Data-driven algorithm for throughput bottleneck analysis of production systems
Artikel i vetenskaplig tidskrift, 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
Författare
Mukund Subramaniyan
Chalmers, Industri- och materialvetenskap, Produktionssystem
Anders Skoogh
Chalmers, Industri- och materialvetenskap, Produktionssystem
Hans Salomonsson
Chalmers, Data- och informationsteknik
Pramod Bangalore
Chalmers, Data- och informationsteknik
Maheshwaran Gopalakrishnan
Chalmers, Industri- och materialvetenskap, Produktionssystem
Muhammad Azam Sheikh
Chalmers, Data- och informationsteknik
Production and Manufacturing Research
2169-3277 (eISSN)
Vol. 6 1 225-246DAIMP - Dataanalys inom underhållsplanering
VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.
Ämneskategorier
Produktionsteknik, arbetsvetenskap och ergonomi
Annan data- och informationsvetenskap
Styrkeområden
Produktion
DOI
10.1080/21693277.2018.1496491