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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.

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