Statistical process control is set of methods for reducing variances and inefficiencies in a system. It relies on statistics to eliminate human error from the quality-control process, measuring everything in raw numbers rather than based on human response. So, in light of the fact that statistical process control relies almost exclusively on numbers, it is most useful in improving processes that are designed based on fairly rigid protocols.

Applications of Statistcal Process Control

In manufacturing, for instance, one of the goals is often to develop a process for creating items that are infinitely replicable with as little variance as possible. There are many practical reasons for this. For one, most companies want their customers to feel secure in their expectations. For another thing, staying within certain statistical ranges is often required of products that are regulated or where precisely calibrated operation is essential.

For example, statistical process control is useful for ensuring that mass-produced foods stay in line with the nutritional information printed on the label. Of course, not having the actual content of the food in line with the label can lead to serious consequences if regulators find out. Another example is in the production of medical equipment, where manufacturing variances can mean the difference between life and death.

But statistical process control can be useful outside manufacturing. Wherever there are rigidly controlled processes with the potential for inefficiencies, bottle necks, and small deteriorations along a complex network of interactive parts, statistical process control is an efficient way to cut through the confusion and drill down to the source of the problem. Where the statistics are off, that’s liable to be where the source of the problem lies.

Using Statistical Process Control

Statistical process control is not any single method but rather a large group of methods that can be applied in any number of ways to an unlimited variety of processes. In general, however, establishing a system of statistical process control involves first putting together a process that runs as well as possible, taking statistics based on the freshly created process, and then continuously monitoring the statistics. When variances become apparent, the sources of the variances are usually easily traced to unusual points in the data.

Companies have come up with all sorts of sophisticated ways to monitor statistics. In the 21st century, the trajectory is toward digital statistical process control systems with as much automation as possible. In some cases, it is even possible to automate the diagnosis of the problem. But for the most part, the technology is still only sophisticated enough to call attention to anomalies that may lead to variances, and then it’s left to people to diagnose the problem and take the appropriate steps to reduce the variances.

Whether automated or completely human controlled, most SPC systems rely on data maps and control charts that present the data in an organized way. In most cases, there are preset ranges within which each data point must fall, and the job of the person monitoring the charts is to watch for data points out of the preset ranges. Ultimately, this is what makes SPC a useful system; while the system can be difficult to set up, it makes checking for problems in the process almost too easy.