AI in Manufacturing – Improving Process using Prescriptive Analytics

Abstract: 

With the rise of Industry 4.0, computation power, data warehousing, and automation, factories have been increasingly becoming intelligent. Preventive maintenance of Machines and predicting the failures have become an increasingly common sight. AI has also empowered in planning and logistics, where the quantity of item to be manufactured and the timing of it, have been decided through the outputs of ML models. Now the manufacturers are increasingly focused on improving the quality of the process and the throughput through sustainable methods as rising global warming is a concern. To improve efficiency and to make the process sustainable, Machine Learning models coupled with optimization are used for Prescriptive Analytics. Data of the industrial process is often huge data with many processes and control variables involved. Understanding the variables requires domain knowledge expertise coupled with feature engineering techniques. A search-based optimization can be used for finding the Pareto optimal solution with objectives to maximize the KPI and finding the support in historical data. Identifying the interaction effects is done by learning the data through a prediction model. The performance after the process is predicted using modelling for the KPI. Sensitivity analysis was conducted to understand the effect of variables on the uncertainty of model output and the KPI. The process, then optimized for maximizing throughput provides prescriptive analytics thereby improving the performance and reducing energy consumption.

Bio: 

Aravind Kondamudi is a Data Science enthusiast. He has completed Dual Degree masters in BITS Pilani and currently working in Aditya Birla Group as a Data Scientist. His love for Data science started while he was working in a Microfluidics lab, he worked on modeling the flows using data science aspects rather than traditional computational modeling. Then he worked to model the metallurgical properties in multiferroic materials. Being a Manufacturing Engineer, He got a chance to work in Aditya Birla Group, a manufacturing giant. He currently works on improving the manufacturing process through Machine Learning models.