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

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)

  • Vaggelis Giannikas

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

  • Yufei Han

    (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)

  • 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)

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.

Suggested Citation

  • Shenle Pan & Vaggelis Giannikas & Yufei Han & Etta Grover-Silva & Bin Qiao, 2017. "Using Customer-related Data to Enhance E-grocery Home Delivery," Post-Print hal-01482901, HAL.
    • Handle: RePEc:hal:journl:hal-01482901
    DOI: 10.1108/IMDS-10-2016-0432
    Note: View the original document on HAL open archive server: https://minesparis-psl.hal.science/hal-01482901
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    Cited by:

    1. Büyüközkan, Gülçin & Ilıcak, Öykü, 2022. "Smart urban logistics: Literature review and future directions," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    2. John Olsson & Daniel Hellström & Henrik Pålsson, 2019. "Framework of Last Mile Logistics Research: A Systematic Review of the Literature," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    3. Maltese, Ila & Le Pira, Michela & Marcucci, Edoardo & Gatta, Valerio & Evangelinos, Christos, 2021. "Grocery or @grocery: A stated preference investigation in Rome and Milan," Research in Transportation Economics, Elsevier, vol. 87(C).
    4. Sergio Pardo-Jaramillo & Andrés Muñoz-Villamizar & Ignacio Osuna & Rolando Roncancio, 2020. "Mapping Research on Customer Centricity and Sustainable Organizations," Sustainability, MDPI, vol. 12(19), pages 1-18, September.
    5. Ioannis Margaritis & Michael Madas & Maro Vlachopoulou, 2022. "Big Data Applications in Food Supply Chain Management: A Conceptual Framework," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    6. Marta Viu-Roig & Eduard J. Alvarez-Palau, 2020. "The Impact of E-Commerce-Related Last-Mile Logistics on Cities: A Systematic Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-19, August.
    7. Julia Kleineidam, 2020. "Fields of Action for Designing Measures to Avoid Food Losses in Logistics Networks," Sustainability, MDPI, vol. 12(15), pages 1-20, July.
    8. Florio, Alexandre M. & Feillet, Dominique & Hartl, Richard F., 2018. "The delivery problem: Optimizing hit rates in e-commerce deliveries," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 455-472.
    9. Antonino Galati & Maria Crescimanno & Demetris Vrontis & Dario Siggia, 2020. "Contribution to the Sustainability Challenges of the Food-Delivery Sector: Finding from the Deliveroo Italy Case Study," Sustainability, MDPI, vol. 12(17), pages 1-12, August.
    10. Mitxel Cotarelo & Teresa Fayos & Haydeé Calderón & Alejandro Mollá, 2021. "Omni-Channel Intensity and Shopping Value as Key Drivers of Customer Satisfaction and Loyalty," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    11. Özarık, Sami Serkan & Veelenturf, Lucas P. & Woensel, Tom Van & Laporte, Gilbert, 2021. "Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    12. Prencipe, Luigi Pio & Colovic, Aleksandra & Binetti, Mario & Ottomanelli, Michele, 2024. "Zero-emission vehicle adoption towards sustainable e-grocery last-mile delivery," Research in Transportation Economics, Elsevier, vol. 104(C).
    13. Priscila Pereira Suzart Carvalho & Ricardo Araújo Kalid & Jorge Laureano Moya Rodríguez & Sandro Breval Santiago, 2019. "Interactions among stakeholders in the processes of city logistics: a systematic review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 567-607, August.
    14. Juan Carlos Martín & Francesca Pagliara & Concepción Román, 2019. "The Research Topics on E-Grocery: Trends and Existing Gaps," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    15. Özarık, Sami Serkan & Lurkin, Virginie & Veelenturf, Lucas P. & Van Woensel, Tom & Laporte, Gilbert, 2023. "An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 194-220.
    16. Uzir, Md. Uzir Hossain & Al Halbusi, Hussam & Thurasamy, Ramayah & Thiam Hock, Rodney Lim & Aljaberi, Musheer A. & Hasan, Najmul & Hamid, Mahmud, 2021. "The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
    17. Mar Vazquez-Noguerol & Jose Comesaña-Benavides & Raul Poler & J. Carlos Prado-Prado, 2022. "An optimisation approach for the e-grocery order picking and delivery problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 961-990, September.
    18. Omoruyi Osayuwamen, 2018. "Competitiveness Through the Integration of Logistics Activities in SMEs," Studia Universitatis Babeș-Bolyai Oeconomica, Sciendo, vol. 63(3), pages 15-32, December.
    19. Pegado-Bardayo, Ana & Lorenzo-Espejo, Antonio & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo, 2023. "A data-driven decision support system for service completion prediction in last mile logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    20. Mashalah, Heider Al & Hassini, Elkafi & Gunasekaran, Angappa & Bhatt (Mishra), Deepa, 2022. "The impact of digital transformation on supply chains through e-commerce: Literature review and a conceptual framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    21. Dariusz Milewski & Beata Milewska, 2021. "The Energy Efficiency of the Last Mile in the E-Commerce Distribution in the Context the COVID-19 Pandemic," Energies, MDPI, vol. 14(23), pages 1-13, November.
    22. repec:iim:iimawp:14638 is not listed on IDEAS
    23. Zhangyuan He & Hans-Dietrich Haasis, 2019. "Integration of Urban Freight Innovations: Sustainable Inner-Urban Intermodal Transportation in the Retail/Postal Industry," Sustainability, MDPI, vol. 11(6), pages 1-25, March.

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    More about this item

    Keywords

    Food Delivery; City Logistics; Data Mining; E-commerce; Freight Transportation.;
    All these keywords.

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