The COVID 19 pandemic and ongoing political and regional conflicts have a highly detrimental impact on the global supply chain, causing significant delays in logistics operations and international shipments. One of the most pressing concerns is the uncertainty surrounding the availability dates of products, which is critical information for companies to generate effective logistics and shipment plans. Therefore, accurately predicting availability dates plays a pivotal role in executing successful logistics operations, ultimately minimizing total transportation and inventory costs. We investigate the prediction of product availability dates for General Electric (GE) Gas Power's inbound shipments for gas and steam turbine service and manufacturing operations, utilizing both numerical and categorical features. We evaluate several regression models, including Simple Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network models. Based on real world data, our experiments demonstrate that the tree based algorithms (i.e., RF and GBM) provide the best generalization error and outperforms all other regression models tested. We anticipate that our prediction models will assist companies in managing supply chain disruptions and reducing supply chain risks on a broader scale.

翻译：新冠疫情以及持续的政治和地区冲突对全球供应链造成了严重破坏，导致物流作业和国际运输面临显著延误。其中最紧迫的问题之一是产品可用日期的不确定性，而这一信息对于企业制定有效的物流和运输计划至关重要。因此，准确预测产品可用日期在成功执行物流作业、最终最大化降低总运输和库存成本方面发挥着关键作用。我们研究了通用电气（GE）燃气发电业务中燃气和蒸汽轮机服务及制造运营的入站货品产品可用日期预测，并利用了数值型与类别型两种特征。我们评估了包括简单回归、Lasso回归、Ridge回归、弹性网络、随机森林（RF）、梯度提升机（GBM）以及神经网络模型在内的多种回归模型。基于真实世界数据的实验表明，基于树的算法（即 RF 和 GBM）提供了最佳的泛化误差，且其性能优于所有其他被测试的回归模型。我们预期，我们的预测模型将在更广泛的范围内帮助企业管理供应链中断并降低供应链风险。