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PROCESS TRANSPARENCY: EFFECTS OF A STRUCTURED READ POINT SELECTION

机译:过程透明度:结构化读点选择的效果

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Modern supply chains, especially in the automotive industry, are prone to events that endanger their ability to deliver products on time. Comprehensive real-time tracking and tracing systems (e.g. RFID-based) can help in making them more robust by identifying events and enabling early responses that are capable of mitigating adverse effects. However, these systems are rarely realized and read points only placed in a few default locations (e.g. receiving and shipping) without further considerations. In this contribution weighted linear optimization models are used to assess the effect of including process characteristics such as variability and cycle times in this decision problem. Using an intuitive simulation setup in which the degree of transparency and system sensitivity are varied, the results indicate that locating read points in sensible places along a process rather than by chance can reduce false alarms by 31% while raising successful identifications by 2%.
机译:现代供应链,特别是在汽车行业,易于危害他们按时提供产品的能力的事件。全面的实时跟踪和跟踪系统(例如,RFID的)可以帮助通过识别事件并实现能够减轻不利影响的早期响应来使其更加强大。然而,这些系统很少实现,并且仅在少数默认位置(例如接收和运输)中读取点而无需进一步的考虑。在该贡献中,加权线性优化模型用于评估包括该决策问题中的过程特征的效果,例如可变性和循环时间。使用直观的仿真设置,其中透明度和系统灵敏度的变化程度,结果表明,沿着过程的读取点定位在一个过程中,而不是偶然可以减少31%的误报,同时提高成功标识为2%。

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