Statistical process control

Will it get the processes "in control"?

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Statistical process control refers to the use of statistical methods to analyze and alter processes for the ultimate purpose of ensuring that a good or service meets its proclaimed (or industry mandated) standards – which may relate to materials, performance, reliability, time, or any quantifiable, objective and measurable characteristic. Specifically, statistical process control “involves establishing standards, monitoring standards, making measurements and taking corrective action as a product or service is being produced. Samples of process outputs are examined; if they are within acceptable limits, the process is permitted to continue. If they fall outside certain specific ranges, the process is stopped and, typically the assignable cause is located and removed” (Render & Stair, 2006, p. 683)

The development and use of statistics and statistical theories about distributions and how they vary has become the cornerstone of process improvement. Statistical process control allows the user to continuously monitor, analyze, and control the process.
SPC is based on the understanding of variation and how it affects the output of any process. Variation is the amount of deviation from a design nominal value. Not every product that is produced will exactly match its design nominal values. That's why we have tolerances on the nominal values to judge whether a product is acceptable or not. But the closer we are to the nominal value the better the product is. Control charts are one SPC tool that enables us to monitor and control process variation.

SPC will not “get the process in control”, but rather, is used to evaluate the output of a process to determine its acceptability. When applied appropriately, it can serve as a management tool to assess the degree to which a process is in control and/or the need to change and refine aspects of the process in order to achieve more desirable results.

All processes have some inherent random variation that is typically acceptable in a product or process. Statistical process control refers to a set of methods for detecting the presence of an unacceptable or non-random variation (i.e. an assignable cause). Even a process that is functioning appropriately will not yield output that conforms exactly to a standard, simply due to the natural and random variation inherent in all processes. A certain amount of variation is inevitable. The main task of statistical process control is to distinguish random from non-random variability, since non-random variability renders a process out of control.

When sample statistics are clustered around the central value within the control limits, a process is said to be “in control”. When a process is “in control” 99.7% of the sample averages fall within +/- 3 standard errors of the chart centerline. If sample averages fall outside the control limits, some assignable cause is most likely responsible and analysis and corrective action should be pursued.

Reference

Render, B. & Stair, R. (2006). Quantitative analysis for management, 9th ed., Upper Saddle River, NJ: Prentice Hall Publishers.

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