Parcel loss prediction in last-mile delivery: deep and non-deep
  approaches with insights from Explainable AI
Jan de Leeuw Zaharah Bukhsh Yingqian Zhang
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.