient temperature or humidity.
• Machines and tools go through normal wear and tear.
• Variability in the options available to the operator.
• It's natural for measurements to vary.

Special causes frequently occur outside the control boundaries or indicate a major change or shift.

Here are some examples of specific cause variation:

• Controllers that have stopped working.
• The equipment is misaligned.
• A change to the measurement system.
• There has been a shift in the procedure.
• The equipment has malfunctioned.
• Raw material properties that aren't mentioned in the design.
• A broken tool, a punch, or a bit.
• The operator is new to the operation and has no prior experience.

By addressing any specific reasons, trends, or adjustments in the process, we can ensure that we create components that meet the customer's expectations. Remember that the control limits should be between the engineer's and the customer's spec limits.

Conclusion 

A six sigma process employs statistical process control (SPC) and its counterpart, statistical quality control (SQC). Six Sigma has its origins in the Japanese manufacturing process, as does one of its key tools, SPC.

However, because they applied management concepts and effective measurement techniques, they have become a fundamental quality standard for the United States and Europe. SPC is also an integral foundation that is needed for six sigma certification.