A note on multi-criteria ABC inventory classification using weighted linear optimization

Zhou, P.; Fan, Liwei

doi:10.1016/j.ejor.2006.08.052

Cited by 177 publications

(136 citation statements)

References 3 publications

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“…This data set is displayed in the Table 1. The ABC classification results of the existing ABC classification models (R model [29], ZF model [33], Chen model [5], H model [11], NG model [25], ZF-NG model [20] and ZF-H model [13]) and our model are showed also in Table 1. Note that all the established classifications respect the same ABC distribtion, with 10 items in the class A, 14 items in the class B and 23 items in the class C. We clearly observe that our proposed approach provides a more efficient classification cost (833.677) than all other models presented from the literature, with a good Fill Rate (0.972) reflecting a good classification.…”

Section: Resultsmentioning

confidence: 99%

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

Cherif

Ladhari

2016

Communications in Computer and Information Science

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Abstract. ABC analysis is a well-established categorization technique based on the Pareto Principle which dispatches all the items into three predefined and ordered classes A, B and C, in order to derive the maximum benefit for the company. In this paper, we present a new approach for the ABC Multi-Criteria Inventory Classification problem based on the Artificial Bee Colony (ABC) algorithm with the Multi-Criteria Decision Making method namely VIKOR. The ABC algorithm tries to learn and optimize the criteria weights, which are then used as an input parameters by the method VIKOR. The MCDM method generates a ranking items and therefore an ABC classification. Each established classification is evaluated by an estimation function, which also represents the objective function. The results of our proposed approach were obtained from a widely used data set in the literature, and outperforms the existing classification models from the literature, by obtaining better inventory cost.

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Section: Resultsmentioning

confidence: 99%

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

Cherif

Ladhari

2016

Communications in Computer and Information Science

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“…Ng (2007) addresses this issue by proposing model like Ramanathan's, yet which is at that point changed into another arrangement of issues, the structure of which makes it simple to perceive the ideal arrangement. Zhou and Fan (2007) proposed an extended version of such weighted linear optimization for multi-criteria stock classification. Ozan and Mustafa (2008) proposed a stock grouping framework based on the fuzzy AHP to help a sensible multi-criteria stock arrangement.…”

Section: Introductionmentioning

confidence: 99%

Decision making with association rule mining and clustering in supply chains

Agarwal¹

2017

10.5267/j.ijdns

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This paper deals with data mining applications for the supply chain inventory management. ABC characterization is typically utilized for stock items arrangement on the grounds that the quantity of stock items is large to the point that it is not computationally practical to set stock and administration control rules for every individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could influence the sale of other items (cross selling effect). Consequently, within each cluster, the inventories should be classified. In this paper, a modified approach is proposed considering both cross-selling effect and clusters to rank stock items. A numerical case is utilized to clarify the new approach. It is represented that by utilizing this modified approach; the ranking of items may get influenced bringing about higher profits.

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“…Zhou & Fan [19] extended the same model and provided a more reasonable and encompassing criterion using two sets of weights, denoted as the most favourable and least favourable, for each item. Chen [20] proposed a peer-estimation approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic TOC-based approach for planning and control of accessories in MTO environments

Keyvani

Lotfi

2018

Scientia Iranica

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In make-to-order systems, customers expect to have more freedom to choose the accessories. But demand variations and internal disorders induce some uncertainties. Hence, a different inventory system is needed for such items that dynamically manage those variations. In this paper, a dynamic approach based on the theory of constraints is proposed for inventory planning and control of accessories. First, the risk of processing time variation is balanced while keeping cycle time balancing. Second, the ribbons of buffer control charts are determined by a buffer planning model in which a multi-criteria ABC analysis is used to apply different customer service levels.To detect demand variations and monitor the buffer, trend of consumption in each monitoring window is carefully traced. Also, simulation-based procedures are recommended to update control ribbons. Comparing the performance of proposed approach to common methods using the data of an automobile company as well as several random test problems confirms that it can significantly reduce the costs and improve the efficiency of inventory system.

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A note on multi-criteria ABC inventory classification using weighted linear optimization

Cited by 177 publications

References 3 publications

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

A Novel Multi-criteria Inventory Classification Approach: Artificial Bee Colony Algorithm with VIKOR Method

Decision making with association rule mining and clustering in supply chains

Dynamic TOC-based approach for planning and control of accessories in MTO environments

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