Decision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

中文翻译：

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贝叶斯退化模型用于备件库存管理

制造和运输等各个部门的决策者都在努力减少停机成本。通常，简短计划的停工时间可以更改班次和生产线配置，并安排较大的维修时间。主动利用这些停机时间来减少由于故障导致的意外停机的成本非常重要。在许多方面，备件的可用性大大影响了此类系统的运营成本。当前的传感器技术可实现对关键组件的状态监视以及基于降级的备件管理。本文主要针对用于新系统的备件库存管理的贝叶斯退化模型。我们提出了一个随机动态计划，以最大程度地减少固定计划范围内的预期备件库存成本。一个数字示例说明了此管理设置中的贝叶斯分析的价值。当关于降解过程的先验信息准确时，所提出的方法可以找到长时间停工和最佳备件订购量之间的最佳时间。该方法可用于分析最佳解决方案对模型参数，成本结构和系统中机器数量的先验分布的准确性和偏差的敏感性。