An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain

Büyüközkan, Gülçin; Göçer, Fethullah

doi:10.1016/j.asoc.2018.04.040

Cited by 115 publications

(65 citation statements)

References 63 publications

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“…Recently, attention paid to the ARAS method, a relatively novel tool for MCDM (Turskis & Zavadskas, 2010a, 2010bZavadskas & Turskis, 2010), according to the theory that states that the accurate understanding of the world complex phenomena is possible through simple relative comparisons (Buyukozkan & Gocer, 2018;Heidary Dahooie, Beheshti Jazan Abadi, Vanaki, & Firoozfar, 2018;Liao, Fu, & Wu, 2015;Tamosaitiene, Zavadskas, Sileikaite, & Turskis, 2017) The ARAS method adopts the optimality degree concept to find the ranking. It is equal to the sum of weighted normalised values of the criteria with respect to each of the alternatives divided by the sum of weighted normalised values of the best option (Chatterjee & Kar, 2018;Ecer, 2018;Heidary Dahooie, Zavadskas, Abolhasani, Vanaki, & Turskis, 2018;Rostamzadeh, Esmaeili, Nia, Saparauskas, & Keshavarz Ghorabaee, 2017;Sivilevicius, Daniunas, Zavadskas, Turskis, & Susinskas, 2012;Turskis, Kersuliene, & Vinogradova, 2017;Turskis, Morkunaite, & Kutut, 2017) Step 1: First, an m Â n decision matrix is formed, where m represents the alternatives and n represents the criteria.…”

Section: Additive Ratio Assessment (Aras) Methodsmentioning

confidence: 99%

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

Dahooie

Zavadskas

Vanaki

et al. 2019

Economic Research-Ekonomska Istraživanja

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The financial performance is an indicator of financial stability, health and condition of any organisation. It could be utilised as a proper measure of the firm's credibility and its ability to pay off debts. Financial institutions use this measure to determine the lending policy and applicants' credits. This study proposes a model based on the CCSD weighing method and hybrid FCM-ARAS approach for clustering and evaluating the financial performance to enable banks to identify target groups and design appropriate and relevant policies. Based on previous studies and the views of senior financial managers of a public bank in Iran, eight economic criteria were evaluated. The presented method was used to assess the financial performance of 58 manufacturing companies applying for loans from a federal bank in Iran. However, the CCSD method was used to calculate criteria weights, and a hybrid FCM-ARAS approach was developed and applied to financial evaluation and clustering the companies. The use of the CCSD method can eliminate errors caused by subjective models and human judgments, and increase the accuracy of the assessment. In this study, the debt ratio and equity to total assets and ROA were identified as the main criteria to assess financial performance.

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Section: Additive Ratio Assessment (Aras) Methodsmentioning

confidence: 99%

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

Dahooie

Zavadskas

Vanaki

et al. 2019

Economic Research-Ekonomska Istraživanja

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“…Monsanto, an agricultural company, is investing in sensor technology to digitize its agricultural operations. Amazon and Alibaba, global retailers, are investing in drones and robotics for product transportation and delivery [46].…”

Section: Digital Supply Chainmentioning

confidence: 99%

Digital Supplier Selection for a Garment Business Using Interval Type-2 Fuzzy Topsis

Özbek

Yıldız

2020

Tekstil Ve Konfeksiyon

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The aim of this study was to use the interval type-2 fuzzy TOPSIS method to select the best supplier among a number of suppliers digitized by Industry 4.0 for a company operating in the garment industry. The interval type-2 fuzzy TOPSIS method involves an interval of type-2 fuzzy sets and can model uncertainties very well to solve fuzzy multi-criteria decision making problems. Alternatives were listed based on closeness indexes, and the best digital supplier was selected based on sensitivity analysis. This is the first study to use the model in question to select the best digital supplier for a company. We, therefore, believe that it will contribute to the literature. It is recommended that the model in question be used in other industries as well.

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“…In addition, the evaluation criteria 1 2 3 4 5 6 7 ( , , , , , , ) c c c c c c c are presented in Table 2. Among these criteria, digital engagement (c 1 ), scale of digital collaboration (c 2 ), and degree of information sharing (c 2 ), are the criteria for expressing the digital level of suppliers, which are displayed based on the criteria proposed by Büyüközkan and Göcer (2018) for the selection of suitable digital suppliers. Service quality (c 4 ) refers to the degree to which the service can meet the needs of recipients.…”

Section: Case Descriptionmentioning

confidence: 99%

“…The DSCF reduces the credit risks of SMEs through extensive information, multi-dimensional data and multi-credit support technology, and thus effectively solves the financing difficulties of SMEs. The digital supply chain (DSC) can be regarded as an advanced intelligent technology system with massive data processing capability and good collaboration and communication for digital information of whole supply chain (Büyüközkan & Göcer, 2018). Therefore, the DSCF can be seen as the combination of DSC and SCF.…”

Section: Introductionmentioning

confidence: 99%

Integrating BWM and Aras Under Hesitant Linguistic Environment for Digital Supply Chain Finance Supplier Section

Liao

Wen

Liu

2019

Technological and Economic Development of Economy

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In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method.

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An extension of ARAS methodology under Interval Valued Intuitionistic Fuzzy environment for Digital Supply Chain

Cited by 115 publications

References 63 publications

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

A new evaluation model for corporate financial performance using integrated CCSD and FCM-ARAS approach

Digital Supplier Selection for a Garment Business Using Interval Type-2 Fuzzy Topsis

Integrating BWM and Aras Under Hesitant Linguistic Environment for Digital Supply Chain Finance Supplier Section

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