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.




smart maintenance

machine learning

Industry 4.0

Predictive analytics



managing bottlenecks


smart manufacturing

Bottleneck prediction


active period





Big data

throughput bottlenecks



Theory of Constraints


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. 125 533-544

DAIMP - 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




More information

Latest update