What Is a Control Chart?

Control charts, invented by Dr. Walter Shewhart, are a statistical process control tool used to determine whether a manufacturing or business process is under control. When the underlying form of the process distributions is known, traditional control charts are mostly designed to monitor process parameters. In the twenty-first century, however, more advanced techniques are available that allow incoming data streaming to be monitored even without knowledge of the underlying process distributions. In other terms, they are routinely used to monitor the quality of a process.

Elements of a Control Chart

Here are the main elements of a control chart:

Advantages of Using a Control Chart

In addition to providing actionable information for correcting issues as they happen in real-time, here are some of the other advantages that a control chart can provide:

Real-time control charts help reduce the margin of error. Because control charts show what’s going on in a manufacturing line in real-time, they allow operators to detect and correct problems before they cause more serious issues in processes and products. This significantly reduces the need for product rework or additional product expenditures to fix a product offering. Control charts act as an early warning detection system in real-time monitoring software, alerting operators that it is time to make a change. This way, they don’t finish the entire run only to discover that they should have made adjustments three hours earlier and now have to bear the costs associated with this issue.Data visibility prevents over-tampering. It is equally important to understand when your process is running smoothly as it is to understand when something is wrong. In particular, when attempting to detect whether a problem exists, operators can frequently over-tamper with a properly running process, resulting in more variances. After analyzing a control chart, operators must decide whether to do something (adjust a process behavior) or do nothing (let the process run as is). From time to time, learning that they can do nothing often deters operators from tampering with their processes.Control charts provide operational insight for critical stakeholders. Control charts provide a wealth of information to all key stakeholders involved in the creation of a manufactured product, from operators and engineers to managers and executives. Control charts can bring together data in ways that provide actionable insights into whether a process needs to be amended when they draw SPC (statistical process control)-based data from a centralized, unified data repository. For example, an engineer can use aggregated data to improve a process. More advanced box and whisker and Pareto charts provide managers with a comprehensive view of the entire plant floor—or even across multiple plants. Control charts can fill a critical need for a variety of groups responsible for manufacturing quality control by allowing them to make decisions based on concrete numbers rather than assumptions.Data accessibility and visibility levels the playing field. Operators, engineers, and managers may evolve their skills and methods based on their opinions and instincts if they do not have access to data. Despite the fact that many experienced manufacturing workers have good instincts, control charts will most likely validate what they have always known to be true. Control charts not only level the playing field but also validate what people already know and dispel process myths. When everyone in the organization has access to the same data and is on the same page, decision-making improves. Manufacturing intelligence provided by process control software will set you apart from your competitors and help you overcome manufacturing challenges.

Types of Process Variations

Understanding the types of process variations is critical to monitoring the stability of a process. It is also critical before even making a control chart. Here are the types of process variations:

Types of Control Charts

Here are some of the various types of control charts:

How To Implement and Use Control Charts

Here are the steps needed to effectively implement and use control charts:

  • 1. Select a Measurement Method and Verify It

    Control charts allow for the Analysis Of Data and trends over time, so decide what data you will collect first. In control charts, two types of data are used: variable and attribute data. Variable data is the result of continuous-scale measurements. It includes elements such as time, weight, temperature, and size. Keep in mind that variable data should also be unbiased. On the other hand, attribute data includes arbitrary distinctions such as pass/fail and good/bad.

    After selecting the measurement system, verify it. One important step that companies often overlook is validating that the data being input into the control chart is accurate. If you input bad data, you’ll make flawed decisions and potentially make processes worse. It is smart to conduct periodic spot checks on your measurement system.

  • 2. Begin Data Collection and Determine Where it Will be Stored

    Create a strategy for gathering data on a regular basis. The people who will collect the data should be trained properly in how to measure and chart the results. The best approach someone can use for storing the collected data is to use an improvement platform with control chart capabilities. This allows all stakeholders to have access to the information and create a history of the process results over time.

  • 3. Document the Reaction Plan

    When you have your control chart and some data, the next step is to have a defined reaction plan that outlines what will happen if a process becomes out of control or nearly out of control. In this process, flow charts and other types of decision trees can prove to be useful tools.

  • 4. Calculate Limits and Assess the Process

    Once you’ve begun collecting data, you can compute the upper and lower control limits, which are typically three standard deviations from the mean. After that, determine whether or not the process is out of control. When an out-of-control condition occurs, it is most likely the result of a special cause variation that is caused by another process or event. The next goal is to determine the underlying Cause Of The Variation. A common cause variation occurs when the chart is in control. There are typically many contributors to common cause variation such as the various factors that are present in the system on a daily basis. When we have common cause variation, we can improve the system, but it is usually a less reactive process where asking “what went wrong last period?” is a less useful question.

  • 5. Implement Actions to Improve the Process

    Once the true cause of the variation is identified, corrective action can then be taken to resolve the problem and improve the process so that it does not happen again. It is not a complicated process overall, but proper care must be observed so that the implementation is effective.


What are the types of quantitative data?

The two types of quantitative data are discrete and continuous. Discrete data refers to quantitative data that can only have specific numerical values. These values are fixed and cannot be subdivided. Tally charts, bar charts, and pie charts are commonly used to represent it. Continuous data, on the other hand, refers to quantitative data that can be infinitely divided into smaller parts. This type of quantitative data can be represented on a scale, such as the length of a piece of string in centimeters or the temperature in degrees Celsius. Continuous data, in essence, can take any value; it is not restricted to fixed values.

What are the types of errors in a control chart?

There are two types of errors in a control chart. Type I or Alpha errors occur whenever a point falls outside the control limits despite the absence of a special cause. As a result, there is a hunt for special causes, and things are frequently tweaked. Tampering typically distorts a stable process while also wasting time and energy. Type II or Beta errors occur when you miss a special cause because the chart isn’t sensitive enough to detect it. In this instance, you will be unaware of the problem and thus unable to solve it.

Are there downsides to using control charts?

Yes, there can also be downsides whenever you use control charts. For instance, control charts can be used incorrectly if the wrong sample group or process parameter/output is selected. If a non-homogeneous sample is used, for example, the results will be skewed. Another oversight can be that incorrect decisions can be made without an understanding of the variation (error) associated with your measurement system.

Control charts are one of the many statistical tools that can be used in aiding continuous process improvement. In addition to that, knowing which type of control chart to use can assure the accurate monitoring of process stability. It eliminates errors and wasted efforts, giving time to genuine opportunities for meaningful improvement. Also, gaining real-time insights into your manufacturing quality control processes through a control chart is important as it can be just the thing to prevent you from incurring unexpected costs that result from process breakdowns and product quality issues. In this article, examples of effective control charts can be obtained for you to use as a reference should you need to make one.