Statistical Process Control - SPC

The fundamentals of Statistical Process Control (though that was not what it was called at the time) and the associated tool of the Control Chart were developed by Dr Walter A Shewhart in the mid-1920’s. His reasoning and approach were practical, sensible and positive. In order to be so, he deliberately avoided overdoing mathematical detail. In later years, significant mathematical attributes were assigned to Shewharts thinking with the result that this work became better known than the pioneering application that Shewhart had worked up.

The crucial difference between Shewhart’s work and the inappropriately-perceived purpose of SPC that emerged, that typically involved mathematical distortion and tampering, is that his developments were in context, and with the purpose, of process improvement, as opposed to mere process monitoring. I.e. they could be described as helping to get the process into that “satisfactory state” which one might then be content to monitor. Note, however, that a true adherent to Deming’s principles would probably never reach that situation, following instead the philosophy and aim of continuous improvement.

Explanation and Illustration:

What do “in control” and “out of control” mean?

Suppose that we are recording, regularly over time, some measurements from a process. The measurements might be lengths of steel rods after a cutting operation, or the lengths of time to service some machine, or your weight as measured on the bathroom scales each morning, or the percentage of defective (or non-conforming) items in batches from a supplier, or measurements of Intelligence Quotient, or times between sending out invoices and receiving the payment etc., etc..

A series of line graphs or histograms can be drawn to represent the data as a statistical distribution. It is a picture of the behaviour of the variation in the measurement that is being recorded. If a process is deemed as “stable” then the concept is that it is in statistical control. The point is that, if an outside influence impacts upon the process, (e.g., a machine setting is altered or you go on a diet etc.) then, in effect, the data are of course no longer all coming from the same source. It therefore follows that no single distribution could possibly serve to represent them. If the distribution changes unpredictably over time, then the process is said to be out of control. As a scientist, Shewhart knew that there is always variation in anything that can be measured. The variation may be large, or it may be imperceptibly small, or it may be between these two extremes; but it is always there.

What inspired Shewhart’s development of the statistical control of processes was his observation that the variability which he saw in manufacturing processes often differed in behaviour from that which he saw in so-called “natural” processes – by which he seems to have meant such phenomena as molecular motions.

Wheeler and Chambers combine and summarise these two important aspects as follows:

In particular, Shewhart often found controlled (stable variation in natural processes and uncontrolled (unstable variation in manufacturing processes. The difference is clear. In the former case, we know what to expect in terms of variability; in the latter we do not. We may predict the future, with some chance of success, in the former case; we cannot do so in the latter.

Why is "in control" and "out of control" important?

Shewhart gave us a technical tool to help identify the two types of variation: the control chart - (see Control Charts as the annex to this topic).

What is important is the understanding of why correct identification of the two types of variation is so vital. There are at least three prime reasons.

First, when there are irregular large deviations in output because of unexplained special causes, it is impossible to evaluate the effects of changes in design, training, purchasing policy etc. which might be made to the system by management. The capability of a process is unknown, whilst the process is out of statistical control.

Second, when special causes have been eliminated, so that only common causes remain, improvement then has to depend upon management action. For such variation is due to the way that the processes and systems have been designed and built – and only management has authority and responsibility to work on systems and processes. As Myron Tribus, Director of the American Quality and Productivity Institute, has often said:

Finally, something of great importance, but which has to be unknown to managers who do not have this understanding of variation, is that by (in effect) misinterpreting either type of cause as the other, and acting accordingly, they not only fail to improve matters – they literally make things worse.

These implications, and consequently the whole concept of the statistical control of processes, had a profound and lasting impact on Dr Deming. Many aspects of his management philosophy emanate from considerations based on just these notions.

So why SPC?

The plain fact is that when a process is within statistical control, its output is indiscernible from random variation: the kind of variation which one gets from tossing coins, throwing dice, or shuffling cards. Whether or not the process is in control, the numbers will go up, the numbers will go down; indeed, occasionally we shall get a number that is the highest or the lowest for some time. Of course we shall: how could it be otherwise? The question is - do these individual occurrences mean anything important? When the process is out of control, the answer will sometimes be yes. When the process is in control, the answer is no.

So the main response to the question Why SPC? is therefore this: It guides us to the type of action that is appropriate for trying to improve the functioning of a process. Should we react to individual results from the process (which is only sensible, if such a result is signalled by a control chart as being due to a special cause) or should we instead be going for change to the process itself, guided by cumulated evidence from its output (which is only sensible if the process is in control)?

Process improvement needs to be carried out in three chronological phases:

Control charts have an important part to play in each of these three Phases. Points beyond control limits (plus other agreed signals) indicate when special causes should be searched for. The control chart is therefore the prime diagnostic tool in Phase 1. All sorts of statistical tools can aid Phase 2, including Pareto Analysis, Ishikawa Diagrams, flow-charts of various kinds, etc., and recalculated control limits will indicate what kind of success (particularly in terms of reduced variation) has been achieved. The control chart will also, as always, show when any further special causes should be attended to. Advocates of the British/European approach will consider themselves familiar with the use of the control chart in Phase 3. However, it is strongly recommended that they consider the use of a Japanese Control Chart (q.v.) in order to see how much more can be done even in this Phase than is normal practice in this part of the world.

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