Name
Title | ||
|---|---|---|
A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. |
Abstract | ||
|---|---|---|
•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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.cie.2018.04.024 | Computers & Industrial Engineering |
Keywords | Field | DocType |
Digitalisation,Bottleneck prediction,Big data,Industry 4.0,Maintenance,Predictive analytics | Bottleneck,Manufacturing,Data-driven,Algorithm,Boosting (machine learning),Production line,Engineering,Throughput,Big data,Automotive industry | Journal |
Volume | ISSN | Citations |
125 | 0360-8352 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mukund Subramaniyan | 1 | 0 | 0.34 |
| Anders Skoogh | 2 | 79 | 10.03 |
| Hans Salomonsson | 3 | 8 | 1.13 |
| Pramod Bangalore | 4 | 16 | 1.94 |
| Jon Bokrantz | 5 | 0 | 0.68 |