The upcoming industrial revolution 4.0 built on the internet of things, and prescriptive analytics paves the way for the spread of continuously monitored condition-based maintenance (CBM) in the industry. In the CBM implementations, it is essential to consider the impact of spare parts quality, lead-time, and inspection errors on maintenance cost and system availability. We propose a new maintenance model that incorporates the effects of these features in addition to the different cost factors, e.g., replacement cost, holding cost, and shortage cost. In a case study, we optimize our model by deciding on the degradation level at which a spare part is ordered. We show that a proper inspection of spare parts pays back up to 22% in maintenance cost savings as the spare parts' quality deteriorates. The vendor mean lead-time, the offered spare part price, and the mean degradation per time unit significantly impact the optimal maintenance cost. Finally, the costly detection processes of defective items installed in the system due to inspection errors have a limited cost reduction.

即将到来的基于物联网的工业革命 4.0 和规范性分析为持续监控的基于状态的维护 (CBM) 在行业中的传播铺平了道路。在 CBM 实施中，必须考虑备件质量、交货时间和检查错误对维护成本和系统可用性的影响。我们提出了一种新的维护模型，除了不同的成本因素（例如，重置成本、持有成本和短缺成本）之外，还结合了这些特征的影响。在一个案例研究中，我们通过确定订购备件的降级水平来优化我们的模型。我们表明，随着备件质量下降，对备件进行适当检查可以节省高达 22% 的维护成本。供应商平均交货时间，提供的备件价格和每单位时间的平均降级显着影响最佳维护成本。最后，由于检查错误而导致安装在系统中的缺陷项目的昂贵检测过程的成本降低有限。