Abstract
Inventory policies in a company may consist of storage, control, and replenishment policy. Since the result of common ABC inventory classification can only affect the replenishment policy, we are proposing a clustering based classification technique as a basis for developing inventory policy especially for storage and control policy. Hierarchical clustering procedure is used after clustering variables are defined. Since hierarchical clustering procedure requires metric variables only, therefore a step to convert non-metric variables to metric variables is performed. The clusters resulted from the clustering techniques are analyzed in order to define each cluster characteristics. Then, the inventory policies are determined for each group according to its characteristics. A real data, which consists of 612 items from a local manufacturer's spare part warehouse, are used in the research of this paper to show the applicability of the proposed methodology.
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References
- [1]Silver E, Pyke D F and Peterson R 1998 Inventory Management and Production Planning and Scheduling (New York: Wiley)
- [2]Frey K and Gordon L A 1999 ABC, strategy and business unit performance International Journal of Applied Quality Management 2 1
- [3]Fuerst W L 1981 Small businesses get a new look at ABC analysis for inventory control Journal of Small Business Management 19 39
- [4]Chu C W, Liang G S and Liao C T 2008 Controlling inventory by combining ABC analysis and fuzzy classification Computers & Industrial Engineering 55 841
- [5]Torabi S A, Hatefi S M and Pay B S 2012 ABC inventory classification in the presence of both quantitative and qualitative criteria Computers & Industrial Engineering 63 530
- [6]Bacchetti A, Plebani R, Saccani N and Syntetos A 2010 Spare parts classification and inventory management: a case study Salford Business School Working Papers Series 408 12
- [7]Kabir G and Sumi R S 2013 Integrating fuzzy Delphi with fuzzy analytic hierarchy process for multiple criteria inventory classification Journal of Engineering, Project, and Production Management 3 22
- [8]Hadi-Vencheh A and Mohamadghasemi A 2011 A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification Expert Systems with Applications 38 3346
- [9]Cakir O and Canbolat M S 2008 A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology Expert Systems with Applications 35 1367
- [10]Hmida J B, Regan G and Lee J 2013 Inventory Management and Maintenance in Offshore Vessel Industry Journal of Industrial Engineering 2013 851092
- [11]Yu M C 2011 Multi-criteria ABC analysis using artificial-intelligence-based classification techniques Expert Systems with Applications 38 3416
- [12]Šimunović K, Šimunović G and Šarić T 2009 Application of artificial neural networks to multiple criteria inventory classification Strojarstvo: časopis za teoriju i praksu u strojarstvu 51 313
- [13]Zandieh M, Farahani H F and Roshanaei V 2013 Multi-criteria inventory classification problem: An effective artificial immune algorithm International Journal of Management Perspective 1 1
- [14]Guvenir H A and Erel E 1998 Multicriteria inventory classification using a genetic algorithm European Journal of Operational Research 105 29
- [15]Ramanathan R 2006 ABC inventory classification with multiple-criteria using weighted linear optimization Computers & Operations Research 33 695
- [16]Halkidi M, Batistakis Y and Vazirgiannis M 2001 On clustering validation techniques Journal of Intelligent Information Systems 17 107
- [17]Hair J F, Black W C, Babin B J, Anderson R E and Tatham R L 2006 Multivariate data analysis (New Jersey: Prentice Hall)