Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics

TL;DR

This paper develops a Bayesian nonparametric model to accurately assess and forecast transport risks in cargo logistics, capturing complex multimodal distributions driven by unobserved events, and enabling better decision-making and risk management.

Contribution

It introduces the probit stick-breaking process mixture model for flexible, state-dependent density estimation of transport risks, addressing limitations of simpler methods like OLS.

Findings

Model captures multimodal risk distributions effectively.

Enables customized pricing and performance evaluation.

Separates recurrent and disruption risks for better management.

Abstract

In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for customer and freight forwarders. In this paper, we investigate ways to assess and forecast transport risks using a half-year of air cargo data, provided by a leading forwarder on 1336 routes served by 20 airlines. Interestingly, our preliminary data analysis shows a strong multimodal feature in the transport risks, driven by unobserved events, such as cargo missing flights. To accommodate this feature, we introduce a Bayesian nonparametric model -- the probit stick-breaking process (PSBP) mixture model -- for flexible estimation of the conditional (i.e., state-dependent) density function of transport risk. We demonstrate that using simpler methods, such as OLS linear regression, can lead to misleading inferences. Our model provides a tool for the forwarder to offer customized price and service quotes. It can also generate baseline airline performance to enable fair supplier evaluation. Furthermore, the method allows us to separate recurrent risks from disruption risks. This is important, because hedging strategies for these two kinds of risks are often drastically different.