Application of a novel approach of production system modelling, analysis and improvement for small and medium-sized manufacturers: a case study
Application of a novel approach of production system modelling, analysis and improvement for small and medium-sized manufacturers: a case study
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DOI：
10.1080/00207543.2022.2079015
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发表时间：
2022-06
影响因子：
9.2
通讯作者：
Yuting Sun;Liang Zhang
中科院分区：
文献类型：
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作者：
Yuting Sun;Liang Zhang
随着工业4.0的新进展带来的巨大机遇，许多制造商正在测试和投资新设备和基础设施，以部署这些技术。然而，由于缺乏内部研发能力和劳动力短缺和/或财政限制，大量中小型制造商（SMM）落后于此。此外，理论生产研究在SMM中的应用经常面临数据可用性低和数据质量低等挑战。在本文中，我们描述了一个案例研究，在一个本地中型制造商的机电设备的工业，消费者和医疗应用，谁是努力满足不断增长的市场需求，并应用生产系统建模的新方法来克服操作启动和停机数据不可用的挑战。具体而言，生产系统的参数化模型是使用基于过程中缓冲区的零件流数据导出的几个系统性能度量来识别的。在建立数学模型的基础上，分析了系统的瓶颈，并提出了一些可能提高系统吞吐量的改进方案。最后，通过计算模型预测的性能指标与参考标称模型产生的性能指标的偏差来分析模型灵敏度。该分析表明，使用我们提出的方法构建的模型是鲁棒的，即使系统参数从基线的变化。
ABSTRACT With the great opportunities created by the new advances in Industry 4.0, many manufacturers are testing and investing in new equipment and infrastructure to deploy these technologies. However, there are a huge number of small and medium-sized manufacturers (SMMs) that are lagging behind due to the lack of in-house R&D capabilities and workforce shortage and/or financial constraints to afford such investment. Additionally, application of theoretical production research in SMMs often confront challenges such as low data availability and data quality, etc. In this paper, we describe a case study at a local medium-sized manufacturer of electromechanical devices for industrial, consumer, and medical applications, who was struggling to meet ever-growing market demand, and apply a novel approach of production system modelling to overcome the challenge of unavailability of the operation up- and downtime data. Specifically, the parametric model of the production system is identified using several system performance metrics derived based on the parts flow data of the in-process buffer. With the mathematical model constructed, the system bottleneck is analysed and a number of improvement scenarios are explored that can potentially enhance the system throughput. Finally, model sensitivity is analysed by calculating the deviation of the model-predicted performance metrics to those produced by a reference nominal model. This analysis demonstrates that the model constructed using our proposed approach is robust even when the system parameters vary from the baseline ones.