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Mining Customer-related Data to Enhance Home Delivery in E-commerce: an experimental study

Author

Listed:
  • Shenle Pan

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Han Yufei
  • Bin Qiao

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Etta Grover-Silva

    (PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris sciences et lettres)

  • Vaggelis Giannikas

    (Institute for Manufacturing - CAM - University of Cambridge [UK])

Abstract

In a B2C e-commerce environment, home delivery service refers to delivering goods from an e-retailer's storage point to a customer's home. High rate of failed delivery due to the customer's absence causes significant loss of logistics efficiency. This paper aims to study innovative solutions to the problem, such as data-related techniques. This paper proposes a methodological approach to use customer-related data to optimize home delivery. The idea is to estimate the attendance probability of a customer via mining his electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Computational experiments reveal that the proposed approach could reduce the total distance from 3% to 20%, and theoretically increase the success rate around 18%-26%. Being an experimental study, this paper demonstrates the effectiveness of data-related techniques or data-based solutions in home delivery problem, and provides a methodological approach to this line of research.

Suggested Citation

  • Shenle Pan & Han Yufei & Bin Qiao & Etta Grover-Silva & Vaggelis Giannikas, 2016. "Mining Customer-related Data to Enhance Home Delivery in E-commerce: an experimental study," Post-Print hal-01320962, HAL.
    • Handle: RePEc:hal:journl:hal-01320962
    as

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