Multi-criteria classification of spare parts inventories – a web based approach

Purpose – The purpose of this paper is to present a fuzzy-rule-based model to classify spare parts inventories considering multiple criteria for better management of maintenance activities to overcome production down situation. Design/methodology/approach – Fuzzy-rule-based approach for multi-criteria decision making is used to classify the spare parts inventories. Total cost is computed for each group considering suitable inventory policies and compared with other existing models. Findings – Fuzzy-rule-based multi-criteria classification model provides better results as compared to aggregate scoring and traditional ABC classification. This model offers the flexibility for inventory management experts to provide their subjective inputs. Practical implications – The web-based model developed in this paper can be implemented in various industries such as manufacturing, chemical plants, and mining, etc., which deal with large number of spares. This method classifies the spares into three categories A, B and ...

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

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

[3]  Claver Diallo,et al.  Integrated Spare Parts Management , 2009 .

[4]  Chi-Yang Tsai,et al.  A multiple objective particle swarm optimization approach for inventory classification , 2008 .

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

[6]  R. J. Kuo,et al.  A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network , 2002, Comput. Ind..

[7]  Ludo F. Gelders,et al.  An Inventory Policy for Slow and Fast Movers in a Petrochemical Plant: A Case Study , 1978 .

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

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  Shamsuddin Ahmed,et al.  Commonality and its Measurement in Manufacturing Resources Planning , 2009 .

[11]  Aris A. Syntetos,et al.  Classification for forecasting and stock control: a case study , 2008, J. Oper. Res. Soc..

[12]  T. Williams.  Stock Control with Sporadic and Slow-Moving Demand , 1984 .

[13]  Liliane Pintelon,et al.  Criticality classification of spare parts: A case study , 2012 .

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

[15]  Marcello Braglia,et al.  Multi‐attribute classification method for spare parts inventory management , 2004 .

[16]  Jin-Xiao Chen.  Peer-estimation for multiple criteria ABC inventory classification , 2011, Comput. Oper. Res..

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

[18]  Chandrasekharan Rajendran,et al.  Criticality analysis of spare parts using the analytic hierarchy process , 1994 .

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

[20]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[21]  L. A. Zadeh,et al.  Fuzzy logic and approximate reasoning , 1975, Synthese.

[22]  M. Z. Babai,et al.  Demand categorisation in a European spare parts logistics network , 2009 .

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

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

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

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

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