Data-driven modeling of plant floor interactions

Researchers: Chiranjeevi Vegi

A data-driven model of the dynamic manufacturing system is a method to provide insights on the stochastic nature of the operation and interdependence of equipment. Currently, no closed form equation exist to model large manufacturing systems. It is possible to study performance and the effects of machine interaction on throughput and quality by leveraging plant floor data. Using descriptive statistics, statistical inference, machine learning and time series analysis, it is possible to identify patterns and trends from the data. Moreover, different machine operating conditions can be studied and tested using discrete event simulation. The data-driven models and simulations can help plant floor operations by suggesting optimal operating parameters to improve different aspects of manufacturing.

 

Discrete Event Simulation flowchart