Efficient management of spare parts inventory is crucial in the automotive aftermarket, where demand is highly intermittent and uncertainty drives substantial cost and service risks. Forecasting is therefore central, but the quality of forecasting models should be judged not by statistical accuracy (e.g., MAE, RMSE) but rather by its impact on key operational performance indicators (KPIs), such as total cost and service level. Yet most existing work evaluates models exclusively using accuracy metrics, and the relationship between these metrics and KPIs remains poorly understood. To address this gap, we propose a decision-centric simulation software framework that enables systematic evaluation of forecasting models in realistic inventory management setting. The framework comprises: (i) a synthetic demand generator tailored to spare-parts demand characteristics, (ii) a flexible forecasting module that can host arbitrary predictive models, and (iii) an inventory control simulator that consumes the forecasts and computes operational KPIs. This closed-loop setup enables researchers to evaluate models not only in terms of statistical error but also in terms of downstream inventory implications. Using a wide range of simulation scenarios, we show that improvements in accuracy metrics do not necessarily lead to better KPIs, and that models with similar error profiles can induce different cost-service trade-offs. We analyze these discrepancies to characterize how forecast performance affects inventory outcomes and derive guidance for model selection. Overall, the framework links demand forecasting and inventory management, shifting evaluation from predictive accuracy toward operational relevance in the automotive aftermarket and related domains. An open-source implementation of the software is available at https://github.com/caisr-hh/TruckParts-Demand-Inventory-Simulator/releases/tag/IDA_2026.

翻译：在汽车售后市场中，备件需求具有高度间歇性，不确定性带来了巨大的成本和服务风险，因此高效的备件库存管理至关重要。预测在其中处于核心地位，但预测模型的质量不应通过统计准确性（如平均绝对误差、均方根误差）来评判，而应通过其对关键运营绩效指标（如总成本和服务水平）的影响来衡量。然而，现有研究大多仅使用准确性指标评估模型，而这些指标与关键绩效指标之间的关系仍不甚明了。为填补这一空白，我们提出了一种以决策为中心的仿真软件框架，能够在现实的库存管理环境中系统性地评估预测模型。该框架包含：（i）一个针对备件需求特性定制的合成需求生成器；（ii）一个可容纳任意预测模型的灵活预测模块；以及（iii）一个消耗预测结果并计算运营关键绩效指标的库存控制模拟器。这种闭环设置使研究人员不仅能够从统计误差角度，还能从下游库存影响角度评估模型。通过大量仿真场景，我们表明准确性指标的提升并不必然带来更好的关键绩效指标，并且具有相似误差特征的模型可能导致不同的成本-服务权衡。我们分析了这些差异，以刻画预测性能如何影响库存结果，并得出模型选择的指导原则。总体而言，该框架将需求预测与库存管理联系起来，将评估重点从预测准确性转向汽车售后市场及相关领域的运营相关性。该软件的开源实现可在 https://github.com/caisr-hh/TruckParts-Demand-Inventory-Simulator/releases/tag/IDA_2026 获取。