Statistical process control is a set of strategies used for discovering and correcting inefficiencies in processes. Although it has most notably been applied to manufacturing, experts in the field have shown that statistical process control can be applied to virtually any process that needs improvement. The ideas behind statistical process control are still relatively new, but adherents to its strategies firmly believe that it can improve not just business processes but also governmental, organizational, and individual processes.
What is statistical process control?
Statistical process control is a way of accounting for the virtually infinite variables that can go into the success or failure of any process. For example, consider a company that mass produces large numbers of books. If some of the books begin coming out with imperfections such as bad binding, stuck pages, inconsistent text appearance, or poor alignment, it may be difficult to pinpoint exactly where in the process the error is occurring. There may be many opinions among the employees and management at the company, but these are based on subjective perception and thus inherently limited.
Meanwhile, there may be some “flaws” in the book printing process that, to some people’s perspective, aren’t flaws at all. For instance, one person might think the ink is too light while another might think it’s just right. Such qualitative evaluations are not without merit, but they are generally not the best tools for analysis when it comes to complex processes such as book printing.
Statistical process control is the opposite of qualitative evaluation. It measures success based on actual numbers that are set beforehand. In the book printing process, statistical process control would establish measurable ways to quantify the success of the finished product. But another key to statistical process control is that it does not just look at the end result. While measuring things like margin size and print shade can tell us whether a book has been produced according to specs, this does not necessarily tell us where exactly in the process the inefficiencies are occurring.
A successful statistical process control model assigns ideal numbers to as many aspects of the process as possible. In the book printing business, for instance, there may be an ideal manufacturing room temperature that leads to an optimal appearance of the ink on the page, or perhaps there are optimal speeds at which the mechanical equipment must run in order to achieve the best results. Finding this data is one of the challenges of statistical process control, but once the numbers are set, this approach is extremely useful for locating inefficiencies in the system.
Philosophically speaking, the main purpose behind statistical process control is to deal with chaos—and indeed it is no coincidence that this method of monitoring processes came about at roughly the same time that chaos theory was in development. In any moderately complex system, there are simply too many factors for a human mind to keep track of, and the way all these variables interact with another is nearly impossible to predict with any exactness.
That’s why statistical process control typically deals with ranges rather than exact figures. In the book manufacturing plant, a statistical process control model would not set exact data points that need to be achieved because a set of exact points is impossible to reach even in the most well-run system. Even if everything at the manufacturing plant is set up to run perfectly, unexpected inefficiencies will always find their way into the system. Because inefficiencies often have not one single cause but many causes feeding off each other, statistical analysis helps managers quickly get to the bottom of what aspect of the system is off.
Advantages and disadvantages
The main advantages of statistical process control have already been outlined. It takes the human element out of the process and allows for a level of objectivity that cannot be achieved through other methods of evaluating process. When there is data available to cover virtually every aspect of the process as well as good information about the ideal ranges for all data points, then it is easy to keep the process running smoothly.
The main disadvantage with evaluation using statistical process control is that it can become rigid and inflexible. When everything about a process is boiled down to numbers, there is a tendency to trust the numbers a little too much. What is needed, if the process is to work as it should while remaining flexible, is an individual or team to continually evaluate the numbers. Returning to the book printing example—there should be someone within the printing company who monitors the numbers as well as the results (i.e., the finished books) and works to locate the problem whenever there are flaws. This same person would be in charge of implementing changes within the ideal data ranges when the process is adjusted.
So, in the long run, this need for human monitoring over the statistical process control can be perceived as a disadvantage in that it makes the system more complex than it otherwise would be and adds a layer of bureaucracy. But of course, in this case the layer of bureaucracy is one that monitors quality and actually encourages flexibility rather than hinders it. That’s the way it’s supposed to work, anyway, but the human element is only as effective as the humans running it.
When properly set up and well run, a statistical process control system can be amazingly powerful in making sure a system runs smoothly. When something in the system is off, checking for inefficiencies can be almost instantaneous, which makes it vastly more timely than human-run inspection processes. And when the data does not seem to shed light on the cause of the problem, this signals to the people running the system that there are factors that have not yet been accounted for. Of course, finding the unaccounted factors can pose significant challenges, but it usually comes down to a process of elimination—and this is one aspect of statistical process control where human input can actually be invaluable.
Nonmanufacturing applications
We’ve already used book printing as an example of how statistical process control can help make a system run more efficiently, but manufacturing is by no means the only potential application for these strategies. The next obvious application is in the service industry, where statistical models can help managers identify inefficiencies in the production process.
Of course, the idea of using statistical process control in the service industry does raise some significant questions, particularly with regard to human subjectivity. Since the value of products and services is always qualitatively determined, one dissatisfied customer or a small number of dissatisfied customers can lead to a mistaken impression that a process is flawed when it in fact works exactly as its creators intended.
This problem can be avoided, however, by upping the data sample. A few dissatisfied customers may indicate misperceptions, incorrect expectations, or mere crankiness on the part of the customers. But a thousand dissatisfied customers in a data pool of a few thousand customers indicates that something is indeed wrong with the process and that the data needs to be evaluated. That’s when the company can begin looking at their data points from within the process itself to see if any of the data is outside its ideal range.
In cases like these, human subjectivity can actually give useful information about the process and its flaws. For this reason, many companies have implemented sophisticated customer opinion surveys to determine the exact nature of any customer dissatisfaction. This only goes so far, however, as customers are of course not aware of the process itself and do not always know exactly why they are dissatisfied. In the end, the sources of the customer dissatisfaction must be located within the process by those who are familiar with it and have access to the data.
Outside business, statistical process control can also be used in governmental applications, though the rigidity of governments has so far kept such new methods from being implemented widely. In any event, one can imagine how a well-designed statistical process control system could be effective in helping governments reduce waste and inefficiency. In an age where austerity is a buzzword throughout the world, governments could greatly benefit from analytical models that help their systems run better.
Personal applications
Although statistical process control was conceived with large-scale systems in mind, its fundamental principles are applicable to systems at all levels of scale. All that’s needed is a large enough sample of data to minimize statistical aberrations. So, for example, if one wants to use statistical process control to regulate one’s personal health, it would be important to think in the long term. Otherwise, the statistics might lead to supposed solutions that are actually unhealthy.
In this scenario, the individual might create a health plan based on ideal ranges of various data points. This could be done based on current recommendations from health authorities. One could investigate how much of each significant vitamin and nutrient is needed for the body to run smoothly, and this data would go along with other points such as sleep time, exercise time, sexual activity, drug and alcohol use, relaxation time, and so on.
This model would of course have to be flexible. Once the ideal ranges are set and implemented over a period of several weeks or months, the individual would keep track of all the data points daily and after several weeks or months would take stock and evaluate how well the system is working. If he or she has been doing everything within the ranges put in place at the outset, then any health issues would need to be addressed by adjusting levels.
The problem with personal applications of statistical process control is that they can take too long. In the personal health system we’ve been outlining, the person would have to go several months before a reasonable amount of data could be accumulated, and then each stage of adjustment would require more months. For an individual, this requires an incredible level of commitment that few would have the patience for.
Meanwhile, the subjectivity issue is also at play here. For personal applications of statistical process control, emotions will always cloud the picture. With enough data, however, and an ability to view things as quantitatively as possible, subjectivity can be overcome. Then the only question that remains is whether this type of system is worth all the work. For anyone skeptical of popular claims about health, using statistical process control may be appealing because it lets one study one’s own body and the health effects of various things. However, one probably requires skill with statistics and the will to stick with the system over long periods.
As anyone can see, statistical process control will probably never catch on for personal use. Not everyone has a knack for statistics, and many people are more results-oriented than process-oriented and do not possess the big-picture view of how the two are intertwined. Yet for anyone for whom statistical process control makes sense, the possibilities are endless.
