Multiple Criteria Inventory Classification in an Electronics Firm

Efficient inventory classification is a vital activity for electronics firms that work with a large amount of inventory items. Although one of the most widely used techniques in inventory classification is ABC analysis, this technique considers only a single criterion as the annual sales volume of each item. In practice, inhomogeneity and the differences among the inventory items necessitate considering multiple criteria to obtain a reliable classification. In this study, an integrated process of the analytic hierarchy process (AHP)-The technique for order preference by similarity to the ideal solution (TOPSIS)-ABC approach is proposed and performed to solve the multiple criteria inventory classification problem in an electronics firm. The steps of the interactive approach are programmed using MATLAB. The results of the integrated interactive approach and of the traditional ABC analysis are presented. The proposed approach gives effective and implementable results for the firm.

[1]  E. Stanley Lee,et al.  An extension of TOPSIS for group decision making , 2007, Math. Comput. Model..

[2]  Ozan Çakir,et al.  A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology , 2008, Expert Syst. Appl..

[3]  Renzo Akkerman,et al.  SKU classification: a literature review and conceptual framework , 2012 .

[4]  Jafar Rezaei,et al.  A rule-based multi-criteria approach to inventory classification , 2010 .

[5]  Bijan Sarkar,et al.  Distance-based consensus method for ABC analysis , 2007 .

[6]  Yi Peng,et al.  FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms , 2011 .

[7]  David L. Olson,et al.  Management of multicriteria inventory classification , 1992 .

[8]  Jonathan Burton,et al.  Using the Analytic Hierarchy Process for ABC Analysis , 1993 .

[9]  D. Clay Whybark,et al.  Implementing multiple criteria ABC analysis , 1987 .

[10]  Rommert Dekker,et al.  An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods , 2008, Eur. J. Oper. Res..

[11]  YuMin-Chun.  Multi-criteria ABC analysis using artificial-intelligence-based classification techniques , 2011 .

[12]  D. Clay Whybark,et al.  Multiple Criteria ABC Analysis , 1986 .

[13]  D. Clay Whybark,et al.  Implementing multiple criteria ABC analysis , 1989 .

[14]  Yi Peng,et al.  Evaluation of Classification Algorithms Using MCDM and Rank Correlation , 2012, Int. J. Inf. Technol. Decis. Mak..

[15]  Peng Zhou,et al.  A note on multi-criteria ABC inventory classification using weighted linear optimization , 2007, Eur. J. Oper. Res..

[16]  D. Towill,et al.  The impact of product life cycle on supply chain strategy , 2003 .

[17]  Petri Niemi,et al.  The role of C-products in providing customer service—refining the inventory policy according to customer-specific factors , 2005 .

[18]  Yi Peng,et al.  Evaluation of clustering algorithms for financial risk analysis using MCDM methods , 2014, Inf. Sci..

[19]  Yong B. Shin,et al.  A CRITICAL REVIEW OF POPULAR MULTI-CRITERIA DECISION MAKING METHODLOGIES , 2013 .

[20]  Abdollah Hadi-Vencheh,et al.  An improvement to multiple criteria ABC inventory classification , 2010, Eur. J. Oper. Res..

[21]  Marco Macchi,et al.  A decision-making framework for managing maintenance spare parts , 2008 .

[22]  Zhengxin Chen,et al.  A Descriptive Framework for the Field of Data Mining and Knowledge Discovery , 2008, Int. J. Inf. Technol. Decis. Mak..

[23]  Wan Lung Ng,et al.  Production , Manufacturing and Logistics A simple classifier for multiple criteria ABC analysis , 2006 .

[24]  L. Canetta *,et al.  Applying two-stage SOM-based clustering approaches to industrial data analysis , 2005 .

[25]  Abdollah Hadi-Vencheh,et al.  A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification , 2011, Expert Syst. Appl..

[26]  T M El-Hossainy,et al.  A Fuzzy Model for Multi-Criteria , 2011 .

[27]  S. M. Hatefi,et al.  Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria , 2014 .

[28]  Murugan Anandarajan,et al.  Classifying inventory using an artificial neural network approach , 2002 .

[29]  Jin-Xiao Chen,et al.  Multiple criteria ABC inventory classification using two virtual items , 2012 .

[30]  Tomislav Šarić,et al.  Application of Artificial Neural Networks to Multiple Criteria Inventory Classification , 2009 .

[31]  Ramakrishnan Ramanathan,et al.  ABC inventory classification with multiple-criteria using weighted linear optimization , 2006, Comput. Oper. Res..

[32]  H. Altay Güvenir,et al.  Multicriteria inventory classification using a genetic algorithm , 1998, Eur. J. Oper. Res..

[33]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[34]  Fariborz Y. Partovi,et al.  THE ANALYTIC HIERARCHY PROCESS AS APPLIED TO TWO TYPES OF INVENTORY PROBLEMS , 1993 .

[35]  Talel Ladhari,et al.  On the inventory performance of multi-criteria classification methods: empirical investigation , 2015 .

[36]  Keith W. Hipel,et al.  A case-based distance model for multiple criteria ABC analysis , 2008, Comput. Oper. Res..

[37]  Valerie Belton,et al.  On a short-coming of Saaty's method of analytic hierarchies , 1983 .

[38]  Min-Chun Yu,et al.  Multi-criteria ABC analysis using artificial-intelligence-based classification techniques , 2011, Expert Syst. Appl..

[39]  Thomas L. Saaty,et al.  DECISION MAKING WITH THE ANALYTIC HIERARCHY PROCESS , 2008 .

[40]  Ricardo Ernst,et al.  MULTI-ITEM CLASSIFICATION AND GENERIC INVENTORY STOCK CONTROL POLICIES , 1988 .

[41]  Felix Hueber,et al.  Introduction To Materials Management , 2016 .