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Predictive maintenance: strategic use of IT in manufacturing organizations

机译:预测维护:制造组织中的战略使用

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A combination of big data and predictive analytics orchestrated through the Internet of Things (IoT) offers many opportunities for researchers in Information Systems, Operations Management and Strategy to look at old problems in new ways, and to identify completely new research areas. While there is much hype, little research has been conducted that informs companies about how to profitably integrate the IoT with strategic or operational processes. This paper views the IoT through the lens of predictive maintenance -- the use of real-time data and predictive analytics algorithms to dynamically manage preventive maintenance policies. These are being used by numerous manufacturing organizations to transition from product-oriented to service-oriented business models. In particular, we analyze optimal preventive maintenance policies in an environment where equipment is subject to a deterioration, which shifts it from its initial, fully-productive state, having a specified, age-dependent failure rate to a less-productive or deteriorated state, having a different, presumably higher, age-dependent failure rate. The deterioration, itself, is a random process.
机译:通过互联网(IOT)策划的大数据和预测分析的组合为信息系统,运营管理和战略的研究人员提供了许多机会,以便以新的方式看待旧问题,并识别全新的研究领域。虽然有很多炒作,但对公司提供了很少的研究,这些研究是如何与战略或运营流程盈利地整合物联网。本文通过预测维护的镜头查看IOT - 使用实时数据和预测分析算法动态管理预防性维护策略。许多制造组织正在使用这些组织从以产品为导向到服务为导向的商业模式。特别是,我们在设备受到劣化的情况下分析最佳预防性维护政策,这将其从其初始,全生产状态转移,使指定的年龄依赖性失效率与较少生产或恶化的状态相移,具有不同,可能的更高,年龄依赖性的失败率。劣化本身是随机过程。

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