An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection
Paper in proceeding, 2021

Bottleneck detection is vital for improving production capacity or reducing production time. Many different methods exist, although only a few of them can detect shifting bottlenecks. The active period method is based on the longest uninterrupted active time of a process, but the analytical algorithm is difficult to program requiring different self-iterating loops. Hence a simpler matrix-based algorithm was developed. This paper presents an improvement over the original algorithm with respect to accuracy.

Shifting bottleneck detection

Production system

Active period method

Throughput bottlenecks

Load balancing


Christoph Roser

Karlsruhe University of Applied Sciences

Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Björn Johansson

Chalmers, Industrial and Materials Science, Production Systems

IFIP Advances in Information and Communication Technology

1868-4238 (ISSN) 1868-422X (eISSN)

Vol. 630 683-689
9783030858735 (ISBN)

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems
Nantes, France,

DAIMP - Data Analytics in Maintenance Planning

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

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Software Engineering

Signal Processing

Computer Science

Areas of Advance




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