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A RFID-based storage assignment system for enhancing the efficiency of order picking

Author

Listed:
  • K. L. Choy

    (The Hong Kong Polytechnic University)

  • G. T. S. Ho

    (The Hong Kong Polytechnic University)

  • C. K. H. Lee

    (The Hong Kong Polytechnic University)

Abstract

In today’s time-sensitive markets, effective storage policies are widely accepted as a means for improving the efficiency of order picking. As a result of customization, the variety of products handled by a warehouse has increased, making storage location assignment problems more complicated. Different approaches have been proposed by researchers for improving storage assignment and order picking. However, many industrial practitioners find it difficult to adopt such approaches due to complexity and high associated costs. In particular, small and medium enterprises (SMEs), that generally, lack resources and who have staff members with weak artificial intelligence backgrounds, still rely on experience when assigning storage locations for diverse products. In these circumstances, the quality of decision making cannot be guaranteed. In view of this, an intelligent system which can be easily adopted by SMEs is designed to improve storage location assignment problems. The proposed system, an RFID-based storage assignment system (RFID-SAS), is a rule-based system incorporating radio frequency identification (RFID) provides decision support for storage assignment in a warehouse. Unlike many existing situations, RFID tags are attached to products at the item level instead of at the pallet level. As the knowledge embedded in the system is represented in the form of rules, evaluation is important and is outlined in this paper. The effectiveness of the system is verified by means of a case study in which the system is implemented in a typical SME specializing in machinery manufacturing. The results illustrate that RFID-SAS can enhance the efficiency of order picking in a warehouse.

Suggested Citation

  • K. L. Choy & G. T. S. Ho & C. K. H. Lee, 2017. "A RFID-based storage assignment system for enhancing the efficiency of order picking," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 111-129, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0965-9
    DOI: 10.1007/s10845-014-0965-9
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    References listed on IDEAS

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    1. Brynzer, H. & Johansson, M. I., 1996. "Storage location assignment: Using the product structure to reduce order picking times," International Journal of Production Economics, Elsevier, vol. 46(1), pages 595-603, December.
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    4. Lim, Ming K. & Bahr, Witold & Leung, Stephen C.H., 2013. "RFID in the warehouse: A literature analysis (1995–2010) of its applications, benefits, challenges and future trends," International Journal of Production Economics, Elsevier, vol. 145(1), pages 409-430.
    5. Petersen, Charles G. & Aase, Gerald, 2004. "A comparison of picking, storage, and routing policies in manual order picking," International Journal of Production Economics, Elsevier, vol. 92(1), pages 11-19, November.
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    Cited by:

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    2. Diego Augusto Jesus Pacheco & Carlos Fernando Jung & Marcelo Cunha Azambuja, 2023. "Towards industry 4.0 in practice: a novel RFID-based intelligent system for monitoring and optimisation of production systems," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1165-1181, March.
    3. Paulina Golinska-Dawson & Karolina Werner-Lewandowska & Karolina Kolinska & Adam Kolinski, 2023. "Impact of Market Drivers on the Digital Maturity of Logistics Processes in a Supply Chain," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    4. Abirami Raja Santhi & Padmakumar Muthuswamy, 2022. "Pandemic, War, Natural Calamities, and Sustainability: Industry 4.0 Technologies to Overcome Traditional and Contemporary Supply Chain Challenges," Logistics, MDPI, vol. 6(4), pages 1-32, November.
    5. Zhang, Guoqing & Shang, Xiaoting & Alawneh, Fawzat & Yang, Yiqin & Nishi, Tatsushi, 2021. "Integrated production planning and warehouse storage assignment problem: An IoT assisted case," International Journal of Production Economics, Elsevier, vol. 234(C).
    6. Yang, Miying & Fu, Mingtao & Zhang, Zihan, 2021. "The adoption of digital technologies in supply chains: Drivers, process and impact," Technological Forecasting and Social Change, Elsevier, vol. 169(C).

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