Autonomous
Manufacturing
Made Simple
The Future of Manufacturing is Here
The ProcessMiner™ AI platform is autonomous manufacturing made simple. Our industry leading autonomous control solution delivers process improvement recommendations and parameter control changes in real-time to the production line. Our platform ensures reductions in scrap, defects and errors commonly encountered in complex manufacturing processes.
Manufacturing + ProcessMiner = Sustainability Goals
Manufacturing Operational Improvement
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- Address Skilled Labor Shortage
- Improve Product Quality
- Increase Yield
- Increase Throughput
Manufacturing Sustainability Improvement
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- Save Energy
- Reduce Emissions
- Reduce Chemicals Usage
- Save Raw Materials
- Decrease Water Usage
Monetize Your Data
Step #1: Collect Clean, Relevant and Actionable Data
The first step in the optimization process is to access the historical data kept on the server and analyze it for data quality and consistency. This analysis will determine if the data collected is clean and consistently formatted for the purpose of optimizing the manufacturing process.
Continuous Quality Improvement is Our #1 Goal
ProcessMiner’s predictive system solves problems before they happen, by making recommendations and prescribing solutions in real-time. In doing so, ProcessMiner™ minimizes raw material usage and reduces other costs, while maintaining both speed and product quality.
Turn-Key, AutoPilot Solution
Easily and quickly deployed turn-key solution for manufacturing processes allows manufacturers to do analysis without a staff of data scientists.
Advanced Data Science
We pair data science, technology and industry expertise to unleash the full potential of digital manufacturing: complete autonomous control for continuous quality improvement.
Real-time Anomaly Detection
Real-time artificial intelligence machine monitoring, predictions, prescriptions and recommendations, gives manufacturers access to metrics both off- and online.
Closed-loop Control
Predictive to root cause automatic vision control. Our AutoPilot platform achieves unprecedented autonomous, closed-loop control of machines for continuous manufacturing.
Partner and Affiliate Organizations
Autonomous Manufacturing Made Simple
Monitor
Monitoring historical data ensures you’re collecting clean, relevant, trustworthy and actionable data to relay back to the server.
Predict
Machine-learning system begins predicting performance measures and identifying improvement opportunities.
Recommend
Easy to read suggestions for minimizing variability though improvement, preventative and corrective actions.
Closed Loop
Through our predictive machine learning and artificial intelligence systems, real-time recommendations are automatically sent for autonomous manufacturing.
Optimize
Continual improvement that optimizes process performance and variability. The longer it’s used, the more ProcessMiner improves.
Learn
With good, clean data in hand, applies advanced data science and machine learning techniques to your manufacturing process to find insights and impact ROI.
What We Do
Continuous Quality Improvement
Reduce energy consumption, raw materials and chemicals usage
Build a real-time AI platform that is self adaptive & evolving
Meet and exceed quality specs through reducing variability
Research & design to solve manufacturing problems and improve quality
Case Studies
ProcessMiner™ Director of Science, Chitta Ranjan, shares his extensive research on data in the manufacturing industry.
Extreme Rare Event Classification using Autoencoders in Keras
In a rare-event problem, we have an unbalanced dataset. Meaning, we have fewer positively labeled samples than negative. In a typical rare-event problem, the positively labeled data are around 5–10% of the total. In an extremely rare event problem, we have less than 1% positively labeled data.
Estimating Non-Linear Correlation in R
Correlation estimations are commonly used in various data mining applications. In my experience, nonlinear correlations are quite common in various processes. Due to this, nonlinear models, such as SVM, are employed for regression, classification, etc.
Sequence Embedding for Clustering and Classification
Here we will learn an approach to get vector embeddings for string sequences. These embeddings can be used for Clustering and Classification. Sequence modeling has been a challenge. This is because of the inherent un-structuredness of sequence data. Just like texts in Natural Language Processing (NLP), sequences are arbitrary strings.
A lot of what Solenis provides is helping our customers meet a specific quality objective. We were aggregating data and visualizing it for our customers to show value for the programs we had, but we weren’t actually doing stuff with that data. A big piece of the analytics space we were struggling with was there was no focus. We met with many vendors, big and small, different sizes and countless specialties. That’s when we came across ProcessMiner. ProcessMiner was very focused on quality. We went with ProcessMiner.
Contact Our Data Science Team
Fill out the form below and we’ll get back to you, or contact us by phone to speak to a miner today.
Have a project in mind?
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U.S. Headquarters
715 Peachtree Street NE
Suite 100
Atlanta, GA 30308
+1 (678) 463-5799
info@processminer.com
International
INDIA
Ahmedabad, Chennai
FINLAND
Oy ProcessMiner Ab
Otakaari 5
02150 Espoo
+358 400 450 748
jvanska@processminer.com