Statistical process control began as a set of methods for companies to monitor the quality of their products and eliminate variances from item to item. The methods borrow ideas from the field of statistics and apply them to sophisticated and often complex processes that are difficult to monitor without an innovative monitoring system. It’s used most often by companies whose large and complex manufacturing processes are cumbersome to track via old-fashioned methods, and it’s also widely used by companies that need as little variance as possible in their manufactured products. And for adventurous statistics buffs, SPC can also be applied to many other areas of life.
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.
