Watch this Ask the Expert session to learn how manufacturers can improve quality and operational performance while lowering the cost of poor quality.
Watch the webinar
You will learn how to:
- Use predictive and prescriptive analytics to make sustainable improvements in yield and throughput so you can lower the cost of quality.
- Use analytics to reduce scrap and energy costs.
- Simplify your production environment so that you can make sense of the hundreds or thousands of variables affecting end-product quality.
The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.
Q&A
Can process optimization run in real time?
It could, but I see in reality, most of our customers don’t. They’ll run the predictive modeling, and they’ll run the process optimization, they’ll generate the prescriptive setpoints. In general, they don’t change those setpoints in real time, they’ll change them periodically. One customer does it once a week for every product line and every production plant. Other customers might do it daily. They may do it on other things, like if they have a new raw material come in. Now, the predictive quality piece you can do in real time, for sure, to understand where you’re at. Though, optimization is usually done in a batch-type sequence.
What are the steps to optimize a process?
Obviously, the first thing is to capture all the data in the production floor. A lot of times, making sure we have all the right data that is tracked properly, that you can track the end metric to upstream production of process data which requires tracking materials as it flows through different assets. It requires tracking materials as they’re associated different materials in batches. Once that’s done, obviously, you have to build the predictive model. The predictive model will tell you the relationship between the output and all the inputs. At SAS with our optimization routines, we can then develop recommended inputs to be set to based on the output that is desired. And that’s why predictive quality and process optimization are kind of tied together so tightly because you have to build that predictive model before you can go to the optimization routine.
Recommended Resources
SAS Production Quality Analytics
Production Quality Analytics e-book
SAS Production Quality Analytics – Reducing the Cost of Quality video
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