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A proposed hybrid storage assignment framework: a case study

Author

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  • Sanjay Sharma
  • Bhavin Shah

Abstract

Purpose - – A hybrid storage assignment (combination class-volume-based) framework considering quality proximity, customer and material categorization are key distinguished contents of this paper. In spite of using individual storage allocation approach, the hybrid allocation policy performs better under certain environment. The paper aims to discuss these issues. Design/methodology/approach - – Although it has been proved that every storage assignment policy has their advantages and limitations, one or more storage assignment policies with combination of zoning and layout design can be used together for further improvement. The authors have conducted this study at warehouse of a manufacturing firm that produce only single product with varieties of material and quality criteria. Picking optimization includes elimination of non-value-added activities like unwanted forklift and package movements, time and distance traveled for retrieval as well as storage. Other allied operations with respect to customer acceptance level and resource utilization are also considered. Findings - – The time and distance from manufacturing point to storage location are accountable as it also contributes to picking performance. Originality/value - – Quality-based cluster analysis is carried out to find out closeness among customers, which is used to propose algorithm with new layout design, zoning and storage allocation policy.

Suggested Citation

  • Sanjay Sharma & Bhavin Shah, 2015. "A proposed hybrid storage assignment framework: a case study," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 64(6), pages 870-892, July.
  • Handle: RePEc:eme:ijppmp:v:64:y:2015:i:6:p:870-892
    DOI: 10.1108/IJPPM-04-2014-0053
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    Cited by:

    1. Rafael Diaz, 2016. "Using dynamic demand information and zoning for the storage of non-uniform density stock keeping units," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2487-2498, April.

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    Keywords

    Case studies; Clusters;

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