Data-driven algorithm for throughput bottleneck analysis of production systems
Journal article, 2018

The digital transformation of manufacturing industries is expected to yield increased productivity.

Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect.

This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected.

The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES data-sets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.

Production system

Maintenance

Data-driven

Throughput bottleneck detection

Smart manufacturing

Author

Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Hans Salomonsson

Chalmers, Computer Science and Engineering (Chalmers)

Pramod Bangalore

Chalmers, Computer Science and Engineering (Chalmers)

Maheshwaran Gopalakrishnan

Chalmers, Industrial and Materials Science, Production Systems

Muhammad Azam Sheikh

Chalmers, Computer Science and Engineering (Chalmers)

Production and Manufacturing Research

2169-3277 (eISSN)

Vol. 6 1 225-246

DAIMP - Data Analytics in Maintenance Planning

VINNOVA, 2016-03-01 -- 2019-02-28.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Computer and Information Science

Areas of Advance

Production

DOI

10.1080/21693277.2018.1496491

More information

Latest update

1/30/2019