Data-driven process redesign: anticipatory shipping in agro-food supply chains

Nguyen Quoc Viet*, Behzad Behdani, Jacqueline Bloemhof

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)


Anticipatory shipping uses historical order and customer data to predict future orders and accordingly ship products to the nearest distribution centres before customers actually place the orders. It is a method to meet the increasing customer requirements on delivery service and simultaneously to reduce operational costs. This paper presents a case of anticipatory shipping in the context of agro-food supply chains. The challenge in these chains is the product perishability that leads to product obsolescence in the case of un-balanced supply and demand. This study introduces a data-driven approach that integrates product quality characteristics in data analytics to identify suitable products for anticipatory shipping at the strategic level. It also proposes process redesigns concerning production and transportation at the operational level to realise anticipatory shipping. Finally, using historical data from a Dutch floriculture supplier as input for a multi-agent simulation, the proposed approach and process redesigns are verified. The simulation output shows that anticipatory shipping could increase delivery service level up to 35.3% and reduce associated costs up to 9.3%.

Original languageEnglish
JournalInternational Journal of Production Research
Issue number5
Early online date17 Jun 2019
Publication statusPublished - May 2020


  • agro-food
  • anticipatory shipping
  • association rule mining
  • multi-agent simulation
  • perishable
  • process redesign

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