Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics

Yan Shang (), David Dunson () and Jing-Sheng Song ()
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Yan Shang: Facebook, Inc., Menlo Park, California 94025
David Dunson: Department of Statistical Science, Duke University, Durham, North Carolina 27710
Jing-Sheng Song: Fuqua School of Business, Duke University, Durham, North Carolina 27708

Operations Research, 2017, vol. 65, issue 6, 1574-1588

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 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.

Keywords: Bayesian statistics; big data; disruptions and risks; empirical; international air cargo logistics; nonparametric; probit stick-breaking mixture model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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