Statistical process control

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Statistical process control
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Statistical Process Control is a set of techniques and statistical methods used to assess the stability of the process. The purpose of SPC is to prevent non-conformity by detecting and early signalling of interference in the process.

SPC is a response to the ineffectiveness of traditional quality inspection. Instead of controlling final product quality inspectors or the employees themselves check produces parts. They do not wait until defective parts appear. If produced element is dangerously close to acceptable limits (tolerance), the immediately take the necessary actions. These actions may include: informing superiors, tuning machines, replacement of worn parts of the machine, etc.

SPC is geared to continuous improvement. It is primarily preventive. Analysis of the process not only provides information on occurring deviations, but also helps to understand the cause of process variation. Thanks to systematic monitoring, organization can minimize losses by removing identified defects and errors. At the same time managers, based on information about the problems, can design processes so as to prevent their next occurrence of the error using e.g.: Poka yoke, design quality or FMEA [1].

The statistical control

One method of quality inspection is a statistical control, often called sampling. Only samples of product are checked. This type of inspection is used for technical and economic reasons. It is not always possible to measure all the units of production batch. Some forms of control are associated with the destruction of the product.

Depending on the size and frequency of sampling, as well as how feedback is used in the manufacturing process, we can distinguish two types of statistical control:

  • Statistical quality control - is passive. It aims to determine whether a given batch of products from which the sample was taken may be accepted.
  • Statistical process control - has active character. The results are used not only to evaluate the products but also the whole process. It is about improving the process [2].

SPC Tools

The basic tools used in statistical process control are:

Purpose of using SPC

The purpose of using these tools is to identify the causes of problems. The causes are divided into:

  • Random,
  • Non-random.

Random causes (also called systemic, natural) are naturally associated with the process. They are common, and their effects are relatively small compared to the causes of non-random. These factors are difficult to identify and eliminate, as the only way out is to change the production system. Examples in this case may be: outdated equipment, inadequate lighting, poor quality material.

Non-random causes occur irregularly, and their effects are usually significant. Typically, they are easy to identify and remove. Examples of non-random causes might be: employee's error, broken or poorly programmed machine. The elimination of these reasons is the basic prerequisite for the achievement of control over the process [3].

More on this in an article on control chart.

Tools for statistical process control

  • Shewhart Control Charts: Shewhart Control Charts are used to detect process changes. They are used to monitor the differences of successive measurements of a process. Shewhart Control Charts detect changes in the mean, variance and range of the process.
  • Acceptance Sampling: Acceptance Sampling is used to inspect a product or process in order to determine if it meets the required specifications. It is used to identify whether the process is capable of producing a suitable product.
  • Design of Experiments: Design of Experiments is a statistical technique used to determine which input variables have an effect on the output of a process. It is used to determine which variables are most influential, and then adjust the process to ensure that the output meets the desired specifications.
  • Process Capability Analysis: Process Capability Analysis is used to evaluate the capability of a process to produce products that meet the required specifications. It involves measuring the variability of the process and comparing it to the variability of the specification limits.

Advantages of Statistical process control

Statistical Process Control (SPC) provides a wide range of advantages to quality control. These include:

  • Improved process performance: SPC helps to identify and reduce process variation, which can result in improved product quality and fewer defects.
  • Increased efficiency: SPC reduces the amount of time and effort required to monitor and control a process.
  • Cost savings: By reducing process variation and improving process performance, SPC can lead to significant cost savings.
  • Enhanced customer satisfaction: SPC helps to ensure product consistency, which can lead to increased customer satisfaction.
  • Improved decision-making: SPC can provide valuable data and insights which can help in making better decisions.
  • Identification of root causes: SPC can help to identify the root causes of process variation, which can help in making corrective actions.

Limitations of Statistical process control

Statistical Process Control has several limitations, including:

  • It can only detect stable and predictable processes and may not be effective for detecting sudden changes in the process.
  • It cannot provide information on why the process is unstable or why changes are occurring.
  • It does not always detect changes in the process quickly and may not be able to identify all sources of variation in the process.
  • SPC requires data to be collected over an extended period of time, which can be costly.
  • It does not always detect small changes that can have an impact on the quality of the product.
  • It is not suitable for processes with high variability.

Other approaches related to Statistical process control

  • Design of Experiments (DOE): DOE is a method used to analyze the effects of different factors on the output of a process. It uses a combination of statistical and engineering principles to identify the most important factors that influence the process and how they interact with each other.
  • Six Sigma: Six Sigma is a process improvement methodology that uses data-driven tools and techniques to identify and eliminate process defects. It focuses on reducing variation in the process, increasing customer satisfaction and improving operational efficiency.
  • Lean Manufacturing: Lean Manufacturing is an approach to manufacturing that focuses on eliminating waste and increasing efficiency. It uses a variety of tools, such as Kaizen, Kanban and 5S, to identify and eliminate waste in the process.
  • Quality Function Deployment: Quality Function Deployment (QFD) is a tool used to systematically identify customer requirements and prioritize them in terms of importance. It helps to ensure that the product meets customer expectations.

In summary, the other approaches related to Statistical Process Control are Design of Experiments, Six Sigma, Lean Manufacturing and Quality Function Deployment. These approaches are used to improve the quality of processes, reduce cost and increase efficiency.


  1. Bank J. Total Quality Management, Publishing Gebethner and Ska, Warsaw 1996
  2. Hamrol A., Mantur W., Quality Management. Theory and Practice ', PWN, Warsaw 2002
  3. Dahlgaard JJ Gopal K Kanji K. Kristensen, Fundamentals of quality management, PWN, Warsaw 2000

Author: Slawomir Wawak, Irena Śliwińska