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A proactive event-driven decision model for joint equipment predictive maintenance and spare parts inventory optimization

机译：联合设备预测维护和备件库存优化的主动事件驱动决策模型

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Manufacturing operations can take substantial advantage of the proactivity concept by utilising event-driven information systems, able to process the sensor data and to provide proactive recommendations. Despite the recent advances in technology and information systems and the variety of methods for prognosis, decision models for joint maintenance and inventory optimization on the basis of real-time prognostic information have not been explored. We propose a proactive event-driven decision model for joint predictive maintenance and spare parts inventory optimization which addresses the Decide phase of the "Detect- Predict- Decide- Act" model and can be embedded to an Event Driven Architecture (EDA) for real-time processing in the frame of e-maintenance concept. The proposed approach was tested in a real manufacturing scenario in automotive lighting equipment industry and proved that maintenance and inventory costs can be significantly reduced by transforming the company's maintenance strategy from time-based to Condition Based Maintenance (CBM).
机译：制造操作可以通过利用事件驱动的信息系统来实现接受性概念的实质优势，能够处理传感器数据并提供主动性建议。尽管技术和信息系统最近进展以及预后的各种方法，但尚未探讨基于实时预后信息的联合维护和库存优化的决策模型。我们提出了一个主动事件驱动的决策模型，用于联合预测维护和备件库存优化，该零件库存优化解决了“检测预测决定”模型的决定阶段，并且可以嵌入到真实的事件驱动的架构（EDA）。电子维护概念框架中的时间处理。拟议的方法在汽车照明设备行业的实际制造场景中进行了测试，并证明了通过将公司的维护策略从基于时间的维护（CBM）转换为基于条件的维护（CBM），可以大大降低维护和库存成本。

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