Journal
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL
Volume 6, Issue 1, Pages 225-246Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/21693277.2018.1496491
Keywords
Throughput bottleneck detection; Smart manufacturing; Maintenance; Data-driven; Production system
Categories
Funding
- VINNOVA
- Swedish Energy Agency
- Swedish Transport Administration
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The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the real-time machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.
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