Data-driven dynamic bottleneck detection in complex manufacturing systems |
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| Affiliation: | 1. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA;2. umlaut Inc., Southfield, MI, 48034, USA;1. Performance Analysis Center of Production and Operations Systems (PacPos), Northwestern Polytechnical University, Xi’an 710072, China;2. Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an 710072, China;3. HKU-ZIRI Lab for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, China;4. School of Industrial Engineering, Eindhoven University of Technology, The Netherlands;1. Jinan University, Institute of Physical Internet, School of Electrical and Information Engineering, Jinan University (Zhuhai Campus), 519070, Zhuhai, PR China;2. Lancaster University, Department of Management Science, Lancaster University Management School, Lancaster University, LA1 4YX, UK;1. Dept. of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA;2. Dept. of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA;3. Dept. of Industrial & Systems Engineering, Rutgers University, Piscataway, NJ, USA |
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| Abstract: | Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are usually non-static (shifting) among stations during production, which requires dynamic bottleneck detection methods. An effective methodology requires proper handling of real-time production data and integration of factory physics knowledge. Traditional data-driven bottleneck detection methods only focus on serial production lines, while most production lines are complex. With careful revision and examination, those methods can hardly meet practical industrial needs. Therefore, this paper proposes a systematic approach for bottleneck detection for complex manufacturing systems with non-serial configurations. It extends a well-recognized bottleneck detection algorithm, “the Turning Point Method”, to complex manufacturing systems by evaluating and proposing appropriate heuristic rules. Several common industrial scenarios are evaluated and addressed in this paper, including closed loop structures, parallel line structures, and rework loop structures. The proposed methodology is demonstrated with a one-year pilot study at an automotive powertrain assembly line with complex manufacturing layouts. The result has shown a successful implementation that greatly improved the bottleneck detection capabilities for this manufacturing system and led to an over 30% gain in Overall Equipment Effectiveness (OEE). |
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| Keywords: | Bottleneck detection Throughput improvement Complex manufacturing systems Smart manufacturing Industry 4.0 |
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