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Each of the companies on this year's Inc. 5000 has its own tale of persistence, perseverance, and pluck. Tales of traction. Tales of triumph. Tales of doing the work, no [...]

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 1,336 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 mixture model--for flexible estimation of the conditional (i.e., state-dependent) density function of transport risk. We demonstrate that using alternative methods 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. Funding: This research was supported, in part, by the National Natural Science Foundation of China [Grant 71390331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1612. Keywords: Bayesian statistics * big data * disruptions and risks * empirical * international air cargo logistics * nonparametric * probit stick-breaking mixture model, 1. Introduction Global trade has grown considerably in recent decades; many companies now have overseas facilities and supply chain partners. International cargo logistics management thus plays an increasingly important role [...]