An algorithm for data-driven shifting bottleneck detection
Journal article, 2016
Manufacturing companies continuously capture shop floor information using sensors technologies, Manufacturing Execution Systems (MES), Enterprise Resource Planning systems. The volumes of data collected by these technologies are growing and the pace of that growth is accelerating. Manufacturing data is constantly changing but immediately relevant. Collecting and analysing them on a real-time basis can lead to increased productivity. Particularly, prioritising improvement activities such as cycle time improvement, setup time reduction and maintenance activities on bottleneck machines is an important part of the operations management process on the shop floor to improve productivity. The first step in that process is the identification of bottlenecks. This paper introduces a purely data-driven shifting bottleneck detection algorithm to identify the bottlenecks from the real-time data of the machines as captured by MES. The developed algorithm detects the current bottleneck at any given time, the average and the non-bottlenecks over a time interval. The algorithm has been tested over real-world MES data sets of two manufacturing companies, identifying the potentials and the prerequisites of the data-driven method. The main prerequisite of the proposed data-driven method is that all the states of the machine should be monitored by MES during the production run.
bottleneck
shifting bottleneck
manufacturing execution systems
real-time
industry4.0
MES
data-driven
decision support
performance analysis
Constraint theory
data-driven maintenance planning
smart manufacturing
Resource management
Industrial Engineering
active duration
shifting production
manufacturing systems
shop floor
big data
shifting
throughput analysis
data analytics
Production Systems
active period
Industry 4.0
bottlenecks
decision making
increase productivity
data driven method
production
Author
Mukund Subramaniyan
Chalmers, Product and Production Development, Production Systems
Anders Skoogh
Chalmers, Product and Production Development, Production Systems
Maheshwaran Gopalakrishnan
Chalmers, Product and Production Development, Production Systems
Hans Salomonsson
CSE Verksamhetsstöd
A. Hanna
Volvo Group
Dan Lämkull
Volvo Cars
Cogent Engineering
23311916 (eISSN)
Vol. 3 1 1-19 1239516Streamlined Modeling and Decision Support for Fact-based Production Development (StreaMod)
VINNOVA (2013-04726), 2013-12-02 -- 2016-12-01.
Subject Categories
Production Engineering, Human Work Science and Ergonomics
Areas of Advance
Production
DOI
10.1080/23311916.2016.1239516