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Production Process Optimization

Optimize production parameters (e.g. pressure, temperature, energy use, line speed, flow rate..) to increase production rate, increase efficiency, and reduce costs – while still keeping high quality rates. 

Machine Learning can detect complex correlations between those parameters and production rate, and learn optimal configurations per different scenarios. Optimize Overall Equipment Effectiveness (OEE)

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ROI Examples
Data Needed

1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Combined Cycle Power Plant Data Set

9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the plant was set to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant.


Off-the-Shelf Products using AI for Production Process Optimization

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