Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance capacity planning of complex product systems (CoPS), intended to value data that is routinely generated and stored, but often disregarded by MROs. State space formulations with multiplicative errors for the simple exponential smoothing (SES), Holt’s linear method (HLM), additive Holt-Winters (AHW), and multiplicative Holt-Winters (MHW) are assessed using real data, comprised of 171 maintenance projects collected from a major Portuguese aircraft MRO. A state space formulation of the MHW, selected using the bias-corrected Akaike information criterion (AICc), is integrated in a Decision Support System (DSS) for capacity planning with probabilistic inference capabilities and used to forecast the workload probability distribution of a future and unprecedent maintenance intervention. The developed tool is validated by comparing forecasted values with workloads of a particular maintenance intervention and with a model simulating current forecasting practices employed by MROs.

尽管在维护能力规划过程中生成和存储了大量数据，但维护、修理和大修(MRO) 组织尚未充分发挥其在预测未来和前所未有的维护干预所需能力方面的潜力。本文探讨了时间序列预测功能在复杂产品系统 (CoPS) 维护能力规划工具中的集成，旨在评估日常生成和存储但经常被 MRO 忽视的数据。用于简单指数平滑(SES)、Holt 线性方法(HLM)、加性 Holt-Winters (AHW) 和乘法 Holt-Winters (MHW) 使用真实数据进行评估，包括从葡萄牙主要飞机 MRO 收集的 171 个维护项目。使用偏差校正Akaike 信息准则(AICc)选择的 MHW 状态空间公式被集成到决策支持系统(DSS) 中，用于具有概率推理能力的容量规划，并用于预测未来和前所未有的维护干预。通过将预测值与特定维护干预的工作量以及模拟 MRO 所采用的当前预测实践的模型进行比较，对开发的工具进行了验证。