RAMPCO manufacturing optimization software leverage Industrial sensors and Artificial Intelligence (AI)-based technologies to digitize plants, track all inputs and outputs, parts and material, allowing full tracking of the process from inputs to end-product.

Data-driven control is an emerging field of recent current interest in industrial control that seeks to develop controllers without knowing dynamical models of the system. We intend to program software models using innovative advanced analytics and deep learning techniques to generate high accuracy and fast simulation through. These models are subsequently implemented in a pioneering integrated production optimization tool, supporting field operations. Our Regression Algorithms will find a relationship between inputs and outputs and extract the corresponding rules. Machine Learning (ML) learns from examples.

The computer retrieves the rules instead of us programming it. However, training the ML is essential. ML has three major branches; supervised, unsupervised, and reinforcement learning. We use supervised learning. Supervised learning has two main sub-branches, namely, regression and classification.

Increase manufacturing throughput driven by RAMPCO

We can solve many operational problems by implementing optimization methods and optimal solutions

Real-life issues are more complicated than the theoretically stated pure base stand-alone mathematical models. Artificial Intelligence (AI) is an adaptive self-learning solution based on massive amounts of data and machine learning algorithms. AI-fueled Product Optimization has a superior ability to predict and continuously adapt to conditions and to generate higher margins. Primarily a producer with substantial assets that are unable to convert their machine-generated data into a readable format to address the needs of customers and suppliers will face challenges which could be detrimental for their survival, and quickly lose out to their competitors. Currently, there is readily available data where process-industry plants are capturing and storing vast amounts of machine data on a persistent and routine basis, which can be used to create algorithms.

State-of-the-art machine learning algorithms identify issues on a real-time basis in production and highlight efficiency variations. Machine Learning, a subset of AI, can be trained (supervised training), so the software can be used daily to identify production issues and continuously improve processes quickly.

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Artificial Intelligence (AI)-based Software to Digitize Production Process

Photo by Alain Pham on Unsplash

This software applies to all production and manufacturing plants

  • Steel Production
  • Chemical Production
  • Petrochemical Industry
  • Automotive Industry

Our product offering software can provide the following values:

  • Safety Improvements
  • Improving product quality
  • Optimizing the production process
  • Reducing the production cost
  • Decreasing the pollution resulting from the production
  • Self-learning of the software over time as the data volume increases

Rampco Machine Learning Software develop a data-driven Machine Learning (ML) model that can use past actual production input parameters and their corresponding output parameters to estimate the system output based on its inputs.

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We develop a data-driven Machine Learning model that can use actual production input parameters and their output parameters to estimate the output.

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