Using Customer-related Data to Enhance E-grocery Home Delivery

Abstract : Purpose: The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. This paper proposes an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Design/methodology/approach: The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. Findings: Computational experiments reveal that the proposed approach could reduce the total travel distance by 3% to 20%, and theoretically increase the success rate of first-round delivery approximately by18%-26%. Research limitations/implications: The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. Practical implications: This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. Social implications: The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping. Originality/value: Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper provides also a methodological approach to this line of research.
Type de document :
Article dans une revue
Liste complète des métadonnées

Littérature citée [51 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-01482901
Contributeur : Shenle Pan <>
Soumis le : mardi 7 mars 2017 - 10:18:01
Dernière modification le : vendredi 6 décembre 2019 - 10:26:54
Archivage à long terme le : jeudi 8 juin 2017 - 12:44:38

Fichier

IMDS_Manuscript_online.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Shenle Pan, Vaggelis Giannikas, Yufei Han, Etta Grover-Silva, Bin Qiao. Using Customer-related Data to Enhance E-grocery Home Delivery. Industrial Management and Data Systems, Emerald, 2017, 117 (9), pp.1917-1933. ⟨10.1108/IMDS-10-2016-0432⟩. ⟨hal-01482901⟩

Partager

Métriques

Consultations de la notice

441

Téléchargements de fichiers

1103