Data-driven algorithm for throughput bottleneck analysis of production systems
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| Title: | Data-driven algorithm for throughput bottleneck analysis of production systems |
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| Authors: | Subramaniyan, Mukund, 1989, Skoogh, Anders, 1980, Salomonsson, Hans, 1985, Bangalore, Pramod, 1983, Gopalakrishnan, Maheshwaran, 1987, Sheikh, Muhammad Azam, 1979 |
| Source: | DAIMP - Dataanalys inom underhållsplanering Production and Manufacturing Research. 6(1):225-246 |
| Subject Terms: | productivity, statistical approach, Bottleneck, Smart Maintenance, Data-driven, Smart manufacturing, Maintenance, data science, Manufacturing Execution System, production, bottlenecks, big data, Production system, Analytics, machine learning, active period, manufacturing, maintenance, Throughput, MES, industry 4.0, Throughput bottleneck detection |
| Description: | 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. |
| File Description: | electronic |
| Access URL: | https://research.chalmers.se/publication/504561 https://research.chalmers.se/publication/504761 https://research.chalmers.se/publication/504761/file/504761_Fulltext.pdf |
| Database: | SwePub |
Nájsť tento článok vo Web of Science | Abstract: | 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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| ISSN: | 21693277 |
| DOI: | 10.1080/21693277.2018.1496491 |