Stockouts of repairable spares usually lead to significant downtime costs. Managers of Maintenance Repair Organizations (MROs) seek advance indicators of future stockouts which might allow them to take proactive actions that are beneficial for achieving target service levels with reasonable costs. Among such (proactive) actions, the most common, and the cheapest one is expediting existing repair processes.

In this study, we develop an advance stockout risk estimation system for repairable spare parts. To the best of our knowledge, this is the first study to estimate the future stockout risk of a repairable part. The method considers different statistics, e.g. the number of ongoing repair processes, demand rate, repair time, etc. to estimate stockout risk of a repairable part for a given planning horizon. In our field tests with empirical data, the suggested method overperforms two heuristic approaches and achieves accuracy rates of 63% for 15 day-planning horizon and 83% for 45 days.

We also suggest a repairable inventory control system including repair expediting, inspection and condemnation processes. To optimize the control parameters we suggest a simple algorithm considering two constraints: Target service level and maximum fraction of expedited demand. The algorithm is proved to be efficient for finding the optimum policy parameter in our tests with empirical data. Tests with empirical data suggest savings up to 8%. Both systems are implemented at an MRO as building blocks of a inventory control tower. The impact of the implementation is assessed with empirical simulations and verified from the financial indicators of the company.

可维修备件的缺货通常会导致重大的停机成本。维护维修组织 (MRO) 的经理寻求未来缺货的预先指标，这可能使他们能够采取有利于以合理成本实现目标服务水平的主动行动。在此类（主动）行动中，最常见且成本最低的行动是加快现有的维修流程。

在这项研究中，我们开发了一个可维修备件的提前缺货风险评估系统。据我们所知，这是第一项估计可修复部件未来缺货风险的研究。该方法考虑不同的统计数据，例如正在进行的维修过程的数量、需求率、维修时间等，以估计给定计划范围内可维修部件的缺货风险。在我们使用经验数据的现场测试中，建议的方法优于两种启发式方法，并且在 15 天的计划范围内实现了 63% 的准确率，在 45 天的时间内达到了 83%。

我们还建议一个可修复的库存控制系统，包括修复加速、检查和报废过程。为了优化控制参数，我们建议采用一种简单算法，考虑两个约束条件：目标服务水平和最大加速需求部分。在我们的经验数据测试中，该算法被证明可以有效地找到最佳策略参数。使用经验数据进行的测试表明节省高达 8%。这两个系统都在 MRO 实施，作为库存控制塔的构建块。实施的影响通过经验模拟进行评估，并从公司的财务指标进行验证。