Statistical process control (SPC) techniques

What is Statistical Process Control?

Statistical Process Control (SPC) is a quality management and process improvement methodology. SPC is useful to monitor and control the quality and stability of a process. SPC primarily relies on statistical analysis techniques to identify and understand variation within a process. Therefore, the goal of SPC is to ensure that a process remains within defined control limits and meets customer specifications consistently over time.

Statistical process control


SPC was pioneered by Walter A. Shewhart, an American physicist, engineer, and statistician. Shewhart was a prominent figure at the Western Electric Company’s Hawthorne Works in the early 1920s. He conducted groundbreaking research on quality control and statistical methods.

Key Principles of Statistical Process Control:

  1. Data Collection:

    SPC relies on the collection of data from the process. This data may come from various stages of the process. Further, this data should be gathered at regular intervals and should be representative of the process performance. It’s important to use consistent and accurate methods for data collection.

  2. Variation Analysis:

    SPC recognizes that processes naturally exhibit variation. In addition, we categorize these variations as common cause variation or special cause variation. Common cause variation represents the inherent variability of a stable process, whereas special cause variation arises from external factors or specific events that lead to abnormal results.

  3. Control Charts:

    The heart of SPC lies in the use of control charts (also called Shewhart charts) to visually monitor process performance over time. Control charts plot data points on a graph with control limits. These are the statistical boundaries that define the expected variation of a stable process. The data points are compared to these control limits to identify patterns and trends. This indicates the presence of special cause variation.

    There are different types of control charts depending on the type of data and the characteristics of the process, such as:

    • Individuals (I-MR) Chart: Used for continuous data from a single data point (I) and the moving range between consecutive data points (MR).
    • X-bar and R Chart: Suitable for monitoring the average (X-bar) and range (R) of subgroups of data.
    • X-bar and S Chart: Similar to the X-bar and R chart, but using the standard deviation (S) instead of the range.
  4. Identifying Outliers:

    SPC aims to identify outliers or data points that fall outside the control limits. Outliers suggest an issue with the process that requires investigation and corrective action.

  5. Process Improvement:

    Sometimes, processes might exhibit common cause variation but might not meet the desired quality levels. In such cases, SPC encourages process improvement through root cause analysis and the implementation of corrective actions.

  6. Continuous Monitoring:

    SPC is not a one-time analysis but rather an ongoing monitoring process. Regularly updating the control chart and reviewing data helps maintain process stability over time.

Statistical Process Control Tools and Techniques

  1. Control Charts (Shewhart Charts):

    As mentioned earlier, control charts are graphical representations of process data over time. They help us identify variations and trends in the process. Some common types of control charts are:

    • Individual (I-MR) Chart: Used for monitoring continuous data from a single data point and the moving range between consecutive data points.
    • X-bar and R Chart: Monitors the average (X-bar) and range (R) of subgroups of data.
    • X-bar and S Chart: Similar to the X-bar and R chart, but uses the standard deviation (S) instead of the range.
  2. Pareto Charts:

    A Pareto chart is a bar graph that displays the frequency or occurrence of various issues or defects in descending order. It helps prioritize the most significant factors affecting the process, so you can focus on improving those areas first.

  3. Histograms:

    Histograms are graphical representations of data distribution. They show the frequency of occurrences of values within specified intervals (bins). They further help us understand the shape of the data and detect any potential issues with the process, such as skewness or abnormal distributions.

  4. Scatter Plots:

    Scatter plots are used to visualize the relationship between two variables. They help identify any correlations between factors that may impact the process performance. Identifying such relationships can guide decision-making during process improvement efforts.

  5. Cause-and-Effect Diagrams (Fishbone or Ishikawa Diagrams):

    These diagrams help identify potential causes of a problem or variation. By categorizing and organizing potential root causes, the team can conduct a more systematic investigation into the reasons behind process deviations.

  6. Run Charts:

    A run chart displays the process data over time in chronological order. It helps visualize trends and patterns in the data, making it easier to spot shifts or unusual occurrences in the process.

  7. Process Capability Analysis:

    Process capability analysis assesses the ability of a process to produce outputs that meet customer requirements. It involves calculating various capability indices like Cp, Cpk, Pp, and Ppk to determine if the process is capable of producing within specification limits.

  8. Regression Analysis:

    Regression analysis is used to study the relationship between a dependent variable and one or more independent variables. It can help identify factors that significantly influence process performance.

  9. Six Sigma Tools:

    Though not specific to SPC, Six Sigma tools like DMAIC (Define, Measure, Analyze, Improve, Control), hypothesis testing, and design of experiments (DOE) are often used in conjunction with SPC to achieve continuous improvement and process optimization.

Remember, the choice of specific SPC techniques depends on the type of data, the nature of the process, and the goals of the improvement project. Using these techniques in the Control phase of DMAIC will enable your team to keep the process on track, detect deviations, and maintain a stable and efficient process over time.


In conclusion, statistical process control provides valuable insights into process performance, enabling organizations to make data-driven decisions for quality improvement. Primarily, control charts, process capability analysis, regression analysis, design of experiments, hypothesis testing, and Pareto analysis are some of the essential SPC techniques that can be used to enhance process efficiency, reduce variation, and ultimately deliver high-quality products or services.

Scroll to Top