For most production companies that have reached a certain level of digital maturity, the issue is not acquiring data. They have TONS of it.
No, the real challenge is how to get from those boatloads of data to concrete insights. Something that they can easily translate into actions on the factory floor.
But where to start?
This article will:
The parallel correlation feature is a starting point for exploring the data you already have by doing correlation analysis of production data.
Use it when there is something – output, scrap rate, anything – you want to improve, but you don’t know where to start.
The parallel correlation feature looks at multiple factors at the same time. This way, it helps you uncover which ones may be driving the results you care about. Kind of like checking many puzzle pieces at once to find the ones that fit together best.
With parallel correlation, you can take your existing historical data and explore correlations and look for causality. That gives you actionable insights right off the bat.
And it means that you can make better decisions based on what’s already happening in your production processes.
Take this common issue: The output of a machine fluctuates. Sometimes it’s high, at other times less so.
Now, what if you could recreate the conditions as they were when the machine performed at its best?
Understanding how different parameters influence output is the first step in that direction, and parallel correlation helps you quickly identify those key parameters
Based on experience, you probably have a hunch about what’s affecting production outcomes. These factors could be anything — from specific machine settings and the product being made to ambient temperature, lighting, or even the operator running the machine.
With parallel correlation, you can put those hunches to the test quickly. You simply select the parameters you believe are influencing performance, and the feature shows you exactly how those factors correlate with your results.
It takes the guesswork out of the equation and gives you clear indicators on which actions to take next.
Once you’ve chosen a set of parameters based on your hypothesis, all you need to do is set a timeframe, and you have a dataset to explore. From there, the interactive multivariate analysis lets you drill down into the data by filtering parameters.
And it shows you the real-time impact of changes as you make them. This means you can immediately see how different conditions — like machine speed or temperature — are affecting production output, and you can adjust accordingly.
You also don’t have to worry about drawing conclusions from too small a dataset. The platform always tells you how much of the original data you’re looking at, so you’ll know whether you’re dealing with a rare event or a more frequent issue.
In the example below, we use the parallel correlation feature to find the glue temperature setpoint that delivers the highest output.
The analysis reveals that the ideal glue temperature is not the same for the 20 ml and 40 ml product variants.
With this insight, the production team can adjust settings for each product and immediately start improving output.
And just like that: Concrete, actionable insights, uncovered in minutes.
Want to unlock hidden potential in your production data? Book a demo of CATCH.AI and see how parallel correlation can help you optimize processes.