IDEAS home Printed from https://ideas.repec.org/a/ids/ijisen/v14y2013i1p74-103.html
   My bibliography  Save this article

Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network

Author

Listed:
  • Golam Kabir
  • M. Ahsan Akhtar Hasin

Abstract

A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. To efficiently control the inventory items and to determine the suitable ordering policies for them, multi-criteria inventory classification is used. The objective of this research is to develop a multi-criteria inventory classification model through integration of fuzzy analytic hierarchy process (FAHP) and artificial neural network approach. FAHP is used to determine the relative weights of the attributes or criteria using Chang's extent analysis and to classify inventories into different categories. Various structures of multi-layer feed-forward back-propagation neural networks have been analysed and the optimal one with the minimum mean absolute percentage of error between the measured and the predicted values have been selected. To accredit the proposed model, it is implemented for 351 raw materials of switchgear section of Energypac Engineering Limited, a large power engineering company of Bangladesh.

Suggested Citation

  • Golam Kabir & M. Ahsan Akhtar Hasin, 2013. "Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 14(1), pages 74-103.
    • Handle: RePEc:ids:ijisen:v:14:y:2013:i:1:p:74-103
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=52922
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alabbasi, Abdulla & Sadhukhan, Jhuma & Leach, Matthew & Sanduk, Mohammed, 2024. "Accelerating the Transition to sustainable energy: An intelligent decision support system for generation expansion planning with renewables," Energy, Elsevier, vol. 304(C).
    2. 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.
    3. 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.
    4. Dhanisetty, V.S. Viswanath & Verhagen, W.J.C. & Curran, Richard, 2018. "Multi-criteria weighted decision making for operational maintenance processes," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 152-164.
    5. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    6. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2019. "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks," International Journal of Production Economics, Elsevier, vol. 212(C), pages 19-38.
    7. Siamak Kheybari & S. Ali Naji & Fariba Mahdi Rezaie & Reza Salehpour, 2019. "ABC classification according to Pareto’s principle: a hybrid methodology," OPSEARCH, Springer;Operational Research Society of India, vol. 56(2), pages 539-562, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijisen:v:14:y:2013:i:1:p:74-103. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=188 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.