在劳动密集型制造业中,利用工人位置数据为人为驱动的决策支持
Ayse Aslan1, Hanane El-Raoui2, Jack Hanson3
1The School of Computing, Engineering and The Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种用于优化制造业工人能力的新方法. 通过分析真实的工作数据,它展示了小调整如何在不增加员工的情况下显著缩短生产时间.
科学领域:
- 工业工程 工业工程 工业工程
- 运营研究 运营研究
- 制造系统制造系统的制造
背景情况:
- 劳动密集型制造系统在很大程度上依赖于人类工人,使他们的实际工作实践对提高生产率至关重要.
- 基于理论模型实施变化可能是无效的;对实际工人行为的数据驱动洞察力至关重要.
- 在这些系统中,容量分配需要一种反映实际运营动态的方法.
研究的目的:
- 为劳动密集型制造业的产能分配提供以人为驱动的决策支持提出一种新的方法.
- 利用工人位置数据和过程挖掘来创建数据驱动的过程模型.
- 利用离散事件模拟来评估基于观察到的工作实践的容量分配调整.
主要方法:
- 使用定位传感器收集工人位置数据.
- 应用过程挖掘算法来生成制造任务的数据驱动过程模型.
- 在数据驱动过程模型的基础上构建一个离散事件模拟模型.
- 调查容量分配调整对系统性能的影响.
主要成果:
- 在没有额外的工人的情况下,通过轻微的容量调整实现了7%的完成时间缩短.
- 通过增加一名工人和优化瓶任务,完成时间减少了16%.
- 该方法证明了数据驱动模拟在手动装配线上的容量分配方面的有效性.
结论:
- 工人位置数据和过程挖掘为实际制造业务提供了宝贵的见解.
- 以真实数据为基础的离散事件模拟对于优化容量分配是有效的.
- 有针对性的产能调整,特别是在瓶任务中,可以显著提高制造效率并缩短完成时间.
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