Three New Approaches for Determining Importance Levels of ABC Items to Reduce Inventory Costs
Abstract
The study of existing models in multi-criteria inventory ABC classification shows that current efforts has sought a new model for this problem based on comparing models. In all of these models, importance level of A, B and C’s items has been assumed to be equal. The equality assumption of importance level of items can be wrong. In this paper we present three new approaches for determining importance levels of inventory items. These approaches can reduce the costs of inventory control. The presented approaches are tested on the 47 items and Ng Model. The results show that number of high importance and low importance items decreases and increases, respectively. Considering the low cost of applying control policies to low importance items, we can say that these results reduce the inventory costs. Excel and Lingo software is used for calculations.
Cohen, M.A. and Ernst, R., Multi-item classification and generic inventory stock control policies. Production and Inventory Management Journal 29(3) (1988) 6–8.
Flores, B.E., D.C. Whybark. Multiple criteria ABC analysis. International Journal of Operations and Production Management 6(3) (1986) 38-46.
Guvenir, H.A. and Erel, E., Multicriteria inventory classification using a genetic algorithm, European Journal of Operational Research 105 (1998) 29-37.
Ng, W.L., A simple classifier for multiple criteria ABC analysis, European Journal of Operational Research 177(2007) 344–353
Partovi FY, Burton J. Using the analytic hierarchy process for ABC analysis. International Journal of Production and Operations Management 13(9) (1993) 29–44.
Partovi, FY.and Anandarajan, M., Classifying inventory using an artificial neural network approach. Computers & Industrial Engineering 41 (2002) 389–404.
Puente, J., D. de la Fuente, P. Priore, R. Pino. ABC classifcation with uncertain data: a fuzzy model vs. a probabilistic model. Applied Artifcial Intelligence 16(6) (2002) 443-456.
Ramanathan, R., ABC inventory classification with multiple criteria using weighted linear optimization, Computers & Operations Research 33 (2006) 695–700.
Rezaei, J. and Dowlatshahi, S., 2010. A rule-based multi-criteria approach to inventory classification, International Journal of Production research, Vol. 48, No. 23, pp. 7107-7126.
Rezaei, J., A fuzzy model for multi-criteria inventory classification, 6th International Conference on Analysis of Manufacturing Systems, AMS 2007, Lunteren, The Netherlands, 11-16 May 2007, 167-172.
Rezaei, J., A note on multi-criteria inventory classification using weighted linear optimization, Yugoslav Journal of Operations Research, 20(2), 293-299.
Silver, E.A., D.F. Pyke, R. Peterson. 1998. Inventory Management and Production Planning and Scheduling.John Wiley & Sons, New York.
Van Laarhoven, P.J.M., Pedrycz, W., 1983. A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems 11, 229-241.
Vollmann, T.E., W.L. Berry, D.C. Whybark. 1997. Manufacturing Planning and Control Systems. McGraw-Hill,Boston.
Zhou P. and Fan, L., A note on multi-criteria ABC inventory classification using weighted linear optimization, European Journal of Operational Research 182(2007) 1488-1491
.Gulsen Aydin Keskin, and Coskun Ozkan, Multiple Criteria ABC Analysis with FCM Clustering, Hindawi Publishing Corporation Journal of Industrial Engineering Volume 2013, Article ID 827274, 7 pages
Z. Eslaminasab, T. Dokoohaki, ABC inventory classification withmultiple-criteria using weighted non-linear programmin, Available online at www.ispacs.com/conferences/cjac Volume 1, Year 2012, Article ID cjac-001-028, Pages 242-251 doi:10.5899/2012/cjac-001-028 Conference Article.
رضائی، جعفر، اسماعیل زاده، منصور، «مقایسه روش های مختلف طبقه بندی ABC چند معیاره موجودی»، فصلنامه علمی- پژوهشی مطالعات مدیریت صنعتی دانشگاه علامه طباطبائی، سال ششم، شماره 17، پائیز 86، صص 22-1.
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