Forecasting the load of Parcel Pickup Points using a Markov Jump Process

TL;DR

This paper introduces a Markov jump process model to forecast parcel pickup point loads, helping manage parcel flows and reduce overload issues in e-commerce logistics.

Contribution

It presents a novel stochastic modeling approach that accounts for parcel lifecycle variability and delays, improving load prediction accuracy for PUP management.

Findings

Effective load prediction for B2C parcel flows.

Model accounts for parcel delays and activity variability.

Applicable to various parcel flow types.

Abstract

The growth of e-commerce has resulted in a surge in parcel deliveries, increasing transportation costs and pollution issues. Alternatives to home delivery have emerged, such as the delivery to so-called parcel pick-up points (PUPs), which eliminates delivery failure due to customers not being at home. Nevertheless, parcels reaching overloaded PUPs may need to be redirected to alternative PUPs, sometimes far from the chosen ones, which may generate customer dissatisfaction. Consequently, predicting the PUP load is critical for a PUP management company to infer the availability of PUPs for future orders and better balance parcel flows between PUPs. This paper proposes a new approach to forecasting the PUP load evolution using a Markov jump process that models the parcel life cycle. The latest known status of each parcel is considered to estimate its contribution to the future load of its target PUP. This approach can account for the variability of activity, the various parcel preparation delays by sellers, and the diversity of parcel carriers that may result in different delivery delays. Here, results are provided for predicting the load associated with parcels ordered from online retailers by customers (Business-to-Customer, B2C). The proposed approach is generic and can also be applied to other parcel flows to PUPs, such as second-hand products (Customer-to-Customer, C2C) sent via a PUP network.