Bayesian Inventory and Replacement Models Using Real-Time Condition Monitoring Information

使用实时状态监测信息的贝叶斯库存和更换模型该赠款提供资金，用于开发将通过状态监测(CM)获得的实时传感器信息纳入备件库存管理和更换决策的方法，目的是改善备件库存的管理。这项研究考虑了制造商管理机器零件库存所面临的问题，该机器零件在大量地理分散的机器上使用，并且容易变质。通过CM捕获的由单个部件的劣化产生的劣化信号使用维纳过程建模。当获得新的传感器信息时，该退化模型可用于预测部件故障以及对维修部件的需求，并定期更新。建立了包含更新需求分布的随机库存模型，研究了最优库存控制策略的形式。虽然自适应库存策略(如本研究中开发的那些策略)可以帮助制造商提高机器可用性并降低库存成本，但其实施可能需要大量的计算，并且可能需要保留大量的传感器数据。因此，这项研究将集中于开发利用现有传感器信息的计算易处理的方法，如果成功，本研究将开发可实施的和广泛适用的工具，这些工具将对制造企业具有实用价值，帮助他们利用CM技术来改善他们的售后服务，更有效地竞争。这项研究还试图促进在状态监测技术、部件维护和更换以及库存管理之间建立必要的联系。这项研究确定了跨学科合作的关键领域，寻求在CM技术研究和利用CM获得的信息开发决策模型的研究之间建立联系。

Bayesian Inventory and Replacement Models Using Real-Time Condition Monitoring InformationAbstractThis grant provides funding to develop methods for incorporating real-time sensor information obtained through condition monitoring (CM) into inventory management and replacement decisions for service parts, with the goal of improving the management of service parts inventories. This research considers the problem faced by a manufacturer who manages inventory for a machine part that is used on a large number of geographically-dispersed machines and which is subject to deterioration. The degradation signal generated by the deterioration of an individual part, and captured via CM, is modeled using a Wiener process. This degradation model, which can be used to predict part failure, as well as demand for service parts, is periodically updated as new sensor information is obtained. A stochastic inventory model that incorporates the updated demand distribution will be developed and the form of the optimal inventory control policy will be studied. While adaptive inventory policies such as those developed in this research can help manufacturers to increase machine availability and reduce inventory costs, their implementation can be computationally intensive and may require the retention of a significant quantity of sensor data. Thus, this research will focus on the development of computationally tractable methods that take advantage of the available sensor information.If successful, this research will develop implementable and broadly applicable tools that will be of practical value to manufacturing firms, assisting them in taking advantage of CM technology to improve their after-sales service and to compete more effectively. This research also seeks to contribute to the development of essential linkages between condition monitoring technology, part maintenance and replacement, and inventory management. This research identifies a critical area for interdisciplinary collaboration, seeking to build connections between research on CM technology and research on the development of decision models that make use of information obtained through CM.