Computer-vision Powered Smart Production Assistant

Over 70% of tasks in manufacturing are still manual; therefore, over 75% of variation in manufacturing comes from human beings. Human errors were the major driver behind $22.1 billion in vehicle recalls in 2016. Currently, when plant operators want to gain an understanding of their manual processes, they send out their highly-paid industrial engineers to run time studies. These studies produce highly biased and inaccurate data that provides minimal value to manufacturing teams. This project aims to create a smart production assistant that helps manufacturing plant operators gain unprecedented visibility into their manual production operations, allowing them to optimize their worker efficiency while maximizing productivity. This will be done by automated data generation using computer vision, conversion of raw data into useable information, visualization of information using common Business Intelligence methodologies and lastly, prediction of future plant performance based on historical information, as well as information about other market drivers.

Faculty Supervisor:

Afshin Rahimi


Hamidreza Setare Kokab




Engineering - mechanical




University of Windsor



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