IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01482901.html
   My bibliography  Save this paper

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-01482901v1
    as

    Download full text from publisher

    File URL: https://minesparis-psl.hal.science/hal-01482901v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1108/IMDS-10-2016-0432?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Ramanathan, Ramakrishnan, 2010. "The moderating roles of risk and efficiency on the relationship between logistics performance and customer loyalty in e-commerce," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(6), pages 950-962, November.
    3. Ann Melissa Campbell & Martin Savelsbergh, 2006. "Incentive Schemes for Attended Home Delivery Services," Transportation Science, INFORMS, vol. 40(3), pages 327-341, August.
    4. Azi, Nabila & Gendreau, Michel & Potvin, Jean-Yves, 2007. "An exact algorithm for a single-vehicle routing problem with time windows and multiple routes," European Journal of Operational Research, Elsevier, vol. 178(3), pages 755-766, May.
    5. Ehmke, Jan Fabian & Campbell, Ann Melissa, 2014. "Customer acceptance mechanisms for home deliveries in metropolitan areas," European Journal of Operational Research, Elsevier, vol. 233(1), pages 193-207.
    6. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    7. Jan Fabian Ehmke, 2012. "Routing in City Logistics," International Series in Operations Research & Management Science, in: Integration of Information and Optimization Models for Routing in City Logistics, edition 127, chapter 0, pages 119-156, Springer.
    8. Niels Agatz & Ann Campbell & Moritz Fleischmann & Martin Savelsbergh, 2011. "Time Slot Management in Attended Home Delivery," Transportation Science, INFORMS, vol. 45(3), pages 435-449, August.
    9. Jan Fabian Ehmke, 2012. "Attended Home Delivery," International Series in Operations Research & Management Science, in: Integration of Information and Optimization Models for Routing in City Logistics, edition 127, chapter 0, pages 23-33, Springer.
    10. Agatz, Niels A.H. & Fleischmann, Moritz & van Nunen, Jo A.E.E., 2008. "E-fulfillment and multi-channel distribution - A review," European Journal of Operational Research, Elsevier, vol. 187(2), pages 339-356, June.
    11. Roberto Baldacci & Aristide Mingozzi & Roberto Roberti, 2011. "New Route Relaxation and Pricing Strategies for the Vehicle Routing Problem," Operations Research, INFORMS, vol. 59(5), pages 1269-1283, October.
    12. Jan Fabian Ehmke, 2012. "Integration of Information and Optimization Models for Routing in City Logistics," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-3628-7, April.
    13. Zott, Christoph & Amit, Raphael & Donlevy, Jon, 2000. "Strategies for value creation in e-commerce:: best practice in Europe," European Management Journal, Elsevier, vol. 18(5), pages 463-475, October.
    14. Breugelmans, Els & Campo, Katia, 2016. "Cross-Channel Effects of Price Promotions: An Empirical Analysis of the Multi-Channel Grocery Retail Sector," Journal of Retailing, Elsevier, vol. 92(3), pages 333-351.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Jean-François Cordeau & Manuel Iori & Dario Vezzali, 2024. "An updated survey of attended home delivery and service problems with a focus on applications," Annals of Operations Research, Springer, vol. 343(2), pages 885-922, December.
    3. 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.
    4. 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.
    5. Büyüközkan, Gülçin & Ilıcak, Öykü, 2022. "Smart urban logistics: Literature review and future directions," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    6. 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.
    7. 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.
    8. 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).
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    15. 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.
    16. 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).
    17. 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.
    18. 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).
    19. 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).
    20. 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.
    21. repec:iim:iimawp:14638 is not listed on IDEAS
    22. 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.
    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.
    24. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jean-François Cordeau & Manuel Iori & Dario Vezzali, 2024. "An updated survey of attended home delivery and service problems with a focus on applications," Annals of Operations Research, Springer, vol. 343(2), pages 885-922, December.
    2. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    3. Ehmke, Jan Fabian & Campbell, Ann Melissa, 2014. "Customer acceptance mechanisms for home deliveries in metropolitan areas," European Journal of Operational Research, Elsevier, vol. 233(1), pages 193-207.
    4. Pedro Amorim & Nicole DeHoratius & Fredrik Eng-Larsson & Sara Martins, 2024. "Customer Preferences for Delivery Service Attributes in Attended Home Delivery," Management Science, INFORMS, vol. 70(11), pages 7559-7578, November.
    5. Strauss, Arne & Gülpınar, Nalan & Zheng, Yijun, 2021. "Dynamic pricing of flexible time slots for attended home delivery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1022-1041.
    6. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    7. Agatz, N.A.H. & Fan, Y. & Stam, D.A., 2020. "Going green: the effect of green labels on delivery time slot choices," ERIM Report Series Research in Management ERS-2020-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Yang, Xinan & Strauss, Arne K., 2017. "An approximate dynamic programming approach to attended home delivery management," European Journal of Operational Research, Elsevier, vol. 263(3), pages 935-945.
    9. Bruno P. Bruck & Filippo Castegini & Jean-François Cordeau & Manuel Iori & Tommaso Poncemi & Dario Vezzali, 2020. "A Decision Support System for Attended Home Services," Interfaces, INFORMS, vol. 50(2), pages 137-152, March.
    10. Waßmuth, Katrin & Köhler, Charlotte & Agatz, Niels & Fleischmann, Moritz, 2023. "Demand management for attended home delivery—A literature review," European Journal of Operational Research, Elsevier, vol. 311(3), pages 801-815.
    11. Avraham, Edison & Raviv, Tal, 2021. "The steady-state mobile personnel booking problem," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 266-288.
    12. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    13. Robert Klein & Jochen Mackert & Michael Neugebauer & Claudius Steinhardt, 2018. "A model-based approximation of opportunity cost for dynamic pricing in attended home delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 969-996, October.
    14. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    15. Niels Agatz & Yingjie Fan & Daan Stam, 2021. "The Impact of Green Labels on Time Slot Choice and Operational Sustainability," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2285-2303, July.
    16. Robert Klein & Michael Neugebauer & Dimitri Ratkovitch & Claudius Steinhardt, 2019. "Differentiated Time Slot Pricing Under Routing Considerations in Attended Home Delivery," Service Science, INFORMS, vol. 53(1), pages 236-255, February.
    17. Abdollahi, Mohammad & Yang, Xinan & Nasri, Moncef Ilies & Fairbank, Michael, 2023. "Demand management in time-slotted last-mile delivery via dynamic routing with forecast orders," European Journal of Operational Research, Elsevier, vol. 309(2), pages 704-718.
    18. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    19. Hernandez, Florent & Feillet, Dominique & Giroudeau, Rodolphe & Naud, Olivier, 2016. "Branch-and-price algorithms for the solution of the multi-trip vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 249(2), pages 551-559.
    20. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.

    More about this item

    Keywords

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-01482901. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.