Providing a New Model to Improving DEA-based Models in Multi-criteria Inventory Classification (Case Study: Pars Khazar)

Document Type : Research Paper


1 Associate Prof., Department of Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran

2 Assistant Prof., Department of Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran

3 MA., Department of Industrial Management, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran


Objective: Many organizations use the ABC classification method to control their large amount of inventories. The most common way to classify inventories is the ABC method. In traditional ABC classification, items are only classified according to one criteria. But there are other criteria that need to be considered in the inventory classification. The purpose of this study is to present a new model for multi-criteria inventory classification.
Methods: Among the multi-criteria inventory classification methods, DEA-based methods do not require decision makers to determine the weight of the criteria; however, in the literature, only the radial methods of data envelopment analysis are used to classify inventory items. In this paper, the cross-efficiency of a non-radial model is proposed in order to improve the average cross-efficiency of the R model, which is a radial model.
Results: Therefore, the proposed method does not have the weakness of R model due to the use of a non-radial model and also it has benefits the cross-efficiency method.
Conclusion: The models were executed on 47 items of inventory related to a common numerical example in the research literature as well as on 80 items of inventory of the Pars Khazar Industrial Company and the results of the implementation of the models have been analyzed. The results of comparing the proposed model with some of the existing models in the literature indicate the superiority of the proposed model.


Alipor Jorshari, A., Yakideh, K., Mahfoozi, GH. (2017). Portfollio optimization by minimum absolute deviation of cross efficiencies. Journalof Industrial Management, 9(3), 475-496. (in Persian)
Chen, J. X. (2011). Peer-estimation for multiple criteria ABC inventory classification. Computers & Operations Research, 38 (12), 1784–1791.
Cooper, W.W. & Park, K.S. & Pastor, J.T. (1999). RAM:A range adjusted measure of inefficiency for use with additive models, and relations to other models and measurrs in DEA. Journal of Productivity Analysis, 11(1), 5-42.
Flores, B.E. & Whybark, D.C. (1987). Implementing multiple criteria ABC analysis. Journal of Operation Management, 7(1-2), 79-84.
Goodarzi, M., Yakideh, K., Mahfoozi, Gh. (2017). Portfollio optimization by synthesis of cross efficiency and Game theory. Journalof Industrial Management, 8(4), 685-706. 
(in Persian)
Guvenir, H.A. & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm.European Journal of Operational Research, 105 (1), 29-37.
Hadi-Vencheh, A. (2010). An improvement to multiple criteria ABC inventory classification. European Journal of Operational Research, 201, 962–965.
Hatefi, S.M., Torabi, S.A. & Bagheri, P. (2014). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776–786.
Hatefi, S.M. & Torabi, S.A. (2015). A common weight linear optimization approach for multicriteria ABC inventory classification. Advances in Decision Sciences, 2015.
Keren, B., & Hadad, Y. (2016). ABC Inventory Classification Using AHP and Ranking Methods via DEA. In Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO), 2016 Second International Symposium on (pp. 495-501). IEEE.
Momeni, M. (2015). New Operational Research Topics. Gange Shaygan, Tehran. (in Persian)
Namazi, M., Ebrahimi, S. (2011). The Investigation of the Iranian Banks' Efficiency by Using Stepwise DEA Technique,Journal of Industrial Management, 2(5), 159-332. (in Persian)
Ng, W.L. (2007). A simple classifier for multiple criteria ABC analysis. European Journal of Operational Research, 177 (1), 344-353.
Park, J., Bae, H., & Bae, J. (2014). Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification. Computers & Industrial Engineering76, 40-48.
Partovi, F. Y. & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers and Industrial Engineering, 41 (4), 389–404.
Ramanathan, R. (2006). ABC inventory classification with multiple criteria using weighted linear optimization. Computers & Operations Research, 33, 695–700.
Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling (Vol. 3, p. 30). New York: Wiley.
Zarei Mahmoudabad, M., Tahari Mehrjerdi, M.H., Mahdavian, A. (2014). Evaluation of R&D Activities in Iran: Data Envelopment Analysis Approach. Journal of Industrial Management, 6(1), 55-79. (in Persian)
Zheng, S., Fu, Y., Lai, K. K., & Liang, L. (2017). An improvement to multiple criteria ABC inventory classification using Shannon entropy. Journal of Systems Science and Complexity30(4), 857-865.
Zhou, P. & Fan, L. (2007). A note on multi-criteria ABC inventory classification using weighted linear optimization. European Journal of Operational Research, 182, 1488-1491.