Statistical process control (SPC) has been around for a long time. While everyone’s heard about it, not many people are doing much with it. The short definition of SPC is that it’s the application of statistical methods to control manufacturing processes.
SPC, particularly real-time SPC, can help you avoid failures. By using SPC you can tell when a process is about to go out of control. And, if you know it’s about to go out of control, you can do something about it before it goes out of control. That helps you significantly reduce quality issues and helps you reduce downgrades and rework. It helps you identify problems, before they’re problems, and then eliminate, or at least minimize, the failures.
SPC helps you improve product consistency. With SPC you can look at the process at lot more closely so you can start ferreting out the causes of product variability. You’ll find processes that you thought were very stable aren’t in fact as stable as you thought. SPC lets you identify those processes, determine the degree that they’re stable or unstable, and then take the actions to improve the process. And, SPC gives you the information to figure out if the steps you’re taking to improve the process actually work.
SPC, particularly historical SPC, helps you with your continuous improvement initiatives. Like most people, you probably have some type of continuous improvement program underway, like Six Sigma or lean manufacturing. SPC helps you analyze where you need improvements and helps you pinpoint specifically what types of improvements you need to make. As you make those improvements, SPC helps you verify that the improvements are actually working and things are actually getting better just as you hoped.
The most basic component of SPC is the data. You have to have data and you have to get it from somewhere. It can exist almost anywhere but most commonly it resides in a database, a historian or both. How it got there is not really important, but you have to have data. You can get it in there automatically (which is probably preferred) or manually (which also works) but you just have to get the data and get it in the historian or database so you can do something with it in SPC.
Once you have the data the most fundamental aspect of SPC is real-time data visibility. That is, you can see all this data in real-time as it’s being collected. So even before you start to apply all the SPC rules, with real-time data visibility you can see the data, and right there, just seeing the data gives you and the operators a lot of important information.
Once you’ve got the data and the real-time visibility, you can then apply all the SPC rules. You can set up specification limits and set up real-time alarms and notifications based on the spec limits. You can apply the basic SPC rules and set up real-time alarms and notification based on them as well. And you can even set up the pattern rules or run rules with real-time alarms and notification on them too. With all of this displayed in real-time, the people who can actually do something about all this, namely the operators on the shop floor, get to see the information they actually need – in real-time.
Historical analysis takes the SPC data to a whole new level. Historical SPC allows you to analyze batches over time, or compare products over time, or compare equipment over time, or lines over time, and so on. There’s really no limit to the time spans you can look at as long as you have the data. So, if you wanted to, you could actually analyze every batch that made a particular product on a particular line for the last year. Historical SPC is where you see if your continuous improvement initiatives are really working over the long-term.
In the real world, SPC has some major business benefits. SPC helps you avoid failures which reduces quality issues and rework. SPC helps reduce process variability which reduces product variability and improves product consistency. And, SPC helps with just about any aspect of continuous improvement you can think of. Take a look at SPC, it’s well worth it.