Frequently Asked Questions
What makes ProcessMiner™ unique?
- Complete autonomous control through predictive closed-loop control.
- Advanced data science paired with industry expertise.
- Turn-key solution that is easily and quickly deployed.
What preparation is needed to implement the ProcessMiner™ platform?
The ProcessMiner™ platform is a cloud hosted application that is interoperable with most existing technology and infrastructure already deployed at the factory. Little preparation is required. Most of the weight-lifting is done by the ProcessMiner™ team. At the initial stage, we conduct an in-depth problem discovery, run a preliminary analysis, and then deploy the real-time predictive and autonomous control.
How do users access the platform?
The ProcessMiner™ dashboard is accessible via a secure URL and the predictions are displayed so that the customer’s operations team can take full advantage of them.
How is data health assessed?
The data health report will identify whether the customer’s data is suitable for advanced analytics. Metrics that evaluate quantity, frequency, quality, and accuracy are used to evaluate data health and integrity.
How long does it take to deploy?
We typically have a new customer operating within four to six weeks of establishing streaming data.
How do we get started?
It’s simple! Contact a Miner today!
What is predictive analytics?
Predictive analytics uses historic data to predict process outcomes. With the advantage of advanced machine learning, these models continue to learn providing the user with accurate information that can drive better decisions.
When a variable in the process changes, will the model adapt to the change?
The ProcessMiner™ platform utilizes advanced machine learning algorithms that identifies changes in the data and adapts the proper data model for the most up-to-date predictions.
How is data collected?
Data can be accessed from any source and in real-time or batch. We currently access data from all historians and IIoT devices operational in the factory or on the production lines.
What data is collected?
The data comprises real-time process variables (collected via sensors or IIoT devices), the control variables, product quality measurements, product failure, product grade, machine run status, and other applicable parameters.
Can new variables or new technology be added to the model?
Contact Our Data Science Team
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715 Peachtree Street NE
Atlanta, GA 30308
+1 (972) 977-8700