ABC classification according to Pareto’s principle: a hybrid methodology

Kheybari, Siamak; Naji, Sara; Rezaie, Fariba Mahdi; Salehpour, Reza

doi:10.1007/s12597-019-00365-4

Cited by 23 publications

(21 citation statements)

References 35 publications

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“…Closely associated with the Pareto principle is an ABC classification. This classification has become a popular business metrics, especially for inventory management [ 39 , 40 ]. The division of our relations into 3 segments, namely: A - the most important, B - moderately important, and C relatively unimportant, identifies ranges of epidemic intensity with differing effects on public health.…”

Section: Methodsmentioning

confidence: 99%

“…The Theil-Sen procedure is acknowledged to be the most common nonparametric technique for estimating a linear trend [28]. The objective of this method is to fit a line to sample points robustly by choosing the median of the slopes of all lines through pairs of points [32][33][34][35][36][37][38][39][40][41][42][43][44]. The Theil-Sen estimator is considerably more accurate than non-robust simple linear regression, especially for skewed and heteroskedastic data.…”

Section: Design and Data Analysismentioning

confidence: 99%

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Mutual relationships between SARS-CoV-2 test numbers, fatality and morbidity rates

2021

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Background The number of SARS-CoV-2 tests conversely to other factors, such as age of population or comorbidities, influencing SARS-CoV-2 morbidity and fatality rates, can be increased or decreased by decision makers depending on the development of the pandemic, operational capacity, and financial restraints. The key objective of this study is to identify and describe, within the probabilistic approach, the relationships between SARS-CoV-2 test numbers and the mortality and morbidity rates. Methods The study is based on a statistical analysis of 1058 monthly observations relating to 107 countries, from six different continents, in an 11-month period from March 2020 to January 2021. The variable utilised can be defined as the number of tests performed in a given country in 1 month, to the number of cases reported in a prior month and morbidities and mortalities per 1 million population. The probabilities of different mortality and morbidity rates for different test numbers were determined by moving percentiles and fitted by the power law and by the three-segment piecewise-linear approximation based on Theil Sen trend lines. Results We have identified that for a given probability the dependence of mortality and morbidity rates on SARS-CoV-2 test rates follows a power law and it is well approximated by the three Theil Sen trend lines in the three test rate ranges. In all these ranges Spearman rho and Kendall tau-b rank correlation coefficients of test numbers and morbidity with fatality rates have values between − 0.5 and − 0.12 with p-values below 0.002. Conclusions According to the ABC classification: the most important, moderately important, and relatively unimportant ranges of test numbers for managing and control have been indicated based on the value of the Theil Sen trend line slope in the three SARS-CoV-2 test rate ranges identified. Recommendations for SARS-CoV-2 testing strategy are provided.

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Section: Methodsmentioning

confidence: 99%

Section: Design and Data Analysismentioning

confidence: 99%

Mutual relationships between SARS-CoV-2 test numbers, fatality and morbidity rates

2021

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“…Zowid et al (2019) and Kartal et al (2016) proposed the concept of multi-criteria and warehousing digitization to optimize the classification of ABC, which could effectively control the operation and efficiency of the storage. As for methods and models, for example, Kheybari et al (2019) proposed to optimize the classification of ABC by entropy of multiple criteria and the technique for order preference by similarity to ideal solution (TOPSIS). Flores et al (1992) proposed that ABC classification method could use multi-criteria AHP for effective material management and control in inventory management.…”

Section: Literature Studiesmentioning

confidence: 99%

A New Method of Storage Management Based on ABC Classification: A Case Study in Chinese Supermarkets’ Distribution Center

Lin

2021

SAGE Open

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Most of the distribution centers of chain supermarkets in China adopt the excessively simple activity-based classification (ABC) as the management method of classifying warehousing work, which often leads to an increase in operation and storage costs of the enterprise, a decrease in efficiency of the commodity circulation operation, and, eventually, a loss in enterprise growth because the goods ordered by stores cannot be delivered immediately. In addition, many studies in recent years have aimed at solving the problems of ABC classification by using the concept, method, or model of multi-criteria; however, limitations can be found in these studies. The main purpose of this study is to propose a new method, based on ABC classification, to improve the shortcomings of traditional classification methods, especially for special industries such as chain supermarkets. First, the case was classified using the ABC classification method as designed according to the actual situation of the case, and then the modified Delphi method, factor analysis, and analytic network process (ANP) decision model constructed by focus group was applied. Finally, ANP is used to put forward a specific improvement scheme and countermeasures for the storage management classification plan of the case. Last but not least, to verify the applicability of the model, an actual case was used to test the model. The results show the reference value and practicability of the proposed model. Furthermore, it can be offered as a set of systematic and scientific decision-making reference standards for managers and decision-makers of chain supermarkets when making warehouse management plans and standards.

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“…In this method, the cost and demand of items, depot repair cycle time, repair cycle time at the base, depot-to-base resupply time, and the probability of items being repaired at the base are considered to be classification criteria. Kheybari et al (2019) used a combination of methods including entropy, the technique for ordering preferences based on similarity to ideal solution (TOPSIS), and goal programming to determine classification criteria weights. Then, the value of each item is calculated.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kheybari et al ( 2019 ) used a combination of methods including entropy, the technique for ordering preferences based on similarity to ideal solution (TOPSIS), and goal programming to determine classification criteria weights. Then, the value of each item is calculated.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

2020

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Inventory classification is a fundamental issue in the development of inventory policy that assigns each inventory item to several classes with different levels of importance. This classification is the main determinant of a suitable inventory control policy of inventory classes. Therefore, a great deal of research is done on solving this problem. Usually, the problem of inventory classification is considered in a multi-criteria and uncertain environment. The proposed method in this paper inspired by the notion of heterogeneous decision-making problems in which decision-makers deal with different types of data. To this aim, a mathematical modeling-based approach is proposed considering different types of uncertainty in classification information. Demand information is considered to be stochastic due to its time-varying nature and cost information is considered to be fuzzy due to its cognitive ambiguity. A hybrid algorithm based on chance-constrained and possibilistic programming is proposed to solve the problems. Considering the stochastic nature of demand information, solving the proposed model using the hybrid algorithm, the classification of items to three classes of extremely important, class A, moderately important, class B, and relatively unimportant, class C, items are determined along with a minimum inventory level required to deal with the stochasticity of demands information. The proposed approach is applied to a case study of classifying 51 inventory items. The obtained results assigned 22%, 39%, and 39% of the items to A, B, and C classes, respectively. Keywords Multi-criteria inventory classification Á Chance-constrained programming Á Possibilistic programming Communicated by V. Loia.

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ABC classification according to Pareto’s principle: a hybrid methodology

Cited by 23 publications

References 35 publications

Mutual relationships between SARS-CoV-2 test numbers, fatality and morbidity rates

Mutual relationships between SARS-CoV-2 test numbers, fatality and morbidity rates

A New Method of Storage Management Based on ABC Classification: A Case Study in Chinese Supermarkets’ Distribution Center

A hybrid fuzzy-stochastic multi-criteria ABC inventory classification using possibilistic chance-constrained programming

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