What Is The Statistical Process Control (SPC)?

Improving quality in industry

What Is Statistical Process Control (SPC)? Statistical Process Control—SPC for short—is a fundamental industrial quality management tool that enables companies to identify, monitor and control variability in their production processes. This article explains what SPC is, how it is applied in quality control, its main characteristics and the importance of variability in this method. We also provide an effective implementation plan for applying SPC in your organization.

What Is Statistical Process Control and What Does It Involve?

Statistical Process Control (SPC) is a method that uses statistical techniques to monitor and control production processes. Its aim is to keep processes stable and predictable, minimizing variations that could affect final product quality. This is achieved through control charts, which identify whether a process is within acceptable limits or requires adjustments to correct deviations.

How is it applied in quality control?

In quality control, SPC identifies variations in production stages. Continuous data collection and statistical analysis reveal two types of variability: common causes (variations inherent in the process) and special causes (specific problems). Tools such as mean and range control charts help keep processes within control limits, detecting irregularities before they affect product quality.

Characteristics of the SPC

The SPC is distinguished by some essential characteristics:

  1. Errors prevention: By identifying variations before they affect production, it allows preventive measures to be taken.
  2. Continuous monitoring: It relies on constant data collection and real-time analysis.
  3. Corrective Actions: Facilitates the detection of special causes of variation, enabling immediate adjustments.
  4. Continuous improvement: By reducing variability, it improves the efficiency and quality of the long-term process.

Statistical process control tools

As Statistical Process Control developed throughout the last century, a standard set of statistical and analytical tools came into use. The first seven are called quality control tools, “QC” or “7-QC”. As the discipline and methodology developed, 7 additional “supplementary tools” (7-SUPP) were developed to improve the process. The seven tools of quality control (7-QC) are:

  1. Cause and effect diagram: The Cause-and-Effect Diagram, also known as the Fishbone Diagram, is a cause analysis method for identifying many possible sources of variation. It is used when the causes are unknown or may relate to other causes. The “5 Whys” process is commonly used alongside cause-and-effect analysis to identify the root cause of variation.
  2. Control sheet – A check sheet is a basic data collection tool enabling analysis of the data gathered. If a staff member can observe and collect data in the same place at the same frequency, a check sheet can provide data for identifying problems and variations.
  3. Control chart – Control charts show changes in a process over time. Each control chart has an upper and lower control limit and an average line. Observing real-time data allows comparisons over time to determine whether the process is in control or out of control. Control charts are excellent for identifying variations as they occur or determining whether a process is stable. They can also be used with special-cause variation and to help determine what actions should be taken.
  4. Histogram – Histograms represent data graphically in groups using user-defined ranges. In many ways, they resemble bar charts. A histogram groups data into ranges users can interpret, helping them understand how often a specific variable occurs within a specified range.
  5. Pareto Diagram – Everyone has seen a Pareto chart at some point. Pareto charts plot value or time as bars ordered from highest to lowest. This gives the user a graphical representation of time usage, cost or frequency for a particular asset or variable. Pareto charts are useful for determining frequency, analyzing cost by component or identifying the most significant cause on which to initiate action.
  6. Diagram of dispersion – A scatter diagram analyzes two variables to determine their relationship and how closely they correlate. If the plotted points are widely scattered, the correlation is weaker; if they lie close to the line, it is stronger. Scatter diagrams are used when data can be paired, for example to determine whether two effects share the same cause.
  7. Stratification - In short, stratification is used to group and classify data into categories. Since raw data have to be categorized to be useful, stratification is best used before data collection to know where to assign individual data points for subsequent analysis. That can include shifts, weeks, suppliers or product types.

What is variability within the SPC?

The variability is a central concept in the SPC. It refers to fluctuations that can occur in production processes due to factors such as machinery, raw material and human factor. The variability can be of two types:

  • Common causes: Variations inherent in the system, which are inevitable but can be reduced.
  • Special Causes: Variations that arise from specific problems and require immediate correction. The SPC aims to differentiate these two sources of variation to keep processes under control.

SPC implementation plan

  1. To identify critical processes: Identify processes whose variability directly affects product quality.
  2. To define product specifications: sets the quality parameters to be met.
  3. Collect process data: conduct continuous sampling of production.
  4. Analyzing data: Use control charts to monitor process variability.
  5. Take corrective actions: If special causes are detected, correct the problems immediately.
  6. Training staff: Training in statistical techniques and control charts is key to ensuring the correct implementation and monitoring of SPC.
  7. Monitoring continuously: Implementation is not static: continual evaluation ensures the process remains within control limits.

Conclusion

Statistical Process Control is a key tool for improving quality in industry. Applying it enables companies to reduce variation, prevent defects and increase efficiency. The key to success in implementation of the SPC lies in staff training, because a thorough understanding of statistical techniques ensures continuous improvement sustained over time. Companies investing in this methodology and team training will see tangible results in product and process quality.