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Exploiting big data in logistics risk assessment via Bayesian nonparametrics

Publication ,  Journal Article
Shang, Y; Dunson, D; Song, JS
Published in: Operations Research
November 1, 2017

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 di erent.

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Published In

Operations Research

DOI

EISSN

1526-5463

ISSN

0030-364X

Publication Date

November 1, 2017

Volume

65

Issue

6

Start / End Page

1574 / 1588

Related Subject Headings

  • Operations Research
  • 3507 Strategy, management and organisational behaviour
  • 1503 Business and Management
  • 0802 Computation Theory and Mathematics
  • 0102 Applied Mathematics
 

Citation

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Shang, Y., Dunson, D., & Song, J. S. (2017). Exploiting big data in logistics risk assessment via Bayesian nonparametrics. Operations Research, 65(6), 1574–1588. https://doi.org/10.1287/opre.2017.1612
Shang, Y., D. Dunson, and J. S. Song. “Exploiting big data in logistics risk assessment via Bayesian nonparametrics.” Operations Research 65, no. 6 (November 1, 2017): 1574–88. https://doi.org/10.1287/opre.2017.1612.
Shang Y, Dunson D, Song JS. Exploiting big data in logistics risk assessment via Bayesian nonparametrics. Operations Research. 2017 Nov 1;65(6):1574–88.
Shang, Y., et al. “Exploiting big data in logistics risk assessment via Bayesian nonparametrics.” Operations Research, vol. 65, no. 6, Nov. 2017, pp. 1574–88. Scopus, doi:10.1287/opre.2017.1612.
Shang Y, Dunson D, Song JS. Exploiting big data in logistics risk assessment via Bayesian nonparametrics. Operations Research. 2017 Nov 1;65(6):1574–1588.

Published In

Operations Research

DOI

EISSN

1526-5463

ISSN

0030-364X

Publication Date

November 1, 2017

Volume

65

Issue

6

Start / End Page

1574 / 1588

Related Subject Headings

  • Operations Research
  • 3507 Strategy, management and organisational behaviour
  • 1503 Business and Management
  • 0802 Computation Theory and Mathematics
  • 0102 Applied Mathematics