What keeps you up at night?
During these times of quarantine, economic chaos, and social distancing, there could be many things on your mind. If you’re responsible for quality control in a manufacturing environment, it could well be the effectiveness of your process.
Enforcing a repeatable process is a must in manufacturing, and it’s critical to quality control. But what happens when a process can’t be enforced as it’s designed?
Consider these process challenges:
Today’s article will tackle three ways data analytics applies to quality control professionals, including real-world examples...and how the right MES solution could be your key tool.
By the way, a great intro to this topic is our previous article about how to get started with data analytics in manufacturing. Ready to improve quality? Let’s go!
Your repeatable process is in place to ensure quality, yet manufacturing doesn’t always go according to plan.
For this example, you’re not a quality control manager; you’re now an operator at a station. And in the course of building an engine, you’ve run out of an oil filter, an obvious key component that’s required before testing the engine.
You can add an oil filter later, so you bypass that step now, and the line keeps running. Data analytics tracks that action, so the engine eventually gets diverted to repair to add the filter. Any action taken outside the process increases the chance of failure, yet you may not have a choice; the line needs to continue to run.
With many stations running, these process deviations can occur multiple times a day. Data analytics quantifies, on a large scale, all steps taken outside of the process, how often they happen, and why they’re happening. Capturing data with reasons for bypassing the prescribed process is critical because you can continuously refine your process, deviate less (making it more repeatable), and increase your ability to produce quality products the first time.
More on MES solutions later, but know this now: a good MES provides the flexibility to deviate from your quality control process and continue working.
OK, so your repeatable process may get interrupted by a deviation. That’s not earth-shattering. It’s when the process repeatedly fails that can be troublesome.
There are two common reasons products end up in repair:
In short, your process could be working properly, yet parts can still fail. That’s a nightmare, and it’s enough to keep you up at night!
Data analytics helps you quantify both problems. Instead of guessing on a cause or hoping things will change, data analytics shows you how many times a part had to be retested, giving you insight into repeatability of your process at the test cell.
There’s a lot you have little control over: parts from suppliers, isolating lot numbers, and trending failures relative to the part numbers you’re using.
Don’t get discouraged! You do control a bunch. If it’s in your building, you control it (at least you should). These are upstream process details that should be looked at as adjustable, and data analytics make it possible to root cause any failures. So, if you discover a better technique or an adjustment, you should be open to change.
As quality control, you’re responsible for overall quality at every level. But, what exactly is “quality” for you? Is it:
With those now top of mind, you’re really responsible for the quality of the final products you’re manufacturing AND the quality of the parts you’re receiving from suppliers. That list doesn’t happen without quality on both ends.
That brings up an interesting situation. What happens when a supplier issue is uncovered by data analytics? For instance, data analytics showed you that a problem correlates with when a shipment was received. Data analytics isolated the issue, which is one of its key functions.
If instructions are being followed to the letter, the supplier sees that, too, and will have to respond with a solution, the two teams work together to fix it. If that doesn’t happen satisfactorily, it may be time for another supplier.
Data analytics gave you something to chase in the upstream process. You analyzed the process, the lot numbers, the deviation...and you correlated that data between them all to uncover insights. You now have the ability to isolate part consumption, by supplier of those parts, to root cause any failures in a way that was unachievable before.
Let’s take a closer look in the context of an actual example. For this, you’re making axles for on-highway trucks, and they need to be tested.
An end-of-line test involves two major concerns: 1) Axles contain gears that move at high speeds, so they have to be partially filled with oil, and that oil needs to stay inside. 2) Because of the moving parts and high stress on the gears, heat is generated and causes expansion, and that pressure needs to be relieved.
Simple, right? Oil seals are used on rotating shafts and gaskets are used on differential covers to keep oil in, and air-breather vents are used to relieve any built-up pressure.
So, time for testing! You plug the air-breather, use the differential oil fill plug as a port to pressurize the axle, measure the pressure decay over time, and confirm that all the things that should keep oil in the axle are doing their job.
Fail! What do you do? This is a big deal. Perhaps you didn’t secure the plug correctly on the air-breather. You retest, and it fails again!
Only two areas could be the cause, differential cover gasket or seals (pinion and axle). Were bolts on the differential cover not tightened correctly? You check the install process, and even though all bolts check out, you tightened the bolts an extra 5 Newton-meters, and it passes the test!
Just because the seals are put in correctly and the bolts are tightened to spec doesn’t mean it’ll meet requirements. You monitor the situation for 30 days, adjust upstream processes, yet you are still having issues. Now you turn your attention to parts, discovering that you’re using non-conforming parts from a particular supplier.
How did your data analytics contribute to solving this issue?
What you know: there’s a better way to organize the mass of activity happening on the plant floor.
What you don’t know: how to gather and use data to improve how that activity is done, making it repeatable.
An MES provides visibility. It connects multiple sites, integrates with equipment, and raises the effectiveness of business applications all to better optimize operations. But, there’s more that an MES does.
An MES solution allows you to confidently take action rooted in knowledge.
Without an MES and its analytic capabilities, you’re bound to make the same mistakes again and again. Having no continuous improvement methodology in place, it’s impossible to increase the quality of your products. No one can afford that.
Always remember that a good MES solution must have two things: 1) provisions within its design that allow you to proceed (not shut down the whole line) and 2) tight controls when you have to deviate.
Does an MES sound interesting? We hope so! Learn more about what an MES solution does and how to select the right one. Read our guide, MES for Discrete Manufacturers; just click on the image below.