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Predictive analytics for truck arrival time estimation: a field study at a European distribution centre

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  • Sjoerd van der Spoel
  • Chintan Amrit
  • Jos van Hillegersberg

Abstract

Distribution centres (DCs) are the hubs connecting transport streams in the supply chain. The synchronisation of coming and going cargo at a DC requires reliable arrival times. To achieve this, a reliable method to predict arrival times is needed. A literature review was performed to find the factors that are reported to predict arrival time: congestion, weather, time of day and incidents. While travel time receives considerable attention, there is a gap in literature concerning arrival vs. travel/journey time prediction. None of the reviewed papers investigate arrival time: all the papers found investigate travel time. Arrival time is the consequence of travel time in combination with departure time, so though the travel time literature is applicable, the human factor involved in planning the time of departure can affect the arrival time (especially for truck drivers who have travelled the same route before). To validate the factors that influence arrival time, the authors conducted a detailed case study that includes a survey of 230 truckers, a data analysis and a data mining experiment, using real traffic and weather data. These show that although a ‘big data’ approach delivers valuable insights, the predictive power is not as high as expected; other factors, such as human or organisational factors, could influence arrival time, and it is concluded that such organisational factors should be considered in future predictive models.

Suggested Citation

  • Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5062-5078
    DOI: 10.1080/00207543.2015.1064183
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    Cited by:

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    3. Sena Aydoğan & Gül E. Okudan Kremer & Diyar Akay, 2021. "Linguistic summarization to support supply network decisions," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1573-1586, August.
    4. A. V. Thomas & Biswajit Mahanty, 2021. "Dynamic assessment of control system designs of information shared supply chain network experiencing supplier disruption," Operational Research, Springer, vol. 21(1), pages 425-451, March.
    5. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).

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