Data-Driven Bottleneck Detection with Minimal Information in Manufacturing Systems

Leede Frank

doi:10.70393/6a6374616d.333634

Authors

Leede Frank University of California-Berkeley

DOI:

https://doi.org/10.70393/6a6374616d.333634

ARK:

https://n2t.net/ark:/40704/JCTAM.v3n1a09

Disciplines:

Applied Mathematics

Subjects:

Mathematical Modeling

References:

28

Keywords:

Bottleneck Detection, Minimal Information, Manufacturing Systems, Queue Directed Graph

Abstract

Bottleneck detection is crucial for optimizing production systems, but many current methods in manufacturing environments rely on large amounts of process data that are difficult to obtain in real time. This paper explores a novel data-driven approach that uses minimal information to identify and analyze production bottlenecks. By combining minimal information with various activity cycle and queuing cycle methods, it maintains bottleneck detection accuracy while reducing data collection requirements.

Author Biography

Leede Frank, University of California-Berkeley

University of California-Berkeley, 94720, US.

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