Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics
This work addresses transport risk assessment for cargo logistics forwarders and airlines, offering tools for customized pricing and supplier evaluation, but it is incremental as it applies an existing Bayesian nonparametric method to a new domain-specific dataset.
The paper tackled the problem of assessing and forecasting transport risk in cargo logistics, which is the deviation of actual from planned arrival times, by introducing a Bayesian nonparametric model (PSBP mixture) to handle multimodal data features, and demonstrated that simpler methods like OLS regression can lead to misleading inferences.
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.