Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase. The ever-increasing availability of data, including product, customer, and order information, has made it possible for the application of machine learning in parcel loss prediction. However, a significant challenge arises from the inherent imbalance in the data, i.e., only a very low percentage of parcels are lost. In this paper, we propose two machine learning approaches, namely, Data Balance with Supervised Learning (DBSL) and Deep Hybrid Ensemble Learning (DHEL), to accurately predict parcel loss. The practical implication of such predictions is their value in aiding e-commerce retailers in optimizing insurance-related decision-making policies. We conduct a comprehensive evaluation of the proposed machine learning models using one year data from Belgian shipments. The findings show that the DHEL model, which combines a feed-forward autoencoder with a random forest, achieves the highest classification performance. Furthermore, we use the techniques from Explainable AI (XAI) to illustrate how prediction models can be used in enhancing business processes and augmenting the overall value proposition for e-commerce retailers in the last mile delivery.

翻译：在电子商务零售领域，一个重要目标是在最后一英里配送阶段减少包裹损失。随着产品、客户和订单信息等数据的日益丰富，应用机器学习进行包裹损失预测已成为可能。然而，数据固有的不平衡性（即只有极低比例的包裹会丢失）带来了重大挑战。本文提出两种机器学习方法——数据平衡监督学习（DBSL）与深度混合集成学习（DHEL），以准确预测包裹损失。此类预测的实际意义在于帮助电商零售商优化保险相关决策策略。我们利用比利时一年期的货运数据对所提出的机器学习模型进行了全面评估。结果表明，结合前馈自编码器与随机森林的DHEL模型取得了最高分类性能。此外，我们采用可解释人工智能（XAI）技术，阐释预测模型如何用于优化业务流程并提升电商零售商在最后一英里配送中的整体价值主张。