January 2, 2018
The Top Advantages of Statistical Process Control: Part One
No matter what line of business you are in—from retail to hospitality to technology to finance—it’s imperative to prevent business problems before they occur. That can mean something as simple as avoiding the loss of one of your customers to poor customer service or as critical as preventing a botched manufacturing job on one of your flagship offerings.
Thankfully, when it comes to the latter, there are enhanced methods that companies can employ to assure manufacturing quality control—such as statistical process control (SPC).
SPC refers to a scientific, data-driven method for quality analysis and improvement that allows you to prevent problems from occurring. One such example is creating control charts—visual diagrams that track shop floor processes and detect issues, variances, and defects in real time.
Today, we explore the how the top benefits of control charts on the manufacturing shop floor.
1. Real-time SPC helps reduce the margin of error
Because control charts reveal what’s going on in a manufacturing line in real time, they allow operators to detect and correct issues before they cause deeper problems in processes and products. This greatly reduces the need for product rework or additional product expenditures to fix an offering.
“Control charts serve as the early warning detection system in your real-time monitoring software, telling you that now is the time to go in and make a change,” says Steve Wise, vice president of Statistical Methods for InfinityQS. “That way, you don’t finish the whole run only to find out you should have made adjustments three hours ago and now have to eat the costs associated with this problem.”
2. Visibility into quality data prevents over-tampering
It is just as important to know when your process is running smoothly as it is to know when something is wrong. Specifically, when trying to detect whether a problem exists, operators can quite frequently over-tamper with a process that was running correctly, which can lead to more variances.
After analyzing a control chart, operators need to determine whether to “do something” (adjust a behavior in the process) or “do nothing” (let the process run as is). Often, learning that they can do nothing prevents operators from over-tampering with their processes.