ABC Inventory Classification Research Articles

A Weighted Least-Square Dissimilarity Approach for Multiple Criteria ABC Inventory Classification

The present paper develops a weighted least-square dissimilarity approach to address the Multiple Criteria ABC inventory classification problem, when the different criteria ranking makes impossible to achieve a group consensus. The proposed approach effectively eliminates drawbacks regarding subjective judgements of multiple decision makers on the criteria importance, and comprehensively aggregates all rankings of the criteria importance to provide a more reasonable and effective classification mechanism. The common weights associated with all rankings are determined. An illustrative example is presented to compare the performance of our approach with the existing studies.

Group multiple criteria ABC inventory classification using TOPSIS approach extended by Gaussian interval type-2 fuzzy sets and optimization programs

The traditional ABC inventory classification is one of such approaches in which items are classified into three classes: A, very important; B, moderately important; and C, relatively unimportant based on annual dollar usage. However, other qualitative or quantitative criteria in real world may affect grouping items in the ABC inventory classification. Hence, multiple criteria ABC inventory classification (MCABCIC) is applied to classify items, instead of using the traditional ABC inventory classification. Hence, it can be taken into account as a multiple-criteria decision making (MCDM) problem due to using different criteria. The aim of this paper is to the extent TOPSIS approach with Gaussian interval type-2 fuzzy sets (GIT2FSs) as an alternative to the traditional triangular membership functions (MFs) for the MCABCIC in which GIT2FSs are more suitable for stating curved MFs. For this purpose, a new limit distance is presented for prioritizing GIT2FSs which is based on alpha cuts. The proposed method determines the positive and negative ideal solutions as left and right reference limits and then calculates distances between assessments and these limits. In our approach, the weights of the quantitative and qualitative criteria are attained via two linear programming, respectively. The model is illustrated using a real case.

Flexible ABC Inventory Classification

ABC inventory classification is a well-known approach to assign inventory item into A, B, and C groups based on their sales and usage volume. This helps inventory management become more efficient. Behind its advantage, it usually shows some problems with an inventory budget and warehouse space because the ABC assignment of SKUs are mad e without an inventory budget and space available involved. In this paper, the ABC group under restricted of an inventory budget and warehouse space to maximize the profit with optimal service level is presented. We establish this proposed model to enhance the existing ABC approach to be more applicable in real life, which has the limited inventory budget and warehouse space. Keywords: ABC Inventory Classification; Inventory Management

An improvement to multiple criteria ABC inventory classification using Shannon entropy

This paper extends the Ng-model [Ng, 2007] for multiple criteria ABC inventory classification based upon Shannon entropy. The proposed approach determines the common weights associated with all criteria importance rankings, and provides a comprehensive scoring scheme by aggregating all rankings of the criteria importance. A numerical illustration is presented to compare the model with previous studies.

Multicriteria ABC inventory classification using acceptability analysis

AbstractThis paper introduces a new procedure for handling the multicriteria ABC inventory classification problem using stochastic multicriteria acceptability analysis. All possible preferences among the evaluation criteria have been considered. Due to the fact that even under certain preference, it is difficult to reach a group consensus on the exact weight values along with each criterion, we calculate preference‐specific intervals under each preference and then formulate a stochastic decision‐making problem. To tackle this problem, we consider different distribution functions of the intervals and then compute the holistic acceptability indices to classify stock‐keeping units. The results derived from our method are compared to the previous results to show the robustness and superiority of our method.

A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem

In this paper we deal with the problem of designing a classifier able to learn the classification of existing units in inventory and then use it to classify new units according to their attributes in a multi-criteria ABC inventory classification environment. To solve this problem we design a multi-start constructive algorithm to train a discrete artificial neural network using a randomized greedy strategy to add neurons to the network hidden layer. The process of weights’ searching for the neurons to be added is based on solving linear programming formulations. The computational experiments show that the proposed algorithm is much more efficient when the dual formulations are used to find the weights of the network neurons and that the obtained classifier has good levels of generalization accuracy. In addition, the proposed algorithm can be straight applied to other multi-class classification problems with more than three classes.

Proposed Model for Inventory Review Policy through ABC Analysis in an Automotive Manufacturing Industry

A global market inventory is one of the largest and most significant resources of a manufacturing business. ABC analysis is one of the methods used extensively in manufacturing industries for inventory classification. The familiar ABC inventory classification approach categorizes inventory items as A, B and C classes according to their annual consumption value. To direct and control the inventory items more proficiently the inventories managers regularly classify and group all the inventory items. The objective of this paper is to create a periodic review policy based on the ABC classification in order to control the raw materials more efficiently. Based on the data collected through original equipment manufacturer, the necessary analysis was carried out. From the observed results, it was found that the developed periodic review policy improved the inventory turnover ratio from 3.15 to 2.13 by optimizing the inventory level for one year.

A Clustering Classification of Spare Parts for Improving Inventory Policies

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.

Multi-criteria inventory classification approach to inventory control based on maximum value criteria normalisation method: a weightless perspective

Effective inventory management is crucial to responsive supply chains. ABC inventory classification is an effective approach to stratifying stock-keeping units, according to classes based on a single criterion or multiple criteria. In this paper, we proposed an equal-weight, rapidly deployable model for inventory classification based on simple criteria transformation, which is a linear transformation of the criteria values. The model is quick and easy to develop and effective in classifying inventory. A pairwise correlation comparison shows the proposed model to be significantly positively correlated with most of the existing models compared. The proposed model also shows better performance, in terms of hits, than most of the models compared. This research also provides practitioners and managers with a model substitutability table by comparing model correlation or class hits.

Multi-criteria inventory classification approach to inventory control based on maximum value criteria normalisation method: a weightless perspective

Effective inventory management is crucial to responsive supply chains. ABC inventory classification is an effective approach to stratifying stock-keeping units, according to classes based on a single criterion or multiple criteria. In this paper, we proposed an equal-weight, rapidly deployable model for inventory classification based on simple criteria transformation, which is a linear transformation of the criteria values. The model is quick and easy to develop and effective in classifying inventory. A pairwise correlation comparison shows the proposed model to be significantly positively correlated with most of the existing models compared. The proposed model also shows better performance, in terms of hits, than most of the models compared. This research also provides practitioners and managers with a model substitutability table by comparing model correlation or class hits.

A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method

ABC analysis is one of the most widely used techniques in inventory management to classify items into three predefined and ordered categories: A (very important items), B (moderately important items) and C (relatively unimportant items). In the literature, most of existing classification models tackled the ABC inventory classification problem as a ranking problem, i.e. a set of inventory items are ranked in a deceasing order based on their performance expressed by an overall weighted score. In this paper, the ABC inventory classification problem will be tackled as an assignment problem, i.e. an inventory item will be classified to the category with which it has the most similar characteristics. For this purpose, the PROAFTN method will be used to classify each inventory item into a specific category. Since the application of PROAFTN method requires the knowledge of some parameter values (e.g. prototypes pessimistic intervals and discrimination thresholds), the Chebyshev's theorem is used for their estimation. To determine the performance of the PROAFTN model with respect to some existing models, a benchmark data set from the literature is used.

Root cause analysis of familiarity biases in classification of inventory items based on logical patterns recognition

Multi-features ABC inventory classification (MCIC) is targeted to optimize inventory management through inventory items classification in order to set policies and rules to manage them. A flawed classification of items may lead to financial losses and customers dissatisfaction. This paper presents a technique for the identification and the correction of the bias that exists in the ABC items classification done by inventory experts and decision makers. In this study, the classification familiarity bias is found and rectified through patterns recognition. A pattern based reclassification is proposed using a multi-class model based on Logical Analysis of Data (LAD). Accuracy prediction tests are conducted in order to evaluate the proposed pattern based classification. The results are compared with the classification obtained based on the Euclidean distance.

A distance‐based decision‐making method to improve multiple criteria ABC inventory classification

AbstractThe present paper extends the Ng‐model for multiple criteria ABC inventory classification based on a distance‐based decision‐making method. The proposed method determines the common weights associated with all rankings of the criteria importance, and then provides a comprehensive scoring scheme by aggregating all rankings. A numerical illustration is presented to compare our computational results with previous studies.

Learning criteria weights with TOPSIS method and continuous VNS for multi-criteria inventory classification

The ABC analysis is a well-known methodology used in inventory management to classify inventory items into three predefined and ordered categories A, B and C: category A contains the most valuable items and category C contains the least valuable ones. The aim of this analysis is to keep - by focusing on the few critical items (of category A) - related inventory costs under control within a supply chain. According to ABC analysis, the classification of items into one of the above categories is based on their weighted scores. The score of each item is the result of an aggregation function that combines the item evaluations on the different criteria and the criteria weights. In this paper, we propose an Automatic Learning Method (ALM) that infers the criteria weights in order to produce a classification of items that minimizes an inventory cost function. The proposed ALM is based on TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) to compute the score of each item and a Continuous Variable Neighborhood Search (CVNS) to infer the criteria weights. To test the performance of the proposed ALM with respect to some others ABC inventory classification models, a benchmark data set of 47 items from an Hospital Respiratory Therapy Unit is used.

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.

Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification

Multi-criteria ABC inventory classification (MCIC), which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. This paper suggests a cross-evaluation-based weighted linear optimization (CE-WLO) model for MCIC that incorporates a cross-efficiency evaluation method into a weighted linear optimization model for finer classification (or ranking) of inventory items. The present study demonstrated the inventory-management-cost effectiveness and advantages of the proposed model using a simulation technique to conduct a comparative experiment with the previous, related investigations. We established that the proposed model enables more accurate classification of inventory items and better inventory management cost effectiveness for MCIC, specifically by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.

Multi-criteria ABC inventory classification using DEA-discriminant analysis to predict group membership of new items

Inventory management plays a significant role in organisations’ success or failure. ABC inventory classification is one of the most popular methods which are regularly applied in inventory management. Correct clustering of inventory items is an important issue of inventory management. The ‘annual cost’ is an important factor in most of previous studies which applied ABC inventory classification. Each item which has higher annual cost is placed in class A. This paper shows that other factors have significant role for classifying inventory items. We use data envelopment analysis (DEA) to classify inventory items into three groups as A, B, or C in the presence of weight restrictions. Weight restrictions allow for the integration of managerial preferences in terms of relative importance of various factors. Then, to predict group membership of new items, the DEA is incorporated with discriminant analysis (DA). To demonstrate applicability of proposed approach a case study is presented.

Optimizing ABC inventory grouping decisions

Inventory managers often group inventory items into classes to manage and control them more efficiently. The well-known ABC inventory classification approach categorizes inventory items into A, B and C classes according to their sales and usage volume. In this paper, we present an optimization model to enhance the quality of inventory grouping. Our model simultaneously optimizes the number of inventory groups, their corresponding service levels and assignment of SKUs to groups, under limited inventory spending budget. Our methodology provides inventory and purchasing managers with a decision-support tool to optimally exploit the tradeoff among service level, inventory cost and net profit. The model and solution are applied for an inventory classification project of a real-life company, and outperform the traditional ABC method. Computational experiments are performed to obtain managerial insights on optimal inventory grouping decisions.

Optimal ABC inventory classification using interval programming

Inventory classification is one of the most important activities in inventory management, whereby inventories are classified into three or more classes. Several inventory classifications have been proposed in the literature, almost all of which have two main shortcomings in common. That is, the previous methods mainly rely on an expert opinion to derive the importance of the classification criteria which results in subjective classification, and they need precise item parameters before implementing the classification. While the problem has been predominantly considered as a multi-criteria, we examine the problem from a different perspective, proposing a novel optimisation model for ABC inventory classification in the form of an interval programming problem. The proposed interval programming model has two important features compared to the existing methods: it provides optimal results instead of an expert-based classification and it does not require precise values of item parameters, which are not almost always available before classification. Finally, by illustrating the proposed classification model in the form of numerical example, conclusion and suggestions for future works are presented.

Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria

Large firms usually employ the well-known ABC inventory classification technique to have an efficient control on a huge amount of inventory items. While the traditional inventory classification method only considers one criterion, namely the annual dollar usage to classify inventory items, the recent literature shows a focus on multi-criteria inventory classification (MCIC) techniques. Surprisingly, among several methods that have been developed for the MCIC problem, there are just a few, which clearly account for qualitative criteria, while most of the affecting criteria are of qualitative type in nature. This paper presents a modified linear optimisation method that enables inventory managers to classify a number of inventory items in the presence of both qualitative and quantitative criteria without any subjectivity. Furthermore, an efficient procedure is proposed to maximise the minimum importance attached to various criteria leading to improvement of discriminating power among inventory items. For validating the proposed method, it is applied on a case study taken from the literature and a comparative study with the existing competent methods is also provided.