Criticality analysis of spare parts using the analytic hierarchy process

Gajpal, Prem Prakash; Ganesh, L.S.; Rajendran, Chandrasekharan

doi:10.1016/0925-5273(94)90095-7

Cited by 131 publications

(47 citation statements)

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“…Tsai and Yeh [31] uses the particle swarm optimization technique and presents an inventory classification algorithm that simultaneously search the optimum number of inventory classes and perform classification, while Mohammaditabar et al deploys the simulating annealing method [24] and proposes an integrated model to categorize the items and at the same time find the best policy. Saaty [30] has developed the Analytic Hierarchy Process (AHP) method, which has been widely deployed by some researchers to tackle the MCIC problem [4,7,8,27,28]. Other researchers [12,14,15] used a fuzzy version of the AHP method (FAHP).…”

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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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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“…The criteria that are often employed in multicriteria methods include lead time, commonality, obsolescence, and criticality [2,[5][6][7][8]. The analytic hierarchy process (AHP), distance-based modeling, and neural network techniques have also been used in different studies [6,[9][10][11]. In later studies, optimization models are used to classify inventory.…”

Section: Introductionmentioning

confidence: 99%

Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate

Iqbal

Malzahn

Whitman

2017

Advances in Decision Sciences

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Multicriteria models have been proposed for inventory classification in previous studies. However, it is important to make a decision when a particular multicriteria inventory classification model should be preferred over other models and also if the highest performing model remains the highest performing at all times. Companies always look for ways to improve customer order fulfillment process. This paper shows how better inventory classification can improve customer order fill rate in variable settings. The method to compare the inventory classification models with regard to improving customer order fill rate is proposed. The cut-off point is calculated which indicates when a model currently in use should be dropped in favor of another model to increase revenue by filling more orders. Sensitivity analysis is also performed to determine how holding cost and demand uncertainty affect the performance metric. Finally, regression analysis and hypothesis testing inform the decision-maker of how a model's performance differs from other models at various values of holding cost and standard deviation of demand.

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“…Gajpal [11] used AHP to perform criticality analysis. The model was used to obtain comparable metrics of criticality and to classify the items into three categories, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The CR is computed by dividing the CI by a value obtained from the table of Random Consistency Index (RI) created by Saaty . RI CI CI = (11) If the CR is less than 10 %, the comparisons are acceptable, otherwise not. RI represents the average index for randomly generated weights.…”

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confidence: 99%

Spare parts criticality analysis using a fuzzy AHP approach

Durán

2015

Teh. vjesn.

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Original scientific paper A distinctive characteristic of the spare parts inventory system is the need to evaluate or define the criticality of items in the inventory. Therefore, there is a need to consider factors such as cost, failure rate and availability, among others. There are few papers in literature that discuss relevant methods to define a reliable hierarchy of a set of spare parts. Up to now, no article has considered ambiguity and uncertainty factors when performing criticality analysis of spare parts. In addition, criticality analysis involves the simultaneous consideration of multiple criteria, including tangible and intangible factors; prioritizing these factors can be a great challenge and a complex task. Therefore, no attempt has been made to incorporate fuzziness into multicriteria decision-making in the criticality analysis in spare parts management. This work proposes a fuzzy-based methodology for prioritization of spare parts. The proposal is based on a multicriteria decision method called Analytical Hierarchy Process (AHP) with triangular numbers. An example is given to illustrate the proposed methodology. Keywords: Analytic Hierarchy Process (AHP); criticality; fuzzy AHP; spare parts Analiza kritičnosti rezervnih dijelova primjenom pristupa neizrazitog analitičkog hijerarhijskog procesIzvorni znanstveni članak Specifično obilježje sustava zalihe rezervnih dijelova je potreba da se procijeni ili odredi kritičnost stavki u zalihi. Stoga je, između ostalih, potrebno razmotriri čimbenike poput cijene, intenziteta kvarenja i raspoloživosti. Postoji mali broj radova u literaturi koji obrađuju relevantne metode za određivanje pouzdane hijerarhije u skupini rezervnih dijelova. Do sada, nijedan rad nije obradio čimbenike nesigurnosti i dvosmislenosti prilikom analize kritičnosti rezervnih dijelova. Uz to, analiza kritičnosti uključuje istovremeno razmatranje višestrukih kriterija, uključujući stvarne i nestvarne čimbenike; davanje prednosti tim čimbenicima može predstavljati veliki izazov i složen zadatak. Zbog toga se nije pokušalo uključiti neizrazitost (fuzziness) pri donošenju odluka o višestrukim kriterijima u analizi kritičnosti kod upravljanja rezervnim dijelovima. U ovom se radu predlaže fuzzy-temeljena metodologija kod određivanja prednosti (prioritizacije) rezervnih dijelova. Prijedlog se zasniva na metodi donošenja odluke na temelju višestrukih kriterija nazvanoj Analitički Hijerarhijski Proces (AHP) s trokutastim (triangular) brojevima. Daje se primjer za ilustraciju ove metodologije.

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Criticality analysis of spare parts using the analytic hierarchy process

Cited by 131 publications

References 1 publication

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

Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate

Spare parts criticality analysis using a fuzzy AHP approach

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