Application of integrated TOPSIS in ASC index: partners benchmarking perspective

Abstract

Purpose – In the rapidly changing business environment, companies must align with suppliers to streamline operations, as well as working together to achieve a level of agility beyond individual companies (Lin et al., 2006). Today’s more dynamic business environment increases the need for greater agility in supply chains, which increases both the importance and frequency of supplier/partner evaluation and benchmarking decision making. The purpose of this paper is to develop a multiple criterion appraisement index (model/module) for supplier/partner alternative firm benchmarking perspective under similar agile supply chain architecture. Design/methodology/approach – In this reporting, evaluation information against subjectivity (uncertain environment) indices has been transformed mathematical dimensionless numbers by fuzzy-based computation module. A new interval-valued fuzzy number set conjunction with modified “technique for order preference by similarity to ideal solution” methodology has been explored from benchmarking (ranking order of firm under similar criterion) point of view of supplier firms. Findings – In this context, a novel “fuzzy mathematical equation” has been developed in perceptive to compute the priority weights and appropriateness ratings of first-level measures which reduced the acquisition of supplementary priority weights and appropriateness ratings assessment in linguistic terms from group decision makers (DMs) for first-level indices. An empirical case study has been carried to ranking order the candidate partner/supplier alternative via collective index (CI) value. Lower value of “CI” reflected higher degree of performance extent. The authors found out the effectiveness and validity of proposed methodology for constructed appraisement module. Originality/value – This research work shall be valuable for that organization which volunteer to obtain the ranking order of partner/supplier alternative (benchmark) under similar agile supply chain architecture in accordance to group DMs’ comprehensive information for select best one supplier for own firm. In this reporting, a novel fuzzy mathematical equation has been developed in order to compute the important weights as well as priority rating of first-level indices/measure which reduced the supplementary important weights and priority rating assessment from group DMs in linguistic terms in order to obtain the measures rating and weights.

Green supply chain management assessment under chains of uncertain indices

“…Fuzzy theory is used by the numerous researchers for exploiting the MCDM techniques under incomplete information and to assess the performance level of numerous supply chains (Liao and Kao, 2011;Mangla et al, 2017;Rostamzadeh et al, 2015;Haleh and Hamidi, 2011;Wu et al, 2013). Fuzzy MCDM theory can reinforce the decision-making process and is required to be implemented for seeking solutions under complex multiattribute problems dealing with fuzzy environment (Shen et al, 2013;Sahu et al, 2016a; Green supply chain management assessment Tadi c et al, 2014;Yazdani et al, 2017). A fuzzy based approach can be used as solution method to capture inherent uncertainties (Yilmaz and Selim, 2016;Sahu et al, 2016b).…”

Composite Indicators of Company Performance: A Literature Survey

“…However. in this survey, there are 33 articles involving subjective judgments for weighting variables, but only 9 articles involving the adoption of fuzzy logic (Chang & Yeh, 2004; Kale, 2009; Singh, Garg, & Sharma, 2010; Gopal & Thakkar, 2015; Ayağ & Samanlioglu, 2016; Li & Zhao, 2016; Sahu, Sahu, & Sahu, 2016; Metaxas, Koulouriotis, & Spartalis, 2016; Rubio‐Romero, Pardo‐Ferreira, De la Varga‐Salto, & Galindo‐Reyes, 2018).…”

“…de Jong and Beelaerts van Blokland (2015); Harik, El Hachem, Medini, and Bernard (2015); Sahu, Sahu, and Sahu (2016); Metaxas, Koulouriotis, and Spartalis (2016); Nerantzidis (2016); Paredes‐Gazquez, Rodriguez‐Fernandez, and de la Cuesta‐Gonzalez (2016); Garg (2017); Seo and Bae (2017); Mendola and Volo (2017); Tinggi, Hla, Jakpar, Cheuk, and Nichol (2017); Zhang, Chen, and Liu (2017); Beelaerts van Blokland, van de Koppel, Lodewijks, and Breen (2019); Azevedo, Godina, and Matias (2017); Rubio‐Romero, Pardo‐Ferreira, De la Varga‐Salto, and Galindo‐Reyes (2018).…”

An Overview of Fuzzy Techniques in Supply Chain Management: Bibliometrics, Methodologies, Applications and Future Directions

“…Fuzzy TOPSIS: Grillo et al (2018) adopted the fuzzy TOPSIS to analyze the suitability of the minimum degree of possibility and then applied that to improve the performance of one production sector. Sahu et al (2016) used the fuzzy TOPSIS to transform the evaluation information against subjectivity indices into mathematical dimensionless numbers in order to develop a multiple criterion appraisement index model for supplier selection. Ozbek and Yildiz (2020) built a model using the fuzzy TOPSIS method to select the best digital supplier for a company.…”