||<p>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 the customer and freight forwarder. 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.</p>