Classifying inventory using an artificial neural network approach
Computers and Industrial Engineering
ABC inventory classification with multiple-criteria using weighted linear optimization
Computers and Operations Research
Peer-estimation for multiple criteria ABC inventory classification
Computers and Operations Research
Management of multicriteria inventory classification
Mathematical and Computer Modelling: An International Journal
Multi-criteria inventory classification with reference items
Computers and Industrial Engineering
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Organizations classically employ the ABC analysis to have an efficient control on a large number of inventory items. The customary classification method considers just one criterion, i.e., the annual dollar usage to classify inventory items. Recently, several methods have been developed for ABC inventory classification, especially DEA-like models that account for other important criteria leading to more logical results in practice. However, these models assume that all criteria are of quantitative type and hence cannot handle the qualitative criteria which are not stated numerically but as linguistic terms. To alleviate this shortcoming, this paper proposes a modified version of an existent common weight DEA-like model by using of some concepts in the current imprecise DEA (IDEA) models and then applies it for ABC inventory classification in the case where there exist both quantitative and qualitative criteria. The merits of employing the modified model to solve the multi criteria inventory classification (MCIC) problem are discussed. A case example is also illustrated to demonstrate the applicability of the modified model in the context of MCIC problem as well as its superiority over existing approaches.