Statistical Process Control (SPC) is a methodology or procedure of quality control based on statistical methods. SPC measures and controls the quality of manufacturing processes, by using control charts, calculating statistical indicators, and taking improvement steps if necessary.

By other words, SPC:

The principles of SPC were developed by Dr. Walter Andrew Shewhart. He published his book "Economic Control of Quality of Manufactured Product" in 1931. This was the time, when quality control stepped to the next level, using statistical methods and analysis. Later on, the methodologies of SPC were improved further by Dr. William Edwards Deming.

As we can see in the title of Mr. Shewhart’s book, the intention of statistical control was to create an economically viable procedure, that keeps reliability of testing on a high level, but decreases cost.

Having SPC used in almost all industrial sectors, it has become the foundation stone of industrial quality control. Conducting SPC in a manufacturing environment has several major benefits, for example:

The general intention of SPC is to continuously measure the capability and conformance of our manufacturing processes (influenced by random effects), and to screen out uncontrolled external effects, that take negative impact on the process. To go deeper into SPC, we have to check some basic terms first:

Data set: the measured values of a given characteristic of random samples.

Control chart: used for containing, visualizing and evaluating the measured values of a given characteristic (e.g. voltage, current, distance, etc.), compared to the specification.

Stability of process: in statistical process control, stability means low variation. A stable process is precise with low variance, and the measured characteristics are in an acceptable range.

Variance / Variation (Ϭ²): is calculated by taking the differences between each value of the data set and the mean, squaring the differences and dividing the sum of the squares by the number of values in the data set.

Standard deviation (Ϭ - sigma): is equal to the square root of variance.

Common cause variation: we call those causes a "common cause" that bring natural, inherent and predictable variation into the process (noise), but only with a small influence. The variation caused by common causes is predictable (based on past experience), and called "controlled variation", or "chance cause" according to Dr. Shewhart. Common cause factors (X's) are the inputs with low impact.

Special cause variation: uncontrolled variation is caused by one or more non-random "special cause", which is more than a consistent noise, and influences our process with extraordinary weight. Special cause variation is unpredictable and chaotic. Dr. Shewhart named it as "assignable variation", while Dr. Deming called it "special cause". Special cause factors (X's) are those few inputs that have high impact on the process results (Y's).

Capability of process: the process is capable, if all requirements (based on specification) are met.

Upper Specification Limit (USL): the highest permissible value of a given characteristic.

Lower Specification Limit (LSL): the lowest permissible value of a given characteristic.

Tolerance range (T): the distance between USL and LSL (T = USL – LSL).

Upper control limit (UCL): the limit, which triggers action if exceeded upwards.

Lower control limit (LCL): the limit, which triggers action if exceeded downwards.

Spread: the quantity (amount) of variability in a given population.

Mean / Average: the arithmetic mean of the data set.

Median: the middle value of the data set.

Normal distribution: the probability density of normal distribution is as follows:

The most important points are to keep variance as low as possible, and to be in the middle of the tolerance range. On top of that, we have to note, that stability or capability by itself is not enough. The process needs to have both to produce proper characteristics consistently. Features of a stable and capable process:

Let’s take some examples (see chart):

Drunken bowman: he shots on the target randomly, the variation of his shots is high, and on top of that, the location of the shots is incorrect, as he barely hits the board. He is neither capable, nor stable.

Lucky drunken bowman: although the variation of his shots is still high, somehow he hits the board every time. He is capable, but not stable.

Skilled bowman with wrong glasses: his skill is superb, his hits are very near to each other, but because of the distorting lens in his glasses, the shots systematically go to the upper right side of the board. He is stable, but not capable.

Robin Hood: with his perfect skill, he is able to systematically find the middle of the target. His shots have very low variance, and the mean is exactly in the middle. He is both stable and capable.

One side of SPC is its development before starting a new manufacturing process (or changing an existing one), while the other side is its execution.

Development - the definition of an SPC procedure contains the following steps and elements:

Execution - a pre-defined statistical process control consist of the following steps (statistical software perform some of these points automatically):

Measuring, registering and comparing the given characteristic of random samples is the most simple type of SPC, while more complex methods exist, such as: frequent calculation and comparison of means, spreads, variances, process capability values (C_p, C_pk) and performing statistical tests (e.g. F-test, T-test, Z-test, Chi-Square, etc.).

Before starting and sustaining a proper SPC, the measurement method must be defined. There is no SPC (and capability study) without a capable measurement. SPC is in very close conjunction with Process Capability (C_p, C_pk) and Measurement System Analysis (MSA).

Use systematic process capability studies (as SPC) to continuously verify your process. It can save you from tons of scrap / rework / customer claim.

During the process development phase in the APQP framework, the control / testing concept of the process must be determined. In various cases, such as safety relevance, or governmental relevance, the process control must be performed with 100% testing instead of using random sampling and SPC. SPC can never reach the confidence level of 100% testing.

Implement and use statistical software that automatically calculates process indicators, and alerts you in case of negative trends and discrepancies are detected. It not only helps quality control, but replace the necessity of manual calculations, that take time and money.

If you cannot measure an important characteristic directly, you have to find another measurable characteristic that is in close connection (relationship) to the original characteristic.

When the SPC concept of a given process is being developed, choosing a proper sampling interval (the time between two samples) is important. The lower your time interval is, the higher your chance is to detect anomalies (and to reduce waste, scrap, rework).

Statistical Process Control (SPC) is a methodology or procedure of quality control based on statistical methods