提升供应链韧性：一种在中断情境下预测产品到货日期的机器学习方法
Enhancing Supply Chain Resilience: A Machine Learning Approach for Predicting Product Availability Dates Under Disruption
AI总结：
针对供应链中断下产品到货日期不确定的问题，结合GE燃气电力部门真实货运数据，测试多种回归模型后发现树基算法RF与GBM预测精度最优，可助力企业降本控险。
摘要：
新冠疫情以及持续的政治与地区冲突对全球供应链造成了极为不利的影响，导致物流运营和国际货运出现严重延误。最紧迫的问题之一是产品到货日期的不确定性，而这一信息是企业制定有效物流与货运计划的关键。因此，准确预测到货日期对于成功开展物流运营、最终将总运输与库存成本降至最低至关重要。我们针对通用电气（GE）燃气电力部门用于燃气轮机和蒸汽轮机维护及制造业务的 inbound shipments（入站货运），结合数值型与类别型特征，开展了产品到货日期预测研究。我们评估了多种回归模型，包括 Simple Regression（简单回归）、Lasso Regression（套索回归）、Ridge Regression（岭回归）、Elastic Net（弹性网络）、Random Forest（RF，随机森林）、Gradient Boosting Machine（GBM，梯度提升机）以及神经网络模型。基于真实世界数据的实验表明，基于树的算法（即RF和GBM）取得了最优的泛化误差，表现优于所有其他受测回归模型。我们预计，这些预测模型将在更广泛的层面上帮助企业应对供应链中断、降低供应链风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
