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Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics

dc.contributor.advisor Dunson, David B Shang, Yan 2015-01-28T18:11:14Z 2015-01-28T18:11:14Z 2014
dc.description.abstract <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>
dc.subject Statistics
dc.subject Transportation planning
dc.subject Business
dc.subject Bayesian statistics
dc.subject big data
dc.subject disruptions and risks
dc.subject empirical
dc.subject nonparametric
dc.subject probit stick-breaking mixture model
dc.title Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics
dc.type Master's thesis
dc.department Statistical Science

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