Criteria Inventory Classification Research Articles

A common-weight DEA model for multi-criteria ABC inventory classification with quantitative and qualitative criteria

ABC analysis is a famous technique for inventory classification. However, this technique on the inventory classification only considering one indicator even though other important factors may affect the classification. To address this issue, researchers have proposed multiple criteria inventory classification (MCIC) solutions based on data envelopment analysis (DEA)-like methods. However, previous models almost evaluate items by different weight sets, and the index system only contains quantitative criteria and output indicators. To avoid these shortcomings, we propose an improved common-weight DEA model for MCIC issue. This model simultaneously considers quantitative and qualitative criteria as well as establishes a comprehensive index system that includes inputs and outputs. Apart from its improved discriminating power and lack of subjectivity, this non-parametric and linear programming model provides the performance scores of all items through a single computation. A case study is performed to validate and compare the performance of this new model with that of traditional ABC analysis, DEA–CCR and DEA–CI. The results show that apart from the highly improved discriminating power and significant reduction in computational burden, the proposed model has achieved a more comprehensive ABC inventory classification than the traditional models.

A Novel Approach Based on Combining ANFIS, Genetic Algorithm and Fuzzy c-Means Methods for Multiple Criteria Inventory Classification

Multi-criteria inventory classification (MCIC) is a widely used inventory classification method that groups inventory items with respect to several criteria, in order to facilitate their management. Many researchers have been used several methods to solve MCIC problem. However, some of them are quite complex to understand and are not capable of handling qualitative data which is impractical in today’s manufacturing conditions. In addition, one of the most common problem is that, in most of the existing methods, when a new inventory item is stored in a warehouse, the classification process must be repeated. In this paper, a new hybrid model generated by genetic algorithm (GA), fuzzy c-means (FCM) and adaptive neuro-fuzzy inference system (ANFIS) is proposed for inventory classification. To create this model, three steps are followed up which are optimizing FCM algorithm by using GA, clustering the data set with FCM algorithm and generating the ANFIS classification model. This model does not need to be regenerated to solve the classification problem whenever a new inventory item is introduced. The model is also capable of handling both quantitative and qualitative criteria. The proposed model is applied to a real-life problem. Results of the model are compared with those of artificial neural network (ANN) model. The comparison showed that the proposed model is more successful than the ANN model.

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.

A Common Weight Linear Optimization Approach for Multicriteria ABC Inventory Classification

Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.

Integrating Fuzzy Delphi with Fuzzy Analytic Hierarchy Process for Multiple Criteria Inventory Classification

A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order to efficiently control the inventory items and to determine the suitable ordering policies for them, multiple criteria inventory classification is used. In this paper, a systematic and logical approach is structured for multiple criteria inventory classification through integrating Fuzzy Delphi Method (FDM) with Fuzzy Analytic Hierarchy Process (FAHP). Fuzzy Delphi method used to identify the most important and significant criteria and, Fuzzy AHP is used to determine the relative weights of the attributes or criteria, and to classify inventories into different categories. To accredit the proposed model, it is implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh. Implementation results show that the proposed method can be used in inventory classification.

Multi-criteria inventory classification through integration of fuzzy analytic hierarchy process and artificial neural network

A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. To efficiently control the inventory items and to determine the suitable ordering policies for them, multi- criteria inventory classification is used. The objective of this research is to develop a multi-criteria inventory classification model through integration of fuzzy analytic hierarchy process (FAHP) and artificial neural network approach. FAHP is used to determine the relative weights of the attributes or criteria using Chang's extent analysis and to classify inventories into different categories. Various structures of multi-layer feed-forward back-propagation neural networks have been analysed and the optimal one with the minimum mean absolute percentage of error between the measured and the predicted values have been selected. To accredit the proposed model, it is implemented for 351 raw materials of switchgear section of Energypac Engineering Limited, a large power engineering company of Bangladesh.

An improvement of two multi-criteria inventory classification models

ABC analysis is a useful method to classify inventory items into different categories that can be specifically managed and controlled. Traditional ABC analysis classifies inventory items into three categories according to the annual dollar usage of an inventory item. Multi criteria inventory classification models have been proposed by researchers in order to take into account other important criteria. From these models we considered in this paper the R- model and the Ng-model in order to improve them. Illustrative examples are presented to compare the improved models and the initial ones. Keywords: ABC classification, Multi criteria inventory classification models, R-model, Ng-model. I. Literature review Companies often manage a large number of items. It is difficult to give all these items, the same level of monitoring and control. For this reason, managers should set priority levels for each category of items. Once this hierarchy established, we can assign specific business rules to each family of items. In this field, the ABC classification is mostly used by managers for inventory control. The classical ABC classification is based on the Pareto principle and according to the criterion of the annual use value. In fact, Class A items are relatively few in number (between 10% and 20%) but they represent between 70% and 80% of the total annual use value. The Class A items represents a great annual use value. For this reason, control of items of this class must be rigorous and tight. Class B contains between 30% and 40% of items that represent 15% to 20% of the total annual use value. The inventory control of class B items can be less tight than that of the first class A. Finally class C items can reach 50% of the items but represent only 5% to 10% in terms of value. Managers can reduce their effort in controlling these items because they are numerous but too small in terms of total annual use value. When handling inventory classification, managers need to consider at the same time many important classification factors. Several Methodologies have been proposed in the literature for the multi- criteria inventory classification problem (MCIC), but we will focus on the weighted optimization models. These models used a weighted additive function to aggregate the performance of an inventory item in terms of different criteria to a single score, called the inventory score of an item. Ramanathan (2006) proposed a linear model which gives each item an opportunity to choose a set of weights favorable to the item itself when maximizing its score. To improve this model proposed by Ramanathan (2006), Zhou and Fan (2007) suggested a new classification based on a composite index. This score or index is a combination of a good index (maximizing score) and a bad index (minimizing score). The items classification will be then based on the calculated composite index. Ng (2007) proposed a linear programming model for the MCIC using a scalar measuring for all the criteria and with the main contribution of giving an importance order to the criteria. Hadi-Vencheh (2010) presented a nonlinear programming model as an extension version of the Ng-model. This model changed in the linear programming the constraint of the criterion weights by considering squared coefficients. Finally, Chen (2011) developed an improved approach to MCIC by which all items are peer-estimated. This new approach first determines two performance scores for each inventory item, then we determine a weight for each score in order to calculate a final aggregated index that will be used for the classification. In this paper we will suggest firstly, a modified variant of R-model proposed by Ramanathan (2006) for MCIC. In this version, called R*-model, we consider the normalization step of the data to measure and analyze the impact of this pre-processing on the initial model. In the second part of this work, we will propose a new weighted linear optimization model which is an improvement of the Ng- model developed by Ng (2007). This new model will be referred to as M-Ng model.

Multiple Criteria Inventory Classification Under Fuzzy Environment

A systematic approach to the inventory control and classification may have significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order control the inventory items and to determine the suitable ordering policies for them, multiple criteria inventory classification is used. A systematic and logical approach is structured for multiple criteria inventory classification under fuzzy environment through integrating Fuzzy Delphi Method (FDM) with Fuzzy Analytic Hierarchy Process (FAHP). Fuzzy Delphi method is used to identify the important criteria and, Fuzzy AHP is used to determine the relative weights of the criteria, and to classify inventories into different categories. To accredit the proposed model, its implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh.

ABC inventory classification in the presence of both quantitative and qualitative criteria

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.

Multiple criteria ABC inventory classification using two virtual items

ABC inventory classification is one of the most popular techniques for organisations to efficiently plan and control thousands of inventory items. Its traditional way is solely based on a single criterion; however, it has been recognised that multiple criteria need to be considered in practice. An alternative approach to multiple criteria inventory classification (MCIC) is proposed by using two virtual items and incorporating the TOPSIS. The proposed approach improves some previous allied methods as it provides a more reasonable and comprehensive performance index and a unique inventory classification without any subjectivity. Comparisons with other allied methods are illustrated through a case study.

A simple classifier for multiple criteria ABC analysis

To have an efficient control of a huge amount of inventory items, traditional approach is to classify the inventory into different groups. Different inventory control policies can then applied to different groups. The well-known ABC classification is simple-to-understand and easy-to-use. However, ABC analysis is based on only single measurement such as annual dollar usage. It has been recognized that other criteria are also important in inventory classification. In the paper, we propose a simple model for multiple criteria inventory classification. The model converts all criteria measures of an inventory item into a scalar score. The classification based on the calculated scores using ABC principle is then applied. With proper transformation, we can obtain the scores of inventory items without a linear optimizer. The model can be widely applied to inventory managers with minimal backgrounds in optimization.