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)
Understand the Use-case under 5 minutes
Video (2.5 minutes)
The Golden Run model is an AI algorithm that uses data to identify ideal process parameters for production.. Its goal is to save time and boost profit for manufacturers by maximizing efficiency and cutting their waste.
Watch
Video (2.5 minutes)
See how predictive analytics and AI are used to detect problems on the shop floor before they occur, Providing insights on the best options for improved plant maintenance and performance
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article (4 minutes)
Operationalize your most profitable production parameters by relying on prescriptive performance analytics to increase throughput without sacrificing quality.
Read
WhitePaper
How Machine Learning could be used for optimizing production by increasing Overall Equipment Effectiveness. We recommend reading the first 4 pages, then pages 13 & 14 to get more in-depth info about this use-case.
Read
Article (6 minutes)
This article explains how AI can out-perform humans at data-driven tasks in manufacturing and how machine learning can help process optimization
ReadCase studies, Organizational Aspects, Return on Investment examples
Article (10 minutes)
In this article you will learn how companies with heavy assets are improving throughput, energy consumption, and profit per hour with customized AI solutions serving production process optimization
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Article (3 minutes)
Case Study showing how a Manufacturer used AI to save than 230 hours over 6 months, helping them execute their next run 15% more efficiently
Read
Article (6 minutes)
How AI can help Aluminum producers optimize their production processes and bolster growth
ReadMore details on the technical aspects of the use-case
Video (5 minutes)
Applying Machine Learning to a manufacturing workflow for an organic synthesis process. It’s used in combination with user-driven monitoring of real-time data optimize production
Watch
Article (6 minutes)
High level explanation for the role of Machine Learning in solving Production Optimization problems, using an example from the Oil & Gas industry
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Article (8 minutes)
In this article you will be exploring how to increase yield by optimizing feed rate, spindle speed, and throughput
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
The idea of this repository iis to utilize variation when scheduling the production of items. For example, it may make sense to produce high-consuming items to hours where the electricity price is expected to be low.
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Data Sets you can use to build Demos, POCs, or test Algorithms
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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