This paper proposes a decision support tool for maintenance capacity planning of complex product systems. The tool – ForeSim-BI – addresses the problem faced by maintenance organizations in forecasting the workload of future maintenance interventions and in planning an adequate capacity to face that expected workload. Developed and implemented from a predictive analytics perspective in the particular context of a Portuguese aircraft maintenance organization, the tool integrates four main modules: (1) a forecasting module used to predict future and unprecedented maintenance workloads from historical data; (2) a Bayesian inference module used to transform prior workload forecasts, resulting from the forecasting module, into predictive forecasts after observations on the maintenance interventions being predicted become available; (3) a simulation module used to characterize the forecasted total workloads through sets of random variables, including maintenance work types, maintenance work phases, and maintenance work skills; and (4) a Bayesian network module used to combine the simulated workloads with historical data through probabilistic inference. A linear programming model is also developed to improve the efficiency of the decision-making process supported by Bayesian networks. The tool uses real industrial data, comprising 171 aircraft maintenance projects collected at the host organization, and is validated by comparing its results with real observations of a given maintenance intervention to which predictions were made and with a model simulating current forecasting practices employed in industry. Significantly more accurate forecasts have been obtained with the proposed tool, resulting in an important cost saving potential for maintenance organizations.

本文提出了用于复杂产品系统维护能力计划的决策支持工具。工具– ForeSim-BI–解决维护组织在预测未来维护干预措施的工作量以及规划足够的能力来应对预期工作量方面面临的问题。该工具是根据葡萄牙航空器维修组织的特定情况从预测分析角度开发和实施的，它集成了四个主要模块：（1）预测模块，用于根据历史数据预测未来和空前的维护工作量；（2）贝叶斯推理模块，用于在获得对预测的维护干预的观察后，将由预测模块产生的先前的工作量预测转换为预测性预测；（3）一个模拟模块，用于通过一组随机变量（包括维护工作类型）来表征预测的总工作量，维护工作阶段和维护工作技能；（4）贝叶斯网络模块，用于通过概率推断将模拟的工作量与历史数据结合起来。还开发了线性规划模型以提高贝叶斯网络支持的决策过程的效率。该工具使用真实的工业数据，包括在东道国组织收集的171架飞机维护项目，并通过将其结果与对给定维护干预措施的实际观察结果进行比较进行了验证，并对该模型进行了仿真，该模型可以模拟行业中采用的当前预测实践。使用建议的工具已获得了更为准确的预测，从而为维护组织节省了重要的成本。（4）贝叶斯网络模块，用于通过概率推理将模拟的工作负载与历史数据结合起来。还开发了线性规划模型以提高贝叶斯网络支持的决策过程的效率。该工具使用真实的工业数据，包括在东道国组织收集的171架飞机维护项目，并通过将其结果与对给定维护干预措施的实际观察结果进行比较进行了验证，并对该模型进行了仿真，该模型可以模拟行业中采用的当前预测实践。使用该工具已获得了更为准确的预测，从而为维护组织节省了重要的成本。（4）贝叶斯网络模块，用于通过概率推理将模拟的工作负载与历史数据结合起来。还开发了线性规划模型以提高贝叶斯网络支持的决策过程的效率。该工具使用真实的工业数据，包括在东道国组织收集的171架飞机维护项目，并通过将其结果与对给定维护干预措施的实际观察结果进行比较进行了验证，并对该模型进行了仿真，该模型可以模拟行业中采用的当前预测实践。使用建议的工具已获得了更为准确的预测，从而为维护组织节省了重要的成本。