Manufacturers are continually looking for ways to improve processes, especially after a breakthrough in the industry. A breakthrough can be any major shift in the industry—such as a new type of material, a new technology, or a new way of improving efficiencies. Such a breakthrough may provide a dramatic advancement to your continuous improvement goals. When that happens, your challenge is maintaining your improvement after the breakthrough has had its effect.

For example, a bicycle manufacturer may respond to a breakthrough such as a lighter material for making frames. That breakthrough helps them in a continuous improvement goal: reducing frame weight. The continuous improvement goal is ongoing—always in front of them. 

Statistical process control (SPC) methods can help you maintain that manufacturing momentum and keep you on the path of continuous improvement. 


Applying SPC Methods

Say you’ve had a breakthrough improvement, and you’ve learned a better way to set up your production line—there’s a different combination of input parameter set points, or maybe different types of inputs—then how do you ensure things will stay at this new level? You want to make sure that your inputs remain stable.

We do that by creating control charts on the inputs with SPC (just as we do with the outputs), especially those that have the greatest effect on output. Essentially, you’re taking one step back—upstream—and applying SPC to those inputs.

 

SPC for Upstream Monitoring and Prototypes

When you apply SPC to your inputs, you watch the central tendency (mean) and variation (standard deviation) of these inputs. You then correlate that with the expected outputs, making sure everything behaves the way you want. One way to achieve this is with prototype runs.

So, you have new settings based on what you discovered from your breakthrough improvement. You’ve adjusted your expected results to these new settings. Sure, you achieve the results you want in the lab. Now, it’s time to move it onto the factory floor. The best way to verify is with small prototype runs. They’re inexpensive, yet they can verify your set up and ensure you’ll get the results you want. Keep in mind that when you go to full-scale production, things don’t always translate. But you have to start somewhere! You’re continuously improving!


Everyday Statistical Process Control

Processes require constant monitoring because they’re on machines. You don’t simply set up a machine on the plant floor and expect it to do its thing. Just as you pay attention to how your car drives and get it regular maintenance, you also need to keep a watchful eye on your manufacturing equipment and processes. 

No two machines are alike, even “identical” machines purchased from the same manufacturer. Under the same test conditions, and for the same material types, each machine produces a little different quality from the others. So, we continuously monitor them. Early warning detection is everything—in automobiles, healthcare (don’t forget your annual check-up), and manufacturing. 

SPC helps us maintain process control in our manufacturing operations. SPC is all about early warning detection.


SPC Terms: Mean and Sigma

In SPC, early warning detection is keeping an eye on two statistics: mean and sigma. That’s where we derive all the knowledge from SPC.

As an example, when you’re looking to maximize your machines’ efficiency, matching the right machine to the right job is paramount. You can save wear-and-tear, money, and headaches by always “job matching” correctly. If your SPC analysis shows that machine ‘A’ produces 10% rework and no scrap, versus machine ‘B’s’ 1% scrap and zero rework, you might want to rethink how you’re using the machines. 

This is valuable information. You can ask whether it’s more profitable to scrap 1% and not have to spend any money on rework—or rework 10%.

 

SPC Tools to Explore the Unknown

Suppose you’ve got these known, predictable means and sigmas for a particular machine or process. In that case, we start by simply using SPC on those to ensure the machine or line remains at the assumed level of variability—whether we like it or not. It doesn’t have to be good; it just has to be predictable, and then we can work with it. You use SPC at this stage to make sure the assumptions you’re making can still be relied upon. 

And then we approach the unknown. The worst sort of problem or issue in manufacturing is the unpredictable one you didn’t see coming. 

Imagine any unpredictable issue: a tool breaks, the machine’s feed malfunctions, or some little capacitor’s resistance on a control board misbehaves—it just causes a blip, or a mean shift, or a variation in the process. Maybe it doesn’t produce bad parts yet, but something has definitely changed.

SPC will point out those changes so you can correct them immediately—before they affect product quality. SPC helps you be prepared for the unpredictable shifts because it helps you see not just major defects—but anything that’s outside the norm.


Now Is the Time for Statistical Process Control

Don’t wait until customer complaints start rolling in before employing SPC. Your processes can tell you all you need to know to produce high-quality products all the time—if you keep an eye on them. 

Focus on the data points that define your processes and set up your SPC software to detect any variations. Make sure you measure the right things in your processes to get the desired results.

Sometimes, changes are subtle and easy to overlook. SPC software won’t miss them. It will help you maintain consistent quality across your entire organization. And, before you know it, you’ll be talking about that breakthrough improvement that vaulted your organization into the stratosphere. Happy hunting and manufacturing!

Watch the rest of the story. 

Learn more about how one customer took its plant from worst to first. Watch the video. When you’re ready to improve quality, reduce variations, and optimize processes in real-time, contact us to learn about our risk-free trial.