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 datasets 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.
productivity
statistical approach
Bottleneck
Smart Maintenance
Data-driven
Smart manufacturing
Maintenance
data science
Manufacturing Execution System
production
bottlenecks
big data
Production system
Analytics
machine learning
active period
manufacturing
maintenance
Throughput
MES
industry 4.0
Throughput bottleneck detection
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 1 225-246DAIMP - Data Analytics in Maintenance Planning
VINNOVA (2015-06887), 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