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An Adaptive Decision Support System for Last Mile Logistics in E-Commerce: A Study on Online Grocery Shopping

Author

Listed:
  • Mohammad K. Al-nawayseh

    (Jerash University, Jerash, Jordan)

  • Mohammad M. Alnabhan

    (Jerash University, Jerash, Jordan)

  • Mutaz M. Al-Debei

    (The University of Jordan, Amman, Jordan)

  • Wamadeva Balachandran

    (Brunel University, London, UK)

Abstract

Last mile logistics represent one of the most important challenging issues in online grocery shopping. Online customers are expecting high logistical services, demanding convenience, high reliable and on-time delivery services. As such, online retailers have to respond to these expectations by providing convenient logistical services while keeping this process cost efficient as much as possible. This research aims to design an e-commerce logistical decision support system for online grocery shopping in Jordan as a case study from the developing countries. Online grocery retailers are supposed to use this model in order to select the most suitable delivery operating system in the future. To implement and evaluate this model, one of the available routing and scheduling online solutions (i.e. “My Route Online”) is used to identify, analyse, and compare the cost efficiencies of the available alternative delivery solutions. The system is tested using real data over three different delivery alternatives (i.e. home delivery, delivery point and pickup point) in order to evaluate and compare their cost efficiencies. The findings from the experiments show that there are significant differences amongst the three delivery alternatives on the basis of three KPIs: cost, distance and time. The findings also indicate that the time indicator has more powerful change effect on cost than distance for all delivery alternatives. According to the level of investments online grocery retailers are willing to offer, customer preferences, and the experimental results, it is concluded that pickup point solution is the best logistical strategy for online grocery retailers to start with.

Suggested Citation

  • Mohammad K. Al-nawayseh & Mohammad M. Alnabhan & Mutaz M. Al-Debei & Wamadeva Balachandran, 2013. "An Adaptive Decision Support System for Last Mile Logistics in E-Commerce: A Study on Online Grocery Shopping," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 5(1), pages 40-65, January.
    • Handle: RePEc:igg:jdsst0:v:5:y:2013:i:1:p:40-65
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    Cited by:

    1. Atkočiūnienė Vilma & Papšienė Palmira, 2023. "Opportunities for Digitisation of Agricultural and Rural Development Solutions," Management Theory and Studies for Rural Business and Infrastructure Development, Sciendo, vol. 45(1), pages 1-8, March.
    2. Cheng-An Tsai & Che-Wei Chang, 2022. "Development of a Partial Shipping Fees Pricing Model to Influence Consumers’ Purchase Intention under the COVID-19 Pandemic," Energies, MDPI, vol. 15(5), pages 1-16, March.
    3. 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.
    4. 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.

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