Control Chart Vs Run Chart

Fahad Usmani, PMP

September 20, 2024

Control charts and run charts are key tools in quality management and process improvement. They help you monitor performance over time. While both charts plot data points in sequence, they serve distinct purposes.

A control chart is used in Statistical Process Control (SPC) and tracks data against predefined control limits to detect process stability and variations. A run chart is a simpler tool that identifies trends and shifts in data without considering control limits.

Control charts are part of seven basic quality, while run charts are not. However, run charts are equally important in quality management as the control charts.

Understanding the differences between these charts can help teams choose the right tool for analyzing process performance and driving continuous improvement.

In today’s post, I will discuss the control chart vs run chart in detail so you will not have a problem using these tools in your processes.

What is a Control Chart?

Control charts help you monitor process stability and consistency over time. They plot data points in a time sequence to show variations and determine whether they fall within acceptable control limits.

A control chart has a central line representing the process average and upper and lower control limits. The project manager defines the control limits, and data points outside these limits indicate special cause variation, signaling that the process may be out of control. Control charts help maintain quality and improve process performance by identifying trends and patterns.

These charts have specification limits above and below control limits, which the client defines. Any data that falls outside of these limits is a non-conformance.

Control charts display both common cause and special cause variations. Common cause variations are normal and don’t require action, while special cause variations need intervention. Control charts can indicate false special cause variations, wasting time and resources.

Elements of Control Charts

A control chart has three elements:

1. Mean

The mean, or central line, in a control chart represents the average value of the process being monitored. It is a reference point for assessing whether the process is stable or fluctuating. Data points plotted on the chart are measured against this average, allowing teams to identify deviations.

A stable process will have data points distributed evenly around the mean. The mean helps detect shifts in performance, and any significant movement away from this central value may indicate process issues that require investigation.

2. Control Limits

Control limits are the boundaries drawn above and below the mean on a control chart, normally set at ±3 standard deviations from the mean. These limits define the expected range of natural variation in the process. Data points outside the control limits suggest that special cause variation is present, meaning the process may be out of control.

Control limits are not the same as specification limits; they are statistical boundaries that help identify whether the process is stable and predictable.

3. Specification Limits

Specification limits are the customer-defined acceptable range for a product or process. Unlike control limits, which are statistical boundaries defined by the project manager, specification limits are set based on the requirements for performance or quality. They represent the maximum and minimum acceptable values for the process outcome.

If a process consistently stays within the specification limits, it meets customer or product requirements. However, even if a process is within control limits, it may fail to meet specification limits if the average is off target.

Control charts help you find answers to the following:

Is the process under control?
Is the project moving in the right direction?
Are the deliverables within the specification limits?

The Rule of Seven

You may assume that immediate action is required when an observation falls outside the control or specification limits. However, this isn’t always true. Sometimes, you may need to investigate even when all data points are within control limits. One such instance is known as the “Rule of Seven.”

This rule states that if seven or more consecutive data points appear on one side of the mean, an investigation should be initiated, regardless of whether the points are still within control limits. This pattern may indicate an underlying process shift that needs attention.

Types of Control Charts

c Control Charts: A c control chart monitors the count of non-conforming items in a production process. It helps identify the number of defects in mass-produced items with a constant sample size. This chart is useful for tracking errors or defects in fixed-size batches.

p Control Charts: p control charts are effective when the sample sizes are unequal. They monitor the proportion of defective units in a sample, making them suitable for processes where the number of units inspected varies over time.

U Control Charts: U control charts investigate defects per unit when the sample size varies. They focus on the number of defects in each sample and are particularly useful when defects fall outside the upper or lower control limits.

X-Bar and R Charts: X-Bar and R charts monitor the mean (X-Bar) and range (R) of subgroups. They are best suited for smaller subgroup sizes, five or fewer. The R chart tracks variation within the subgroups, while the X-Bar chart measures the average performance.

X-Bar and S Charts: X-Bar and S charts are designed for larger subgroups, normally five or more. Instead of the range, these charts use standard deviations (S) to assess variability. They help monitor both the mean and variability, providing insights into the stability of larger sample groups over time.

Example of Control Charts in Real World Scenario

In a manufacturing plant producing electronic components, a control chart monitors the diameter of a critical part. The engineering team collects measurements from samples taken every hour. By plotting these measurements on the control chart, they can visualize process stability.

When data points fall outside the control limits, the team investigates potential causes, such as machine calibration issues, allowing them to take corrective actions and maintain product quality.

Benefits of Control Charts

Process Monitoring: Control charts help track the performance of a process over time, allowing for early detection of unusual variations or trends before they result in defective outputs.
Improved Quality Control: Control charts help maintain consistent quality by distinguishing between common-cause and special-cause variations.
Data-Driven Decisions: Control charts provide objective, real-time data that guide decision-making, reducing reliance on intuition and guesswork in process management.
Cost Reduction: Detecting process deviations early helps prevent defects and rework, leading to reduced waste, improved efficiency, and lower operational costs.

Limitations of Control Charts

Requires Statistical Knowledge: Properly interpreting control charts requires an understanding of statistical principles, which may be a barrier for some users without the necessary training.
Assumes Normality: Control charts often assume that the data follows a normal distribution, which may not always be true in real-world scenarios, leading to inaccurate conclusions.
Limited to Measurable Data: Control charts can only be used for processes where data can be measured or quantified. They do not capture qualitative factors or subjective variations.
Time-Consuming Setup: Initial setup, including selecting the right chart type and collecting enough data to establish control limits, can be time-consuming and resource-intensive.

What is a Run Chart?

A run chart is a simple graph that tracks data points in a time sequence. It helps identify trends, shifts, or patterns in a process. Unlike control charts, run charts do not include control limits and instead focus on visualizing how a process behaves over time.

Each data point represents a measurement, and the chart shows how the values change to a central line, typically the median. Run charts are useful for detecting non-random variation and shifts in performance, making them a valuable tool for process improvement and quality monitoring in various industries.

A run chart helps you analyze:

Trends in the process.
If the process has shifted.
Whether or not the process has any patterns.

Small to medium-sized organizations use run charts to communicate project progress with clients.

You will need to draw the planned progress line. Once the project starts, you will update this chart with a new line showing the actual progress. This lets the client see status and variation.

Example of Run Chart in Real World Scenario

An example of a run chart can be observed during a live match broadcast if you are a cricket fan. As the commentator presents the score of the first team, you get a clear visual of their performance.

Once the second team starts batting, you can track and compare their progress in real-time. This lets you see how each team’s scores evolve throughout the match, providing valuable insights into their performances.

Benefits of Run Charts

Easy to Use: Run charts are simple to create and interpret, requiring no complex statistical knowledge.
Identifies Trends: They help detect trends, shifts, or patterns in data over time, making it easier to spot process improvements or issues.
Monitors Process Performance: Run charts represent a process’s performance over time, offering real-time insights.
Quick Insights: Run charts have no control limits and give a quick snapshot of performance without the need for deep analysis.

Limitations of Run Charts

No Control Limits: Unlike control charts, run charts do not have control limits, making it harder to determine if a process is stable or in control.
Limited Statistical Analysis: Run charts cannot differentiate between common cause and special cause variations, providing less precise diagnostic capability.
Basic Data Representation: They offer only a basic overview of data trends and may require additional tools for deeper process analysis.
Limited Predictive Power: Run charts are good for visualizing trends but cannot predict future outcomes or highlight the cause of variation.

Difference between Control Chart and Run Chart

The following table shows the key difference between the control chart and the run chart:

Parameter	Control Chart	Run Chart
Purpose	Monitors process stability and identifies variations.	Tracks data trends over time without control limits.
Control Limits	Includes upper and lower control limits based on statistical calculations.	Lacks control limits, focusing solely on data points.
Variations	Distinguishes between common cause and special cause variations.	Does not differentiate between types of variations.
Data Analysis	Provides a more detailed analysis of process behavior and performance.	Offers a simpler visualization of data trends.
Complexity	Requires knowledge of statistical concepts to interpret effectively.	Easier to understand and create, suitable for basic analysis.
Application	Used in quality control and process improvement initiatives.	Useful for identifying trends and shifts in various contexts.

When to Use Control Charts and When to Use Run Charts

Use control charts to monitor process stability and variability over time. They are ideal for situations where you want to identify and distinguish between common cause and special cause variations. Control charts are particularly beneficial in quality control and manufacturing environments, where maintaining consistent performance is critical.

Run charts are best used for tracking data trends and patterns over time without statistical limits. They are effective when you want a straightforward visual representation of a process’s performance. Run charts are suitable for preliminary data analysis, helping identify shifts or trends before more detailed investigation is required.

Summary

Run charts and control charts are valuable tools in project management. Examining either can reveal insights that reading multiple reports may not provide. While both charts are useful, control charts offer more detailed information than run charts. By effectively utilizing control charts, you can identify and eliminate errors in products and processes, allowing you to focus on continuous improvement.

Further Readings:

Reference:

This topic is important from a PMP exam point of view.

Fahad Usmani, PMP

I am Mohammad Fahad Usmani, B.E. PMP, PMI-RMP. I have been blogging on project management topics since 2011. To date, thousands of professionals have passed the PMP exam using my resources.

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