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 a forecasting model should be judged not by statistical accuracy (e.g., MAE, RMSE, IAE) 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 operational KPIs remains poorly understood. To address this gap, we propose a decision-centric simulation software framework that enables systematic evaluation of forecasting model 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 their downstream implications for inventory decisions. Using a wide range of simulation scenarios, we show that improvements in conventional accuracy metrics do not necessarily translate into better operational performance, and that models with similar statistical error profiles can induce markedly different cost-service trade-offs. We analyze these discrepancies to characterize how specific aspects of forecast performance affect inventory outcomes and derive guidance for model selection. Overall, the framework operationalizes the link between demand forecasting and inventory management, shifting evaluation from purely predictive accuracy toward operational relevance in the automotive aftermarket and related domains.

翻译：在汽车售后市场中，备件库存的高效管理至关重要，该领域需求高度间歇且不确定性带来巨大的成本与服务风险。因此，预测处于核心地位，但预测模型的质量不应通过统计精度（如平均绝对误差、均方根误差、绝对误差积分）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精度指标评估模型，而这些指标与运营关键绩效指标之间的关系仍鲜为人知。为填补这一空白，我们提出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评估预测模型。该框架包含：（i）针对备件需求特征定制的合成需求生成器，（ii）可容纳任意预测模型的灵活预测模块，以及（iii）利用预测结果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者不仅能够从统计误差角度评估模型，还能分析其对库存决策的下游影响。通过大量仿真场景，我们证明传统精度指标的提升未必转化为更好的运营绩效，且具有相似统计误差特征的模型可能导致显著不同的成本-服务权衡。我们分析这些差异以刻画预测性能的具体方面如何影响库存结果，并推导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之间的关联操作化，将评估重点从纯粹的预测精度转向汽车售后市场及相关领域的运营相关性。