Revolutionizing Polymer Manufacturing: How goML Cut Production Time by 24% and Delivered Faster Than Ever for 40% of Customers

Table of Contents

Business Problem

The grade of plastic manufactured is a combination of input raw material quality & quantity, which decides the tensile strength & other characteristics, making it suitable for manufacturing specific types of plastic products
The polymers sourcing market, being highly un-managed, has a host of supply chain issues & pricing fluctuation, leading to the unpredictability of the end product costs & quality
To overcome this, Atom Corporation had set up an R&D unit of 25 people to manually test different combinations of these raw materials to produce the end product in multiple, controlled ways
The manual work required more than 30 days to produce the correct raw material manufacturing composition. Therefore, much wastage increased the order fulfilment time by 50%, resulting in order pile-up & customer loss.
GoML worked helped build an ML-based solution to predict the correct composition of raw polymers balls to produce a grade of plastic with certain characteristics

We worked with the team at Atom Corp. to understand the data that R&D team churns & the overall experimentation process
Manually collected data from the last 7 years was shared with us in excel files, which was loaded into S3 bucket
The data was then cleaned, removing the data where records of experiments which were done in non-standard environments & conditions and the Preprocessed data was stored in RDS SQL Database by leveraging SQL jobs
Amazon SageMaker was leveraged to experiment (Python , Pandas, Scikit learn) with 3 models Mixed logistic regression, Decision tree classification & SVM classification
It was found that a certain configuration of Decision tree classification performed as per expectation giving out an accuracy of 89%. The model was then deployed with production data on Amazon SageMaker