A classification approach based on the outranking model for multiple criteria ABC analysis

Liu, Jiapeng; Liao, Xiuwu; Zhao, Wenhong; Yang, Na

doi:10.1016/j.omega.2015.07.004

Cited by 70 publications

(30 citation statements)

References 39 publications

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“…Based on the enterprise survey, we found that there are many types of fragile parts in household appliances that must be managed using different methods. The ABC analysis method is an important and easy way to classify inventory items and has been improved in many ways (Roda et al., ; Liu et al., ; Li et al., ). In this paper, we focus on the shortage portion; therefore, we use the demand information and unit value but not the total value to perform the classification.…”

Section: Numerical Examples and Resultsmentioning

confidence: 99%

System dynamics simulation‐based model for coordination of a three‐level spare parts supply chain

Zhao

Chang

et al. 2019

Int Trans Operational Res

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In some spare parts distribution systems for consumption goods, a portion of consumers’ demand is met by inventories in maintenance outlets, while the remaining portion is satisfied with emergency orders from the supplier, which can enhance customers’ satisfaction but cause added order‐processing costs and transportation costs. Faced with the trade‐off between the supplier's and outlet's profits, it is highly crucial to design and coordinate a supply chain in a way that determines to what extent each level should hold inventories by considering classification of spare parts. Therefore, in this paper, a system dynamics approach is used to design a three‐level supply chain simulation model consisting of one business unit, one distribution center, and one maintenance outlet. The business unit and distribution center belong to one company, so we consider them as one supplier. Therefore, we focus on the coordination between the distribution center and the maintenance outlet. The objective is to propose a contract to enhance these two parties’ profits. Through numerical analysis, we provide a contract with which the spare parts in class A are differentiated in their normal and emergency procurement prices and the spare parts in class B and class C have a prescribed customer service level. Using this type of contract, both distribution center's and maintenance outlet's profits can be enhanced, and the management process can be simplified.

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Section: Numerical Examples and Resultsmentioning

confidence: 99%

System dynamics simulation‐based model for coordination of a three‐level spare parts supply chain

Zhao

Chang

et al. 2019

Int Trans Operational Res

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“…The other criteria have only been reported in a few (mostly one or two) studies related to the multiple criteria inventory classification problem, i.e. ordering cost [66][67], substitutability [19] [47], replaceability [19] [47], perishability [8], storage cost [8], current item status [26], severity of the impact of its running out [26], number of hits [53], average value per hit [53], payment terms [19], durability [54][55] [73], limitations of warehouse space [49], last use date [54][55], supplier [54], and turnover rate [60]. We note that some proposed models offer more flexibility about the criteria to be included.…”

Section: Classification Criteria and Methodsmentioning

confidence: 99%

“…Some other approaches apply mixed approaches, e.g. AHP and the k-means algorithm [61], FAHP and ANN [55], FAHP and DEA [49], CA and SAA [60]. Concerning the nature of the output shown in Table 1, most of the approaches generate a grouping of the spare parts intothreeclasses: C,B andA.Unfortunately,thegeneratedoutputsin thesecasescannot beusedto classifynew/unseen items.…”

Section: Optimizationmentioning

confidence: 99%

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Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Chakhar

Siraj

et al. 2017

European Journal of Operational Research

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Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of 'if-then' decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the crossvalidation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision-making process.

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“…Al-Obeidat et al [1] proposed an hybrid model which combine Differential Evolution method with the PROAFTN method, by using the evolutionary algorithm to inductively obtain PROAFTN's parameters from data to achieve a high classification accuracy. Liu et al [21] combined the methods of Electre III and the simulating annealing to deal with the compensatory effect of the items against criteria and opted for grouping criteria. A new MCDM method of Evaluation based on Distance from Average Solution (called EDAS) [19] is introduced by calculating the best alternative according to the distance from positive and negative solutions.…”

Section: Introductionmentioning

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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A classification approach based on the outranking model for multiple criteria ABC analysis

Cited by 70 publications

References 39 publications

System dynamics simulation‐based model for coordination of a three‐level spare parts supply chain

System dynamics simulation‐based model for coordination of a three‐level spare parts supply chain

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

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

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