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What Is Statistical Process Control and What’s a SPC Chart?

What Is Statistical Process Control

Organizations must tightly control quality to ensure they are delivering products and services that meet their performance standards, user demands, and industry compliance regulations. Out-of-compliance products or services can lead to unhappy customers, tarnish a company’s reputation, and create costly rework and waste.

Enter statistical process control charts (SPCs).

SPCs help track quality and monitor the stability and variability of processes over time to ensure consistency and flag deviations. When trying to understand SPC charts, it helps to define what SPC is, why it’s important, its benefits, and how you can learn more through an online quality management course.

What Is Statistical Process Control?

Statistical process control (SPC) is a quality management technique used to monitor and control industrial processes by analyzing and interpreting statistical data. It involves the collection and analysis of data to detect variations and trends within a process, enabling organizations to identify and address potential issues before they result in defects or deviations from desired standards. SPC provides a systematic approach for maintaining process stability and improving overall product or service quality through the application of statistical methods and control charts.

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Why Use Statistical Process Control?

While statistical control charts are now used in a variety of industries to refine and improve processes, the earliest application of the methodology was in manufacturing. Manufacturers are constantly looking for process improvements to reduce waste and optimize efficiency, after all.

Here’s the deal: defects discovered downstream are expensive to fix, especially in large production runs. SPC helps monitor performance in real-time to uncover trends or changes in processes before they result in faulty products or services.

SPC Benefits

SPC provides several significant benefits, including:

  • Minimizes waste and rework
  • Increases process efficiency
  • Enhances product quality
  • Helps detect defects earlier
  • Increases product reliability
  • Helps analyze processes for improvement

SPC techniques are designed to evaluate and monitor processes for continuous improvement. Doing so helps improve overall quality, customer satisfaction, and cost-efficiency.

What Are SPC Charts?

The whole point of quality assurance (QA) and quality control (QC) is to determine best practices and create standard operating procedures to ensure optimal quality. An SPC control chart helps team members track process changes and identify the impacts of those changes over time.

SPC charts have three main components:

  • Central line shows the expected value of the process based on averages from historical data
  • Upper control unit represents the maximum acceptable variation
  • Lower control unit represents the minimum acceptable variation

SPC Graph

SPC charts are used to predict expected outcomes, determine when processes are stable, and uncover and correct defects outside control limits when they occur.

What Are Control Limits in SPCs?

Control limits are the standard deviations that appear above or below the central line. If data is located within the upper and lower control units, processes are considered “in control.” In other words, results are within the acceptable range of variations.

If results are outside the control units, processes are considered “out of control” and require further evaluation to determine the cause of such anomalies.

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How to Implement SPC Charts

You can create an SPC chart in Excel, other spreadsheets that support graphing and statistical analysis, or SaaS-based SPC platforms. Regardles of what tools you use, implementing SPC charts requires these steps:

Define Measurements

The first step is to identify what want to measure and how you will do so. Variable data generally provides a higher quality of information.

Determine Time Intervals

SPC charts plot data in time order, depending on the period you want to examine. Since SPC charts measure deviations over time, you must establish consistent frequencies and periods for collecting and plotting data. SPC charts can be analyzed daily, weekly, monthly, or for any period you determine is appropriate.

Establish Control Units

To establish control units, you need to calculate the standard deviation (σ) from your sample data to determine your acceptable upper control and lower control units.

  • Upper control limit (UCL) = average + 3 x σ
  • Lower control limit (LCL) = average – 3 x σ

Plots Data Points

With your limits defined, you can plot data points and look for patterns that fall outside acceptable limits.

Correct Out-of-Control Data Points

Project and process managers investigate any out-of-control data points to determine what causes the deviations and take steps to resolve any problems.

Calculate Cp and Cpk

Two important data points to examine are Cp and Cpk. The process capability index (Cp) measures the potential of a process to produce future results within specification limits. The process capability index with centering (Cpk) takes into account process variation and deviation from the process to determine how well processes perform against targets.

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

Monitor Progress

The final step is to monitor progress and performance in your statistical process control chart.

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The 7 Quality Control (7-QC) Tools

The 7 Quality Control (7-QC) tools were first created by quality management expert Dr. Kaoru Ishikawa. Also called the Seven Basic Tools of Quality, 7-QC tools are used across a variety of industries to provide a structured approach to problem-solving to reduce defect rates and improve process quality.

#1. Pareto Charts

A Pareto chart is a bar graph that depicts the frequency or cost in terms of time or money, highlighting the impact of various issues in processes.

Use cases include:

  • Analyzing the frequency of problems
  • Prioritizing the most significant problems
  • Analyzing correlations among specific components

#2. Cause-and-Effect Diagram (Also Called a Fishbone or Ishikawa Diagram)

The cause-and-effect diagram visually identifies and analyzes potential causes or problems that contribute to outcomes.

Use cases include:

  • Root cause analysis
  • Problem-solving
  • Brainstorming potential causes
  • Generating ideas for improvement

#3. Check Sheet

Checks sheets are a data collection tool used to gather and organize data.

Use cases include:

  • Tracking defects
  • Recording process observations
  • Documenting events or issues
  • Collecting data for analysis

#4. Histogram

Histograms are a visual representation of data distribution, showing the frequency of observations within periods.

Use cases include:

  • Analyzing data distribution
  • Identifying potential process deviations
  • Understanding data variability

#5. Scatter Plot

Scatter plots are graphical representations of the relationships between two variables.

Use cases include:

  • Identifying patterns, trends, or correlations between variables
  • Understanding the impact of process parameters on performance

#6. Control Chart

Control charts are visual representations that show how a process changes over time, comparing data to baselines (centerline).

Use cases include:

  • Evaluating process stability
  • Identifying trends or shifts in data
  • Determining whether processes operate within statistical controls

#7. Stratification

Stratification breaks down data into meaningful sub-groups for analysis.

Use cases include:

  • Identifying patterns or anomalies within data
  • Examining variations across different groups
  • Understanding the impact on process performance

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The 7 Supplemental (7-SUPP) Tools

There are also supplemental tools that are used with statistical process control charts to provide additional insight into the QA/QC process to identify patterns, monitor progress, and improve processes. Called 7-SUPP tools, they include the following:

  1. Data stratification divides data sets into sub-groups to compare performance across groups and isolate areas for improvement.
  2. Defect maps graph the distribution of defects within a process to identify areas of the biggest impact.
  3. Events logs capture and document incidents and deviations during a process or project.
  4. Process flowcharts visually represent the sequence of events within a process.
  5. Progress centers are designated areas that perform ongoing quality checks.
  6. Randomization assigns elements randomly for experimental design.
  7. Sample size determination helps uncover the number of samples needed to make statistically valid conclusions.

SQC vs. SPC

SPC and statistical quality control (SQC) both play a significant role in analyzing, monitoring, and improving quality. However, there are key differences.

SPC applies statistical and analytical tools to control process inputs. SQC uses the same tools to monitor process outputs. SPC helps reduce defects in processes, while SQC measures the results of processes to ensure they meet project requirements.

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FAQs

What is a SPQ chart used for?

An SPC chart, also known as a control chart, is used to monitor and analyze the variation in a process over time. It visually displays data points plotted on a chart and control limits, which are statistical boundaries representing acceptable variation. SPC charts help identify data patterns, trends, and outliers, enabling organizations to assess process stability, detect deviations, and take appropriate corrective actions. By providing real-time insights into process performance, SPC charts aid in quality control, process improvement, and maintaining consistent standards, ultimately enhancing product or service quality.

Why Are SPC’s Important?

SPC charts are important in various industries and organizations in the. They provide a data-driven approach to quality management, enabling businesses to identify and address process variations efficiently. By tracking and analyzing data over time, SPC charts help detect anomalies, trends, and shifts in process performance, allowing timely interventions to prevent defects or deviations. This proactive monitoring helps businesses uphold high quality standards, reduce waste, optimize resources, and enhance customer satisfaction. SPC charts provide a visual representation of data, making it easier for American organizations to communicate and collaborate across teams, facilitating informed decision-making and continuous improvement efforts. Overall, SPC charts play a crucial role in driving efficiency, productivity, and competitiveness in the American business landscape.

What Are the Methods Used in Statistical Process Control?

Statistical process control (SPC) utilizes several methods to monitor and analyze process variation. These methods include control charts, which graphically display process data over time, enabling the identification of trends, shifts, and out-of-control conditions. Other SPC techniques involve calculating and interpreting statistical measures such as mean, range, standard deviation, and process capability indices. Hypothesis testing is employed to assess whether observed variations are statistically significant. Additionally, Pareto analysis helps prioritize and address the most significant sources of process variation. SPC also incorporates tools like scatter plots, histograms, and cause-and-effect diagrams for data visualization and root cause analysis. By employing these diverse methods, SPC enables organizations to effectively understand, control, and improve their processes to achieve optimal quality and performance.

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