发表机构
Queen’s University; Kinaxis Inc.（女王大学; Kinaxis公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究供应链规划中供应商提前期预测问题，提出审查感知上下文学习模型LeadTime-ICL，结合Transformer与条件归一化流头，经预训练可适应新数据集，在多数据集上评估表现优异，为工业规划提供准确低成本预测。
AI 中文摘要
供应商提前期预测是物料需求计划、库存优化和供应链风险管理的核心输入。然而，许多行业提前期数据集自然存在右删失情况。标准回归和分类方法会丢弃此信息，传统生存模型需要特定任务建模。我们提出了LeadTime-ICL（LT-ICL），一种用于概率提前期预测的审查感知上下文学习模型。它将Transformer主干与条件归一化流头相结合，在合成右删失提前期任务上预训练，能在无特定任务参数更新的情况下适应新行业数据集。我们给出了理论支持，通过在24个专有供应链数据集上评估，LT-ICL在多个指标上表现最佳，支持右删失概率预测，证明预训练上下文模型可为工业规划系统提供准确且低适应成本的预测。
英文摘要
Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management. However, many industrial lead time datasets are naturally right-censored: at the time forecasts are required, some orders have not yet arrived. Standard regression and classification approaches discard this information, while conventional survival models require task-specific modeling. We propose LeadTime-ICL (LT-ICL), a censoring-aware in-context learning model for probabilistic lead time forecasting. LT-ICL combines a transformer backbone with a conditional normalizing-flow head, producing a full predictive distribution over lead times. The model is pretrained on synthetic right-censored lead time tasks, enabling in-context adaptation to new industrial datasets without task-specific parameter updates. We provide theoretical support for this formulation by showing that excess CRPS is bounded by prior misspecification and amortized approximation errors, providing clear direction for improving forecasting performance. We evaluate LT-ICL on 24 proprietary supply-chain datasets spanning seven industries. LT-ICL achieves the lowest point-forecasting error on 15 of the 24 datasets, and the lowest probabilistic forecasting error on 14 datasets, yielding the best average rank across both. These results support right-censored probabilistic forecasting as a practical formulation for supplier lead time prediction and demonstrate that pretrained in-context models can provide accurate, low-adaptation-cost forecasting for industrial planning systems.
