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Statistical process control - a way to predict quality

Statistical Process Control (SPC for short) is a set of statistical methods and techniques for assessing process stability.

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What is statistical process control?

Statistical Process Control (SPC for short) is a set of statistical methods and techniques for assessing process stability. The goal of SPC is to reduce the number of process changes and prevent nonconformities from occurring. With SPC, it is possible to better control the process and improve its quality.

Statistical process control is an answer to the ineffectiveness of traditional control methods, which are mainly based on controlling the final product. According to the idea of SPC, control takes place during the process by monitoring tolerance limits. And when these limits are exceeded specialists immediately take appropriate action. Such actions can be, for example, replacement of a worn part, replacement of a knife or replenishment of appropriate fluids or oils. Thanks to this approach, quality costs are considerably lower than in the case of post facto measures.

Statistical process control tools

As already mentioned – SPC is a collection of methods and tools. Among those most commonly used we can include:

  • Control Sheets – consist of a graphical representation of the variability of a manufacturing or service process.
  • Pareto diagram – is a graphical way of presenting data on a bar chart in a descending manner.
  • Histogram – is a type of graph that shows numerical data in order.
  • Correlation Diagram – is a way of arranging relationships between dependent variables on a dot plot.
  • Process flow diagram – is a graphical representation of results from a process (Y) in relation to a time line (X).

You can learn more about these and other statistical tools by attending Six Sigma Green Belt training.

Causes of process variability

Processes that are included in a statistical study are subject to certain factors that cause their variability and scatter of values. These factors can be divided into:

  • Special factors – these can cause large changes in the process and recur, but the reasons for their occurrence are very specific. For example, they can be: machine failures, defective batches of raw materials, changes in current voltage.
  • Natural factors – they usually cause small changes in the process, and their causes can be different. For example, these may be: temperature changes on the shop floor, changes in material over time within the tolerance limits, changes in settings due to inaccuracy of sensors on the machine.

To start using SPC actively, you must first get rid of special factors. Their presence will make process control much more difficult. When we have only natural factors left we can start to optimize the process with SPC.

Statistical Process Control - factors

The benefits of using statistical process control:

  • Reduce rework and waste (through a preventive approach and early response to deviations),
  • Reduced risk of complaints and market returns,
  • Improved process efficiency and operational performance,
  • Reduce quality costs by not requiring 100% inspection,
  • Reduction of the technical production cost,
  • Possibility of extending the knowledge about processes functioning thanks to statistical research,
  • Ability to generate professional reports and analysis, based on real data.

Link to the product:

SPC and MSA

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