Using Customer-related Data to Enhance E-grocery Home Delivery - Mines Paris Accéder directement au contenu
Article Dans Une Revue Industrial Management and Data Systems Année : 2017

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


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.
Fichier principal
Vignette du fichier
IMDS_Manuscript_online.pdf (1.88 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01482901 , version 1 (07-03-2017)



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, 2017, 117 (9), pp.1917-1933. ⟨10.1108/IMDS-10-2016-0432⟩. ⟨hal-01482901⟩
327 Consultations
1125 Téléchargements



Gmail Facebook Twitter LinkedIn More