A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

[Display omitted] •Proposes a MES based data-driven algorithm to predict throughput bottlenecks.•Devised an approach to determine the size historical machine data to predict bottlenecks.•Proposes an evaluation framework to evaluate the algorithm performance.•Compared the performance of the algorithm...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 125; pp. 533 - 544
Main Authors: Subramaniyan, Mukund, Skoogh, Anders, Salomonsson, Hans, Bangalore, Pramod, Bokrantz, Jon
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2018
Subjects:
active period
ARIMA
Big data
bottleneck
Bottleneck prediction
bottlenecks
constraints
data-driven
decision-making
Digitalisation
Industry 4.0
machine learning
Maintenance
managing bottlenecks
prediction
Predictive analytics
production
Real-world
shifting
smart maintenance
smart manufacturing
Theory of Constraints
throughput
throughput bottlenecks
Digitalisation
Big data
Industry 4.0
Bottleneck prediction
Maintenance
Predictive analytics
ISSN:0360-8352, 1879-0550
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Summary:[Display omitted] •Proposes a MES based data-driven algorithm to predict throughput bottlenecks.•Devised an approach to determine the size historical machine data to predict bottlenecks.•Proposes an evaluation framework to evaluate the algorithm performance.•Compared the performance of the algorithm with naïve bottleneck prediction method.•Proposed algorithm outperformed naïve method of bottleneck prediction. 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.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2018.04.024