KIS develops Machine Learning model to optimize manufacturing process


The R&D area of a 60B+ global enterprise wanted to prove out whether Machine Learning (ML) principles could be used to predict and optimize variables in their manufacturing process to develop new products. IOT sensors had gathered historical production data, however this data was largely underutilized. The objective was to create ML models to analyze data, look for patterns, and predict important quality control metrics for the manufacturing process.


consumer goods


3 months



KIS Solutions Machine Learning practice was engaged to work with the client team. Historical IOT sensor data was provided which measured important metrics along various steps in the manufacturing process (Temperature, pressure, material density, etc). Initial modeling work involved looking at the provided historical data to highlight dependent variables and compare against known scientific facts.  

Subsequently ~6 machine learning algorithms were trained from the data with test data holdouts. The purpose of the initial models was to predict “manufactured product final density” – an important measure in the quality control process of this manufacturing pipeline.

The 6 models were trained and tested against each other. Results were presented to the client and the model with the highest accuracy was chosen.

The chosen model was trained on additional data, and the final model was shown to be able to predict product density with an accuracy of>90%. This level of model accuracy was deemed to be greater than the “human experts” who were monitoring the data via manual means.

Future plans include streaming realtime data from IOT sensors deployed in various factories, and passing this data to a realtime ML algorithm which could predict and avoid manufacturing quality issues before they occur!

a few nice words

There is a ton of hype in the industry now regarding AI and ML. It seems like ML is the new “flavor of the day” and people are searching for ways to fit it into any problem. This project was a perfect example of where ML can actually provide significant value, and it was pleasure to work on such a project and show real results!

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