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.

How Statistical Process Control Works

Imagine, for example, a company that manufactures frozen burritos and ships them to grocery stores across the U.S. The company is required to print a detailed list of all the ingredients contained in the burritos, and they also must provide accurate information regarding the nutritional value of each item. Regulations allow for a small amount of variance from item to item, but each burrito must nevertheless fall into a very narrow range in terms of ingredients and nutritional value, not to mention other categories like size and weight. How does the company go about ensuring that every single burrito that is shipped out meets the required specs?

It’s rather simple, actually. The company has data points associated with every step of the manufacturing process, and they have required ranges for every data point. The finished burritos are regularly monitored for variances, and when some items begin coming out of the process outside the specs required by regulation, the quality control team examines the stats and searches for points that are outside the required ranges. When a problem is found in the data, it may relate to an aging piece of equipment, for example, or an improperly trained worker. In any case, locating the problem in the statistics leads the quality controllers straight to the source of the issue.

The Benefits of Statistical Process Control

From the above example, one can already begin to see how incredibly useful SPC can be. Without such a system in place, correcting variances in products can be an exhausting process requiring far too many resources. Imagine if that company lacked a quality control system and suddenly discovered that the ratio of ingredients inside their burritos was off in a few respects. The only way to locate the problem would be to engage in a long survey of every step of the process, every worker assigned to that process, and every ingredient that goes into it. In such scenarios, any variance can mean a huge loss of productivity and hence of profits.

In other words, statistical process control is far more efficient than quality control practices that rely completely on human observation. Plus, it also removes that flawed human element that is so prone to getting things wrong. If that food company were to take a guess at what was off in their process and get it wrong, it could lead to serious consequences. Using statistics greatly diminishes the potential for human error and thus protects companies against fines, lawsuits, lost business, and so on.

Controlling The Human Element

There are many reasons why statistical analysis is useful for evaluating processes, but the biggest factor in favor of statistical process control is its objectivity. While other evaluation methods involve human judgment and hence are subject to flaws of subjectivity and simple human error, statistical process control looks at the actual results with no evaluation and no human judgment.

The underlying assumption of statistical process control is that by quantifying elements of processes and examining them in raw numbers, the truth about where the processes work and don’t work can be reached. But of course, it’s important to keep in mind that not everything can be measured statistically. For instance, statistical process control can be used to make sure every item that comes off an assembly line falls within the range of certain specs, but what it cannot so easily do is quantify customer satisfaction with the items. Subjective human reactions to things are inherently unreliable as data, especially since people often don’t know exactly why they do or do not like something.

But with that being said, there are ways that statistical process control can be applied to human reactions to things—namely, by taking a large sample size. So, for example, if you poll five people about why they do or do not like a product, you’re liable to get a range of answers, some unexpected, and you probably won’t get any actionable information out of it. But if you analyze the behavior of 5,000 people with regard to a product, this gives you very clear data about human tendencies.

Protecting Against Human Error

While human reactions to a problem don’t necessarily apply directly to the process under analysis, what they can do is point toward parts of the process that could be improved. The failure of a product can stem from a variety of sources. Sometimes it boils down to poor planning or design, sometimes it relates to bad craftsmanship or shoddy materials, and sometimes it’s from lack of quality control. Getting a sense of the human response to a product should point to which of these is most relevant.

Setting aside how people respond to products, the essential benefit of statistical process control is that it is useful in virtually any type of manufacturing that follows a fairly rigid process. There can be some difficulty in pinpointing where in the process to take the statistical data, but once this is set, it becomes an incredibly efficient way to locate points in the process that have deteriorated or where things could be improved. If the data is read correctly, then human judgment doesn’t even enter into it.

For anyone new to statistical process control, it may be difficult to imagine exactly how these methods can be put into action, so let’s look at a few real-world applications in which SPC is now being used.

Food manufacturing: In most developed countries, there are very strict regulations governing how food can be processed and marketed. In most places, makers of food products are required to print full ingredient lists along with detailed information about the nutritional value of the food. In order to stay true to what’s printed on the label, companies must make sure that the products they make have very little variance from item to item.

That’s where statistical process control comes in. When items begin coming out of the manufacturing process with flaws or variances, the managers simply go to the statistics and look for data points that are out of line with expectations. More often than not, finding the source of the problem is as simple as locating the problematic statistic, tracing its cause, and making small, precise adjustments to the process.

Medical supplies: Statistical process control has proved immensely useful in fields where life and death depend on items being manufactured to precision. Before statistical process control, the manufacturing of medical supplies required huge quality control teams to monitor all stages of the process and test every piece of equipment as it came out of the manufacturing process. Today, while there is still extensive quality control that must be done, statistical process control has made monitoring for and eliminating variances far more efficient. What was once done by a full department in a company can now be done by one or a few individuals.

Vehicle manufacturing: As with medical supplies, vehicles such as cars, trucks, and airplanes, must be manufactured with every element within very specific ranges, or else the safety of the vehicles’ operators and passengers is put at risk. Since Henry Ford pioneered many aspects of the modern assembly line in the early 20th century, the automobile industry has always been at the cutting edge of the world’s manufacturing processes, and the industry’s use of SPC keeps it at the cutting edge even to this day.

The average consumer of motor vehicles doesn’t realize just how complex today’s cars are. A typical vehicle can have upwards of 10,000 parts, and for aircraft this figure might be multiplied a few times. Making sure all these thousands of parts are assembled well and run perfectly requires extensive statistical monitoring. Today, much of the process is automated, but human process control managers still play a large role.

Here are a few types of tools that are commonly used by professionals in the field of statistical process control:

Flow charts: A flow chart maps a process in all its complex parts from beginning to end. While they are not unique to statistical process control and in fact have little to do with the statistics themselves, flow charts are useful for giving quality control managers an overall picture of the process, which makes it easier to evaluate data discrepancies pointing to potential variances. They’re also useful for giving a clear picture of where portions of the process fall within an overall timeline and in relation to other portions.

Run graphs: A run graph is simply a graph that displays data in terms of time. While run graphs can be useful for looking at a single point of data, in statistical process analysis they are often used to get a quick snapshot of the relationships between different data points. For example, if two data points almost always rise and fall at the same time, then this points to a likely relationship, whether direct or indirect, between what is measured by the two statistics. Run graphs are most useful for two variables and can become quite chaotic when more variables are introduced, but using them to interpret the relationships between three or more data points is not unheard of.

Designed experiments: A designed experiment is exactly what it sounds like. When a process begins creating results that are out of line with expectations, quality control managers may undergo a set of experiments to pinpoint the source of the flaws. This may involve segmenting the process, taking some elements out, or adding elements in an attempt to attenuate the flaws for informational purposes.

Pareto charts: Pareto charts are based on the Pareto Principle, which states that that not all causes of a phenomenon have the same impact. So, for example, if a product is flawed, there may be four different causes of the flaw, but it may be that one of the causes is the biggest culprit while the others would not be significant without the primary cause. Pareto charts try to make sense of this, mapping the relative impact of different factors in a system.

Control graphs: Control graphs are essentially maps of the frequency of variations over time. If a process is statistically stable, a control graph shows nothing. It’s only when instability occurs and variances start creeping in that a control graph becomes useful. These graphs are helpful for locating patterns of variance and identifying whether variances result from mere chance or are results of a flaw in the process.

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.

The statistical process control when it will be applied properly can really improve the stuffs that you have already done and instilled in your business or organization. Just like the other business tools, this particular process has also the capacity to manage the different industrial wireless machines with more efficiency.

The process control is a method that uses statistics and the discipline in the engineering which is dealing with the mechanisms, architectures, and the algorithms for the task of maintaining the result of a certain process within the preferred range.

This kind of system is widely used in the industry and it also has the capacity in enabling the mass production of various processes such as oil refinery, refueling of truck, chemical plants, the loading of trucks and many more involved in the field of industrial.

Learning to utilize this kind of process has many benefits to offer you which will help your business or organization to run smoothly and without the usual problems that are encountered by most businesses today. It is very helpful in the process of monitoring of the manufacturing environment and controls via electrical means the flow of the manufacturing process which is based on the limitation set but the one who will use it.

In a system where this kind of process control is running, there is the device for measurement that is generally used in distinguishing the liquid or the gas which are both represented in the industrial environment. The signature frequency of the various gasses or of the liquid will be sent to the designated receiver then will be converted into a digital form of signal which is identified by its processor. This particular identification is typically controlled by a specific host controller as well as the automation system designated for the tasks.

The following are the different types of the process control which are utilized in the wireless industrial products:

• Discreet process
• Batch process
• Continuous process
• Statistic process control

The above mentioned are some of the processes that are used by various companies and organizations nowadays and is said to be one of the reason why most businesses are enjoying high profits. This is because a well run system running in a company will assure that there will be fluidity that will result in positive output.

From the processes mentioned above, it is the statistical process that is considered as the most popular especially in the field of engineering since they are broadly used in the production of the wireless industrial control products. It is also the one that is utilized in the maintenance of a complex process or in the huge machines used in the field of engineering.

The statistical process control is one of the most effective methods that can be used in the effective monitoring of different processes. It is very efficient as it uses the distinctive charts that can break down complex stuffs into simpler figures that can be understood by any ordinary employee. This is the reason why most fields nowadays are considering this process over the others.

The statistical process control has the capacity to deal with analog type of signals from meters and sensor that are transported to specialized kind of computers. To sum it up, this kind of process is making the extensive utilization of the digital and analog conversion.

This process also involves the different statistical techniques that is measuring and analyzing the variation used for a particular process. Most of the times, this process can be seen in the process of manufacturing where it has the mission of monitoring the quality of the products and maintain the fixed quota at the same time.

There are many things that will simply show you the importance of having this process running into your organization or business. As you can see, most of your competitors are already using it, so you might as well as use it if you do not want to be left behind.

The following are the importance of this process control:

• With the help of this process, the task of monitoring your business important procedures will be much easier.
• It can also help you to increase the productivity of your company while maintaining its overall quality
• It can implement the consistency within your business which will result in great output or finish products.

Moreover, there is the instance that the removal of the unimportant and defective processes can also result into less production of the flawed products and services which will surely benefit your customers. This will satisfy your customers as they can receive the products they have requested exactly on time. This way, you will never lose the grasp on the loyalty of your consumers since you are not letting them down and giving back the worth of their money.

Just like what has been said before, this process is use to monitor the process consistency in the production of various products. It also help in making sure that a specific process will be always under control without relying much on your employee. It is just like inserting a well run system that will be able to run even without the supervision of human being.

Below are the things that you can expect that this process will do for your business:

• It will provide the crucial feedback as well as the observation to the different processes that have been used before.
• It will also alert you whenever there will be problem within a particular procedure. It will immediately detect the causes of the problem thus giving you more time to think ahead of the solution that will not allow the problem to grow bigger.
• It can carry out the perfect characterization of the process and can also lessen the time intended for inspection.
• It can consistently monitor the quality of the process running into your company
• It will also provide the needed mechanism that will make some processes alterations apart from tracking the changes that have occurred.

As you can see, having the knowledge about the statistical process control will really pay off given the above capabilities and benefits that it can give you and your company. Remember, others are already using it, so you must start utilizing it since you have lots of things to catch up.

The statistical process control, as you can see is what most organizations and companies are looking for and is the one that separates them from each other. The amount of knowledge that they have about it will determine the edge they will have over their rivals. But most importantly, it can help them in the smooth operation of their companies.

In case you are not yet aware of this process, it is important that you will have some knowledge on its history. This will show you where this process came from and how it is able to benefit different people:

This process was discovered by Walter A. Shewhart in the early part of 20’s. It was W. Edwards Deming who had applied this method during the World War II in USA. He had used this method in the improvement of the quality of the production of ammunitions as well as the other significant products during that time. Mr. Deming was also the one responsible in introducing this particular method to the industry of Japanese people right after the war has ended.

It was Shewhart who had created the concept of the control charts and the idea of coming up of a statistical control by the use of a designed experiment. He had discovered that variables present in the manufacturing data are not always behaving the same with the data present in nature. This forced him to conclude that although each process has their variation from one another, there are some processes that are able to display a controlled type of variation which is just natural to the available process.

In the year 1988, the institute of Software Engineering has introduced the perception that the statistical process method can be utilized to various non-manufacturing methods like the processes in software engineering. This idea has made it clear that this method is a very useful and beneficial tool whenever it will be applied to processes of knowledge-intensive, non-repetitive kind of process such as those in engineering. However, some of these concepts are still encountering skepticism and has remained divisive as of the current time.

There are two types of variation that normally occurs in the process of manufacturing: the common cause variation and the special cause type of variation. These two can cause successive variation regarding the finish product.

• Common cause—this includes variation in the electrical current strength, raw materials properties, and temperature.
• Special cause—or also known as the assignable-cause type of variation only takes place seldom when compare to the first type of variation.

The statistical process procedure is effectively indicating that an action is very needed to be done in a particular process. It will also tell one company when one should not take any action.

The statistical process control will give you many benefits as long as you will understand it. Knowing how to utilize this process will make you one of the people who have succeeded on their respective businesses.

The main emphasis of the statistical process control is usually seen in the aspect of the quality improvement, but this method can also be used for the improvement of human relations, profits and productivity, enhancing the area where you are good at and pin pointing the field where you will really to do some improvement.

To correct the common misconception about this method, this one will not provide you the fast remedy for a problem. This is the approach to the quality management which is focusing on facts and will have to involve everyone within the organization. Before you would achieve results here, it will take some time, patience as well as your commitment and of the management.

There is the philosophy use in the basic quality which will require you to do things right even if it will be the first time. This philosophy must be really applied to all aspects and groups within any organization such as the purchasing, engineering, manufacturing and others. The process control is helping in the reduction of the product variability and processes that results in the increased the quality of the products that are being produced.

Below are some of the basic philosophies involved in this method:

• Improvement of the quality would result in enhanced productivity
• Improvement in productivity would result in the lowering of the cost as well as the prices
• Improvement in quality and the lowered prices would result in the improvement in market share
• Improvement in the market share would generate more job opportunities

Understanding how this process control work will be crucial so that you can fully utilize the benefits that it can give to your organization. A simple demonstration how it is work is by contrasting the classic type of control cycle and the SPC type of control cycle. Below is the brief comparison of these two:

• Classic cycle—it is when a company is using the inspection called the sorting method. In this process, the products are produce first and will be sorted the bad from the goods after the completion of their production. This kind of method usually result in high scrap, high cost and the poor quality of products deliver from the customers.
• SPC method—this one is the exact opposite of the classic method. In this process, you will be properly briefed of the things that are happening inside the production, which mean you can make the necessary adjustments even before the products could be out for the market. This method will result in the reduced products’ variability, the reduction in the cost of inspection, less scraps and much improved product’s quality.

What you have read above are the facts and important details that will make you understand the statistical process control is all about. as you can see, having the knowledge of this process and using it for your company, you are guaranteed that you will get the benefits that it is capable of providing just like it has provided to other organizations.