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: Upasana Roy Chowdhury is a data science consultant with supply chain and manufacturing experience.