Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes
ABSTRACTThroughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2023.2283031 |
| _version_ | 1797292129987330048 |
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| author | Anders Skoogh Matthias Thürer Mukund Subramaniyan Andrea Matta Christoph Roser |
| author_facet | Anders Skoogh Matthias Thürer Mukund Subramaniyan Andrea Matta Christoph Roser |
| author_sort | Anders Skoogh |
| collection | DOAJ |
| description | ABSTRACTThroughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice. |
| first_indexed | 2024-03-07T19:47:17Z |
| format | Article |
| id | doaj.art-55dea4e733f64559b8b45d3db9f16b27 |
| institution | Directory Open Access Journal |
| issn | 2169-3277 |
| language | English |
| last_indexed | 2024-03-07T19:47:17Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj.art-55dea4e733f64559b8b45d3db9f16b272024-02-28T19:28:41ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772023-12-0111110.1080/21693277.2023.2283031Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modesAnders Skoogh0Matthias Thürer1Mukund Subramaniyan2Andrea Matta3Christoph Roser4Department of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, SwedenDepartment of Mechanical Engineering, Chair of Factory Planning and Intralogistics, Chemnitz University of Technology, Chemnitz, GermanyDepartment of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, SwedenDepartment of Mechanical Engineering, Politecnico di Milano, Milan, ItalyDepartment of Management Science and Engineering, Karlsruhe University of Applied Sciences, Karlsruhe, GermanyABSTRACTThroughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.https://www.tandfonline.com/doi/10.1080/21693277.2023.2283031Throughput bottleneckstheory of constraintsproduction controloperations management |
| spellingShingle | Anders Skoogh Matthias Thürer Mukund Subramaniyan Andrea Matta Christoph Roser Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes Production and Manufacturing Research: An Open Access Journal Throughput bottlenecks theory of constraints production control operations management |
| title | Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes |
| title_full | Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes |
| title_fullStr | Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes |
| title_full_unstemmed | Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes |
| title_short | Throughput bottleneck detection in manufacturing: a systematic review of the literature on methods and operationalization modes |
| title_sort | throughput bottleneck detection in manufacturing a systematic review of the literature on methods and operationalization modes |
| topic | Throughput bottlenecks theory of constraints production control operations management |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2023.2283031 |
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