A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines
Journal article, 2018
Smart manufacturing is reshaping the manufacturing industry by boosting the integration of information and communication technologies and manufacturing process. As a result, manufacturing companies generate large volumes of machine data which can be potentially used to make data-driven operational decisions using informative computerized algorithms. In the manufacturing domain, it is well-known that the productivity of a production line is constrained by throughput bottlenecks. The operational dynamics of the production system causes the bottlenecks to shift among the production resources between the production runs. Therefore, prediction of the throughput bottlenecks of future production runs allows the production and maintenance engineers to proactively plan for resources to effectively manage the bottlenecks and achieve higher throughput. This paper proposes an active period based data-driven algorithm to predict throughput bottlenecks in the production system for the future production run from the large sets of machine data. To facilitate the prediction, we employ an auto-regressive integrated moving average (ARIMA) method to predict the active periods of the machine. The novelty of the work is the integration of ARIMA methodology with the data-driven active period technique to develop a bottleneck prediction algorithm. The proposed prediction algorithm is tested on real-world production data from an automotive production line. The bottleneck prediction algorithm is evaluated by treating it as a binary classifier problem and adapted the appropriate evaluation metrics. Furthermore, an attempt is made to determine the amount of past data needed for better forecasting the active periods.
shifting
Real-world
production
smart maintenance
machine learning
Industry 4.0
Predictive analytics
Digitalisation
throughput
managing bottlenecks
Maintenance
smart manufacturing
Bottleneck prediction
ARIMA
active period
constraints
decision-making
bottlenecks
bottleneck
Big data
throughput bottlenecks
prediction
data-driven
Theory of Constraints
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), CSE Verksamhetsstöd
Pramod Bangalore
Chalmers, Computer Science and Engineering (Chalmers)
Jon Bokrantz
Chalmers, Industrial and Materials Science, Production Systems
Computers and Industrial Engineering
0360-8352 (ISSN)
Vol. 125 533-544DAIMP - Data Analytics in Maintenance Planning
VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.
Subject Categories
Production Engineering, Human Work Science and Ergonomics
Computer Science
Computer Systems
Areas of Advance
Production
DOI
10.1016/j.cie.2018.04.024