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Inventory control system design by integrating inventory classification and policy selection

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  • Mohammaditabar, Davood
  • Hassan Ghodsypour, Seyed
  • O'Brien, Chris

Abstract

Very large numbers of inventory items complicate the inventory control process. Companies categorize their inventory items into a few groups and take similar inventory control policies for the items in each group to overcome this problem. In this regard many grouping methods have been proposed. Some researchers have studied the appropriate inventory policy for each group. Since both the actions of categorization and policy selection are sub-optimal solutions for the original problem of efficient inventory control policy, this paper proposes an integrated model to categorize the items and find the best policy simultaneously. As it is difficult to find a global solution, simulated annealing is used to find appropriate solutions. The model results are compared with the findings of other methods both for dissimilarity and total inventory values.

Suggested Citation

  • Mohammaditabar, Davood & Hassan Ghodsypour, Seyed & O'Brien, Chris, 2012. "Inventory control system design by integrating inventory classification and policy selection," International Journal of Production Economics, Elsevier, vol. 140(2), pages 655-659.
    • Handle: RePEc:eee:proeco:v:140:y:2012:i:2:p:655-659
    DOI: 10.1016/j.ijpe.2011.03.012
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    References listed on IDEAS

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    Cited by:

    1. Sheikh-Zadeh, Alireza & Rossetti, Manuel D. & Scott, Marc A., 2021. "Performance-based inventory classification methods for large-Scale multi-echelon replenishment systems," Omega, Elsevier, vol. 101(C).
    2. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    3. Sheikh-Zadeh, Alireza & Rossetti, Manuel D., 2020. "Classification methods for problem size reduction in spare part provisioning," International Journal of Production Economics, Elsevier, vol. 219(C), pages 99-114.
    4. Lolli, F. & Ishizaka, A. & Gamberini, R., 2014. "New AHP-based approaches for multi-criteria inventory classification," International Journal of Production Economics, Elsevier, vol. 156(C), pages 62-74.
    5. Hu, Qiwei & Chakhar, Salem & Siraj, Sajid & Labib, Ashraf, 2017. "Spare parts classification in industrial manufacturing using the dominance-based rough set approach," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1136-1163.
    6. Yang, Liu & Li, Haitao & Campbell, James F. & Sweeney, Donald C., 2017. "Integrated multi-period dynamic inventory classification and control," International Journal of Production Economics, Elsevier, vol. 189(C), pages 86-96.
    7. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    8. Jussim, Maxim, 2014. "Entwicklung eines Simulationstools zur Analyse von Prognose- und Dispositionsentscheidungen im Krankenhausbereich," Bayreuth Reports on Information Systems Management 57, University of Bayreuth, Chair of Information Systems Management.
    9. Hadhami Kaabi & Khaled Jabeur & Talel Ladhari, 2018. "A Genetic Algorithm-Based Classification Approach for Multicriteria ABC Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1805-1837, November.
    10. Hung, Yi-Feng & Hsiao, Jui-Yi, 2013. "Inventory rationing decision models during replenishment lead time," International Journal of Production Economics, Elsevier, vol. 144(1), pages 290-300.

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