Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions
Review article, 2021

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. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis.

Production system

Data-driven

Manufacturing system

Throughput bottlenecks

Manufacturing

machine learning

Artificial intelligence

data analytics

Author

Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Jon Bokrantz

Chalmers, Industrial and Materials Science, Production Systems

Muhammad Azam Sheikh

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd, Data Science Research Engineers

Matthias Thürer

Jinan University

Qing Chang

University of Virginia

Journal of Manufacturing Systems

0278-6125 (ISSN)

Vol. 60 734-751

DAIMP - Data Analytics in Maintenance Planning

VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Areas of Advance

Production

DOI

10.1016/j.jmsy.2021.07.021

More information

Latest update

9/13/2021