Jinan University

Assembly Line

Artificial intelligence for throughput bottleneck analysis

Date:

Authors: Mukund Subramaniyan, Anders Skoogh, Jon Bokrantz, Muhammad Azam Sheikh, Matthias Thurer, Qing Chang

Organizations: Chalmers University of Technology, Jinan University, University of Virginia

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data.

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