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A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment

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  • Lam, H.Y.
  • Choy, K.L.
  • Ho, G.T.S.
  • Cheng, Stephen W.Y.
  • Lee, C.K.M.

Abstract

Customer orders with high product varieties in small quantities are often received by the logistics service providers with requests for customized value-added services and timely delivery, so the warehouse has to plan its logistics strategy in such a way that it can effectively maintain the quality of its services. In addition, they have to pay attention to the possible risks that may occur during the logistics operations so as to prevent loss if they fail to deal with the problems and risks properly. In order to facilitate the decision making process in warehouse operations, an intelligent system, namely the knowledge-based logistics operations planning system (K-LOPS), is proposed to formulate a useful action plan by considering the potential risks faced by the logistics service providers. The system makes use of Radio Frequency Identification technology to collect real-time logistics data. Analytical hierarchy processes and case-based reasoning are integrated into the system. These help to categorize the potential risk factors considered by customers and formulate the logistics operations strategy, respectively, as the different product characteristics and order demands are taken into consideration. The searching performance in case-based reasoning is enhanced by the iterative dynamic partitional clustering algorithm. After conducting a trial run in the case company, the result shows that there is significant improvement in case retrieval time and in solution formulation.

Suggested Citation

  • Lam, H.Y. & Choy, K.L. & Ho, G.T.S. & Cheng, Stephen W.Y. & Lee, C.K.M., 2015. "A knowledge-based logistics operations planning system for mitigating risk in warehouse order fulfillment," International Journal of Production Economics, Elsevier, vol. 170(PC), pages 763-779.
    • Handle: RePEc:eee:proeco:v:170:y:2015:i:pc:p:763-779
    DOI: 10.1016/j.ijpe.2015.01.005
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    Cited by:

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    2. Rivera, Liliana & Sheffi, Yossi & Knoppen, Desirée, 2016. "Logistics clusters: The impact of further agglomeration, training and firm size on collaboration and value added services," International Journal of Production Economics, Elsevier, vol. 179(C), pages 285-294.
    3. Mahlet Demere Tadesse & Helen Zewdie Kine & Girma Gebresenbet & Lóránt Tavasszy & David Ljungberg, 2022. "Key Logistics Performance Indicators in Low-Income Countries: The Case of the Import–Export Chain in Ethiopia," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    4. Giannikas, Vaggelis & Lu, Wenrong & Robertson, Brian & McFarlane, Duncan, 2017. "An interventionist strategy for warehouse order picking: Evidence from two case studies," International Journal of Production Economics, Elsevier, vol. 189(C), pages 63-76.
    5. Vitor W. B. Martins & Rosley Anholon & Osvaldo L. G. Quelhas & Walter Leal Filho, 2019. "Sustainable Practices in Logistics Systems: An Overview of Companies in Brazil," Sustainability, MDPI, vol. 11(15), pages 1-12, July.
    6. Carolina Gerini & Anna Sciomachen, 2019. "Evaluation of the flow of goods at a warehouse logistic department by Petri Nets," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 354-380, June.
    7. Vidal Vieira, José Geraldo & Ramos Toso, Milton & da Silva, João Eduardo Azevedo Ramos & Cabral Ribeiro, Priscilla Cristina, 2017. "An AHP-based framework for logistics operations in distribution centres," International Journal of Production Economics, Elsevier, vol. 187(C), pages 246-259.

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