A Hesitant Fuzzy Linguistic TOPSIS Model to Support Supplier Segmentation

Borges, William Viana; Lima Junior, Francisco Rodrigues; Peinado, Jurandir; Carpinetti, Luiz Cesar Ribeiro

doi:10.1590/1982-7849rac2022210133.en

William Viana Borges ^** Corresponding Author

Universidade Tecnológica Federal do Paraná, Programa de Pós-Graduação em Administração, Curitiba, PR, Brazil.

http://orcid.org/0000-0001-5166-2846

Francisco Rodrigues Lima Junior

Universidade Tecnológica Federal do Paraná, Programa de Pós-Graduação em Administração, Curitiba, PR, Brazil.

http://orcid.org/0000-0001-7053-5519

Jurandir Peinado

Universidade Tecnológica Federal do Paraná, Programa de Pós-Graduação em Administração, Curitiba, PR, Brazil.

http://orcid.org/0000-0003-4777-6984

Luiz Cesar Ribeiro Carpinetti

Universidade de São Paulo, Escola de Engenharia de São Carlos, Departamento de Engenharia de Produção, São Carlos, SP, Brazil.

http://orcid.org/0000-0002-8357-2607

* Corresponding Author

Editor-in-chief:

Marcelo de Souza Bispo (Universidade Federal da Paraíba, PPGA, Brazil)

Associate Editor:

Fernando Luiz Emerenciano Viana (Universidade de Fortaleza, Brazil)

Reviewers:

Nadya Regina Galo (Universidade Federal de Goiás, Brazil)

Luciano Ferreira da Silva (Universidade Nove de Julho, Brazil)

Authorship

William Viana Borges*

Universidade Tecnológica Federal do Paraná, Programa de Pós-Graduação em Administração

Av. Sete de Setembro, n. 3165, Rebouças, 80230-010, Curitiba, PR, Brazil

E-mail: williamvianaborges@gmail.com

https://orcid.org/0000-0001-5166-2846

Francisco Rodrigues Lima Junior

Universidade Tecnológica Federal do Paraná, Programa de Pós-Graduação em Administração

Av. Sete de Setembro, n. 3165, Rebouças, 80230-010, Curitiba, PR, Brazil

E-mail: frjunior@utfpr.edu.br

https://orcid.org/0000-0001-7053-5519

Jurandir Peinado

Universidade Tecnológica Federal do Paraná, Programa de Pós-Graduação em Administração

Av. Sete de Setembro, n. 3165, Rebouças, 80230-010, Curitiba, PR, Brazil

E-mail: jurandirpeinado@utfpr.edu.br

https://orcid.org/0000-0003-4777-6984

Luiz Cesar Ribeiro Carpinetti

Universidade de São Paulo, Escola de Engenharia de São Carlos, Departamento de Engenharia de Produção

Av. Trabalhador São-Carlense, n. 400, Parque Arnold Schimidt, 13566-590, São Carlos, SP, Brazil

E-mail: carpinet@sc.usp.br

https://orcid.org/0000-0002-8357-2607

Authors' Contributions

1^st author: conceptualization (lead); formal analysis (lead); investigation (lead); methodology (lead); validation (lead); visualization (lead); writing - original draft (lead).

2^nd author: conceptualization (equal); methodology (equal); project administration (lead); supervision (equal); validation (lead); writing - original draft (supporting).

3^rd author: conceptualization (supporting); supervision (lead); validation (equal); writing - review & editing (equal).

4^th author: conceptualization (equal); supervision (equal); validation (supporting); writing - review & editing (lead).

Conflict of Interests

The authors have stated that there is no conflict of interest.

		Author(s)
Method(s)	AHP	Bianchini, Benci, Pellgrini and Rossi (2019Bianchini, A., Benci, A., Pellgrini, M.P., & Rossi, J. (2019). Supply chain redesign for lead-time reduction through Kraljic purchasing portfolio and AHP integration. Benchmarking: An International Journal, 26(1), 1194-1209. https://doi.org/10.1108/BIJ-07-2018-0222 https://doi.org/10.1108/BIJ-07-2018-0222... ), Park, Shin, Chang and Park (2010Park, J., Shin, K., Chang, T., & Park, J. (2010). An integrative framework for supplier relationship management. Industrial Management & Data Systems, 110(4), 495-515. https://doi.org/10.1108/02635571011038990 https://doi.org/10.1108/0263557101103899... ), Torres-Ruiz and Ravindran (2018Torres-Ruiz, A., & Ravindran, R. (2018). Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. Journal of Cleaner Production, 172(1), 4478-4493. https://doi.org/10.1016/j.jclepro.2017.10.304 https://doi.org/10.1016/j.jclepro.2017.1... )
	AHP and fuzzy 2-tuple	Santos, Osiro and Lima (2017Santos, L. F. O. M., Osiro, L., & Lima, R. H. P. (2017). A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria. Expert Systems with Applications, 79(1), 53-64. https://doi.org/10.1016/j.eswa.2017.02.032 https://doi.org/10.1016/j.eswa.2017.02.0... )
	AHP and fuzzy relations	Rezaei and Ortt (2013bRezaei, J., & Ortt, R. (2013b). Supplier segmentation using fuzzy logic. Industrial Marketing Management, 42(4), 507-517. https://doi.org/10.1016/j.indmarman.2013.03.003 https://doi.org/10.1016/j.indmarman.2013... )
	AHP, fuzzy c-means and VIKOR	Akman (2015Akman, G. (2015). Evaluating suppliers to include green supplier development programs via fuzzy c-means and VIKOR methods. Computers & Industrial Engineering, 86(1), 69-82. https://doi.org/10.1016/j.cie.2014.10.013 https://doi.org/10.1016/j.cie.2014.10.01... )
	AHP, K-means and simulated annealing algorithm	Che (2011Che, Z. H. (2011). Clustering and selecting suppliers based on simulated annealing algorithms. Computers & Mathematics with Applications, 63(1), 228-238. https://doi.org/10.1016/j.camwa.2011.11.014 https://doi.org/10.1016/j.camwa.2011.11.... )
	AHP, PROMETHÉE and MAUT	Segura and Maroto (2017Segura, M., & Maroto, C. (2017). A multiple criteria supplier segmentation using outranking and value function methods. Expert Systems with Applications, 69(1), 87-100. https://doi.org/10.1016/j.eswa.2016.10.031 https://doi.org/10.1016/j.eswa.2016.10.0... )
	Best-worst method	Rezaei and Lajimi (2019Rezaei, J., & Lajimi, H. F. (2019). Segmenting supplies and suppliers: Bringing together the purchasing portfolio matrix and the supplier potential matrix. International Journal of Logistics Research and Applications, 22(4), 419-436. https://doi.org/10.1080/13675567.2018.1535649 https://doi.org/10.1080/13675567.2018.15... ), Rezaei, Wang, and Tavasszy (2015)
	DEA	Restrepo and Villegas (2019Restrepo, R., & Villegas, J. G. (2019). Supplier evaluation and classification in a Colombian motorcycle assembly company using data envelopment analysis. Academia Revista Latinoamericana de Administración, 32(2), 159-180. https://doi.org/10.1108/ARLA-04-2017-0107 https://doi.org/10.1108/ARLA-04-2017-010... )
	DEMATEL	Parkouhi, Ghadikolaei, and Lajimi (2019Parkouhi, S. V., Ghadikolaei, A. S., & Lajimi, H. F. (2019). Resilient supplier selection and segmentation in grey environment. Journal of Cleaner Production, 207(1), 1123-1137. https://doi.org/10.1016/j.jclepro.2018.10.007 https://doi.org/10.1016/j.jclepro.2018.1... )
	ELECTRE TRI	Rezaei et al. (2017Rezaei, J., Kadzinski, M., Vana, C., & Tavasszy, L. A. (2017) Embedding carbon impact assessment in multi-criteria supplier segmentation using ELECTRE TRI-rC. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2454-y https://doi.org/10.1007/s10479-017-2454-... )
	Fuzzy-AHP	Lo and Sudjatmika (2015Lo, S., & Sudjatmika, F. V. (2015). Solving multi-criteria supplier segmentation based on the modified FAHP for supply chain management: A case study. Soft Computing, 20(12), 4981-4990. https://doi.org/10.1007/s00500-015-1787-1 https://doi.org/10.1007/s00500-015-1787-... )
	Fuzzy-AHP and fuzzy c-means	Haghighi, Morad and Salahi (2014Haghighi, P. S., Morad, M., & Salahi, M. (2014). Supplier segmentation using fuzzy linguistic preference relations and fuzzy clustering. I. J. Intelligent Systems and Applications, 5(1), 76-82. https://doi.org/10.5815/IJISA.2014.05.08 https://doi.org/10.5815/IJISA.2014.05.08... )
	Fuzzy c-means and fuzzy formal concept analysis	Jharkharia and Das (2019Jharkharia, S., & Das, C. (2019). Low carbon supplier development: A fuzzy c-means and fuzzy formal concept analysis based analytical model. Benchmarking: An International Journal, 26(1), 73-96. https://doi.org/10.1108/BIJ-03-2018-0074 https://doi.org/10.1108/BIJ-03-2018-0074... )
	Fuzzy-TOPSIS	Lima and Carpinetti (2016Lima, F. R. Junior, & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174(1), 128-141. https://doi.org/10.1016/j.ijpe.2016.01.023 https://doi.org/10.1016/j.ijpe.2016.01.0... ), Medeiros and Ferreira (2018Medeiros, M., & Ferreira, L. (2018). Development of a purchasing portfolio model: An empirical study in a Brazilian hospital. Journal Production Planning & Control, 29(7), 571-585. https://doi.org/10.1080/09537287.2018.1434912 https://doi.org/10.1080/09537287.2018.14... )
	Fuzzy inference	Aloini, Dulmin, Mininno and Zerbino (2019Aloini, D., Dulmin, R., Mininno, V., & Zerbino, P. (2019). Leveraging procurement-related knowledge through a fuzzy-based DSS. Journal of Knowledge Management, 23(1), 1077-1104. https://doi.org/10.1108/JKM-10-2018-0614 https://doi.org/10.1108/JKM-10-2018-0614... ), Osiro, Lima and Carpinetti (2014Osiro, L., Lima, F. R. Junior, & Carpinetti, L. C. R. (2014). A fuzzy logic approach to supplier evaluation for development. International Journal of Production Economics, 153(1), 95-112. http://dx.doi.org/10.1016/j.ijpe.2014.02.009 http://dx.doi.org/10.1016/j.ijpe.2014.02... ), Rezaei and Ortt (2013aRezaei, J., & Ortt, R. (2013a). Multi-criteria supplier segmentation using a fuzzy preference relations based AHP. European Journal of Operational Research, 225(1), 75-84. https://doi.org/10.1016/j.ejor.2012.09.037 https://doi.org/10.1016/j.ejor.2012.09.0... )
	PROMETHÉE	Boujelben (2017Boujelben, M. A. (2017). A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering. Omega, 69(1), 126-140. https://doi.org/10.1016/j.omega.2016.08.007 https://doi.org/10.1016/j.omega.2016.08.... )
	RST, VIKOR and fuzzy c-means	Bai, Rezaei and Sarkis (2017Rezaei, J., Kadzinski, M., Vana, C., & Tavasszy, L. A. (2017) Embedding carbon impact assessment in multi-criteria supplier segmentation using ELECTRE TRI-rC. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2454-y https://doi.org/10.1007/s10479-017-2454-... )
	VIKORSORT	Demir, Akpinar, Araz and Ilgin (2018Demir, L., Akpinar, M. E., Araz, C., & Ilgin, M. A. (2018). A green supplier evaluation system based on a new multi-criteria sorting method: VIKORSORT. Expert Systems with Applications, 114(1), 479-487. https://doi.org/10.1016/j.eswa.2018.07.071 https://doi.org/10.1016/j.eswa.2018.07.0... )
Segmentation dimensions	Supplier attractiveness and strength of relationship	Aloini et al. (2019Aloini, D., Dulmin, R., Mininno, V., & Zerbino, P. (2019). Leveraging procurement-related knowledge through a fuzzy-based DSS. Journal of Knowledge Management, 23(1), 1077-1104. https://doi.org/10.1108/JKM-10-2018-0614 https://doi.org/10.1108/JKM-10-2018-0614... )
	Supplier capabilities and supplier willingness to collaborate	Bai et al. (2017Bai, C., Rezaei, J., & Sarkis, J. (2017). Multicriteria green supplier segmentation. IEEE Transactions on Automation Science and Engineering, 64(4), 515-528. https://doi.org/10.1109/TEM.2017.2723639 https://doi.org/10.1109/TEM.2017.2723639... ), Boujelben (2017Boujelben, M. A. (2017). A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering. Omega, 69(1), 126-140. https://doi.org/10.1016/j.omega.2016.08.007 https://doi.org/10.1016/j.omega.2016.08.... ), Haghighi et al. (2014Haghighi, P. S., Morad, M., & Salahi, M. (2014). Supplier segmentation using fuzzy linguistic preference relations and fuzzy clustering. I. J. Intelligent Systems and Applications, 5(1), 76-82. https://doi.org/10.5815/IJISA.2014.05.08 https://doi.org/10.5815/IJISA.2014.05.08... ), Lo and Sudjatmika (2015Lo, S., & Sudjatmika, F. V. (2015). Solving multi-criteria supplier segmentation based on the modified FAHP for supply chain management: A case study. Soft Computing, 20(12), 4981-4990. https://doi.org/10.1007/s00500-015-1787-1 https://doi.org/10.1007/s00500-015-1787-... ), Rezaei and Ortt (2013aRezaei, J., & Ortt, R. (2013a). Multi-criteria supplier segmentation using a fuzzy preference relations based AHP. European Journal of Operational Research, 225(1), 75-84. https://doi.org/10.1016/j.ejor.2012.09.037 https://doi.org/10.1016/j.ejor.2012.09.0... ), Rezaei and Ortt (2013b), Rezaei, Kadzinski, Vana and Tavasszy (2017), Rezaei et al. (2015), Santos et al. (2017Santos, L. F. O. M., Osiro, L., & Lima, R. H. P. (2017). A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria. Expert Systems with Applications, 79(1), 53-64. https://doi.org/10.1016/j.eswa.2017.02.032 https://doi.org/10.1016/j.eswa.2017.02.0... )
	Cost and delivery performance	Lima and Carpinetti (2016Lima, F. R. Junior, & Carpinetti, L. C. R. (2016). Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management. International Journal of Production Economics, 174(1), 128-141. https://doi.org/10.1016/j.ijpe.2016.01.023 https://doi.org/10.1016/j.ijpe.2016.01.0... )
	Supplier investment decisions and supplier collaboration decisions	Jharkharia and Das (2019Jharkharia, S., & Das, C. (2019). Low carbon supplier development: A fuzzy c-means and fuzzy formal concept analysis based analytical model. Benchmarking: An International Journal, 26(1), 73-96. https://doi.org/10.1108/BIJ-03-2018-0074 https://doi.org/10.1108/BIJ-03-2018-0074... )
	Critical and strategic	Segura and Maroto (2017Segura, M., & Maroto, C. (2017). A multiple criteria supplier segmentation using outranking and value function methods. Expert Systems with Applications, 69(1), 87-100. https://doi.org/10.1016/j.eswa.2016.10.031 https://doi.org/10.1016/j.eswa.2016.10.0... )
	Diversity efficiency and cross efficiency	Restrepo and Villegas (2019Restrepo, R., & Villegas, J. G. (2019). Supplier evaluation and classification in a Colombian motorcycle assembly company using data envelopment analysis. Academia Revista Latinoamericana de Administración, 32(2), 159-180. https://doi.org/10.1108/ARLA-04-2017-0107 https://doi.org/10.1108/ARLA-04-2017-010... )
	Strategic importance and relationship attractiveness	Park, Shin, Chang and Park (2010Park, J., Shin, K., Chang, T., & Park, J. (2010). An integrative framework for supplier relationship management. Industrial Management & Data Systems, 110(4), 495-515. https://doi.org/10.1108/02635571011038990 https://doi.org/10.1108/0263557101103899... )
	Resiliency enhancer and resiliency reducer	Parkouhi et al. (2019Parkouhi, S. V., Ghadikolaei, A. S., & Lajimi, H. F. (2019). Resilient supplier selection and segmentation in grey environment. Journal of Cleaner Production, 207(1), 1123-1137. https://doi.org/10.1016/j.jclepro.2018.10.007 https://doi.org/10.1016/j.jclepro.2018.1... )
	Country, supplier business performance, and supplier E&S	Torres-Ruiz and Ravindran (2018Torres-Ruiz, A., & Ravindran, R. (2018). Multiple criteria framework for the sustainability risk assessment of a supplier portfolio. Journal of Cleaner Production, 172(1), 4478-4493. https://doi.org/10.1016/j.jclepro.2017.10.304 https://doi.org/10.1016/j.jclepro.2017.1... )
	Potential for partnership and delivery performance	Osiro et al. (2014Osiro, L., Lima, F. R. Junior, & Carpinetti, L. C. R. (2014). A fuzzy logic approach to supplier evaluation for development. International Journal of Production Economics, 153(1), 95-112. http://dx.doi.org/10.1016/j.ijpe.2014.02.009 http://dx.doi.org/10.1016/j.ijpe.2014.02... )
	Supply risk and impact on profit	Bianchini et al. (2019Bianchini, A., Benci, A., Pellgrini, M.P., & Rossi, J. (2019). Supply chain redesign for lead-time reduction through Kraljic purchasing portfolio and AHP integration. Benchmarking: An International Journal, 26(1), 1194-1209. https://doi.org/10.1108/BIJ-07-2018-0222 https://doi.org/10.1108/BIJ-07-2018-0222... ), Medeiros and Ferreira (2018Medeiros, M., & Ferreira, L. (2018). Development of a purchasing portfolio model: An empirical study in a Brazilian hospital. Journal Production Planning & Control, 29(7), 571-585. https://doi.org/10.1080/09537287.2018.1434912 https://doi.org/10.1080/09537287.2018.14... )
	Supply risk, profit impact, capabilities, and willingness to collaborate	Rezaei and Lajimi (2019Rezaei, J., & Lajimi, H. F. (2019). Segmenting supplies and suppliers: Bringing together the purchasing portfolio matrix and the supplier potential matrix. International Journal of Logistics Research and Applications, 22(4), 419-436. https://doi.org/10.1080/13675567.2018.1535649 https://doi.org/10.1080/13675567.2018.15... )

Chosen criteria	Decision-maker 1		Decision-maker 2
Chosen criteria	Judgments	Conversion	Judgments	Conversion
C₁: Effort to reduce material waste	Between very low and low	[VL,L]	Between medium and high	[M,H]
C₂: Ability to work in a team	Between low and medium	[L,M]	Between medium and high	[M,H]
C₃: Commitment to quality	Between medium and high	[M,H]	Between medium and high	[M,H]
C₄: Willingness to share information, ideas, technology, and cost savings	Between very low and low	[VL,L]	Between medium and high	[M,H]
C₅: Long-term relationship	Between low and medium	[L,M]	Between medium and high	[M,H]
C₆: Honesty	Between medium and high	[M,H]	Between high and very high	[H,VH]
C₇: Safety auditorship	Between medium and high	[M,H]	Perfect	[P]
C₈: Ease of communication	Between very low and low	[VL,L]	Between medium and high	[M,H]
C₉: Compliance procedures	Between medium and high	[M,H]	Perfect	[P]

Criteria	S_p	S_q	$d (H_{S}^{1}, H_{S}^{2})$		D⁺	$d (H_{S}^{1}, H_{S}^{2})$		D^-	CC_i	CC_i normalized
C₁	2	3	3	4	7	1	1	2	0.222	0.057
C₂	3	3	3	3	6	1	2	3	0.333	0.086
C₃	3	4	2	3	5	2	2	4	0.444	0.114
C₄	2	3	3	4	7	1	1	2	0.222	0.057
C₅	3	3	3	3	6	1	2	3	0.333	0.086
C₆	4	4	2	2	4	2	3	5	0.556	0.143
C₇	4	6	0	2	2	4	3	7	0.778	0.200
C₈	2	3	3	4	7	1	1	2	0.222	0.057
C₉	4	6	0	2	2	4	3	7	0.778	0.200

Chosen criteria	Decision-maker 1		Decision-maker 2
Chosen criteria	Judgments	Conversion	Judgments	Conversion
C₁: Environmental certifications	Between high and very high	[M,H]	Between medium and high	[M,H]
C₂: Proper waste disposal	Between medium and high	[P]	Between medium and high	[M,H]
C₃: Disposal of hazardous materials	Between medium and high	[M,H]	Between medium and high	[M,H]
C₄: On-time shipment	Between medium and high	[M,H]	Between high and very high	[H,VH]
C₅: Technological capability	Between high and very high	[VL,L]	Between high and very high	[H,VH]
C₆: Quick problem solving	Between medium and high	[L,M]	Between high and very high	[H,VH]
C₇: Technical knowledge	Perfect	[M,H]	Between high and very high	[H,VH]
C₈: Productivity and efficiency	Between medium and high	[M,H]	Between high and very high	[H,VH]
C₉: Quality	Between medium and high	[H,VH]	Between high and very high	[H,VH]
C₁₀: Average training time per employee	Between very low and low	[P]	Between low and medium	[L,M]
C₁₁: Employees’ healthcare	Between low and medium	[M,H]	Between medium and high	[M,H]
C₁₂: Child labor	Between medium and high	[H,VH]	Between very low and low	[VL,L]
C₁₃: Work conditions	Between medium and high	[M,H]	Between medium and high	[M,H]
C₁₄: Safety training	Between high and very high	[M,H]	Perfect	[P]
C₁₅: Number of accidents	Perfect	[AB]	Between high and very high	[H,VH]
C₁₆: Employee satisfaction	Between medium and high	[M,A]	Between medium and high	[M,H]
C₁₇: Company reputation	Between high and very high	[A,MA]	Between medium and high	[M,H]
C₁₈: Technical structure	Between medium and high	[M,A]	Between medium and high	[M,H]
C₁₉: Financial situation	Between medium and high	[M,A]	Between low and medium	[L,M]

	C₁	C₂	C₃	C₄	C₅	C₆	C₇	C₈	C₉	C₁₀
CC_i	0.556	0.444	0.444	0.556	0.667	0.556	0.889	0.556	0.556	0.111
CC_i normalized	0.056	0.045	0.045	0.056	0.067	0.056	0.09	0.056	0.056	0.011
	C₁₁	C₁₂	C₁₃	C₁₄	C₁₅	C₁₆	C₁₇	C₁₈	C₁₉
CC_i	0.333	0.222	0.444	0.889	0.889	0.444	0.556	0.444	0.333
CC_i normalized	0.034	0.022	0.045	0.09	0.09	0.045	0.056	0.045	0.034

C_i	Decision-maker 1						Decision-maker 2
C_i	F₁	F₂	F₃	F₄	F₅	F₆	F₁	F₂	F₃	F₄	F₅	F₆
C₁	[M,H]	[H]	[M,H]	[M,H]	[M,H]	[L,M]	[L]	[H,VH]	[L]	[L,M,H]	[M,H]	[M,H]
C₂	[M,H]	[H]	[M,H]	[M,H]	[M,H]	[M,H]	[L,M]	[H,VH]	[M]	[M,H]	[H,VH]	[M,H]
C₃	[M,H]	[H,VH]	[M,H]	[M,H]	[M,H]	[L,M]	[L]	[H,VH]	[L,M]	[M,H]	[M,H]	[M,H]
C₄	[L]	[L,M]	[VL,L]	[L,M]	[L,M]	[VL,L]	[M,H]	[M,H]	[L]	[M,H]	[M,H]	[H,VH]
C₅	[L,M]	[H,VH]	[M,H]	[M,H]	[H,VH]	[M,H]	[L,M]	[P]	[L,M]	[H,VH]	[H,VH]	[P]
C₆	[L,M]	[H,VH]	[L,M]	[M,H]	[M,H]	[M,H]	[H]	[P]	[H,VH]	[H,VH]	[P]	[P]
C₇	[M]	[H,VH]	[M,H]	[M,H]	[M,H]	[M,H]	[P]	[P]	[H,VH]	[M,H]	[M,H]	[P]
C₈	[L,M]	[M,H]	[M,H]	[M,H]	[M,H]	[L,M]	[L,M]	[P]	[L,M]	[H,VH]	[H,VH]	[L,M]
C₉	[M,H]	[M,H]	[M,H]	[M,H]	[M,H]	[M,H]	[P]	[P]	[P]	[P]	[P]	[P]

C_i	F₁	F₂	F₃	F₄	F₅	F₆
C₁	[2, 3]	[4, 4]	[2, 3]	[3, 4]	[3, 4]	[3, 3]
C₂	[3, 3]	[4, 4]	[3, 3]	[3, 4]	[4, 4]	[3, 4]
C₃	[2, 3]	[4, 5]	[3, 3]	[3, 4]	[3, 4]	[3, 3]
C₄	[2, 3]	[3, 3]	[2, 2]	[3, 3]	[3, 3]	[2, 4]
C₅	[2, 3]	[5, 6]	[3, 3]	[4, 4]	[4, 5]	[4, 6]
C₆	[3, 4]	[5, 6]	[3, 4]	[4, 4]	[4, 6]	[4, 6]
C₇	[3, 6]	[5, 6]	[4, 4]	[3, 4]	[3, 4]	[4, 6]
C₈	[2, 3]	[4, 6]	[3, 3]	[4, 4]	[4, 4]	[2, 3]
C₉	[4, 6]	[5, 6]	[4, 6]	[4, 6]	[4, 6]	[4, 6]

C_i	Distances from the PIS $\| \| h_{i j} - h_{j}^{+} \| \|$						C_i	Distances from the NIS $\| \| h_{i j} - h_{j}^{-} \| \|$
C_i	F₁	F₂	F₃	F₄	F₅	F₆		F₁	F₂	F₃	F₄	F₅	F₆
C₁	0.114	0.029	0.114	0.057	0.057	0.086	C₁	0.029	0.114	0.029	0.086	0.086	0.057
C₂	0.129	0.043	0.129	0.086	0.043	0.086	C₂	0.043	0.129	0.043	0.086	0.129	0.086
C₃	0.229	0.000	0.171	0.114	0.114	0.171	C₃	0.057	0.286	0.114	0.171	0.171	0.114
C₄	0.114	0.086	0.143	0.086	0.086	0.086	C₄	0.057	0.086	0.029	0.086	0.086	0.086
C₅	0.300	0.043	0.257	0.171	0.129	0.086	C₅	0.000	0.257	0.043	0.129	0.171	0.214
C₆	0.357	0.071	0.357	0.286	0.143	0.143	C₆	0.143	0.429	0.143	0.214	0.357	0.357
C₇	0.300	0.100	0.400	0.500	0.500	0.200	C₇	0.300	0.500	0.200	0.100	0.100	0.400
C₈	0.200	0.057	0.171	0.114	0.114	0.200	C₈	0.000	0.143	0.029	0.086	0.086	0.000
C₉	0.200	0.100	0.200	0.200	0.200	0.200	C₉	0.300	0.400	0.300	0.300	0.300	0.300
D⁺	1.943	0.529	1.943	1.614	1.386	1.257	D^-	0.929	2.343	0.929	1.257	1.486	1.614

Supplier	F₁	F₂	F₃	F₄	F₅	F₆
RC_i	0.323	0.816	0.323	0.438	0.517	0.562
RC_i normalized	0.281	0.849	0.281	0.421	0.528	0.588
Ranking	6^th	1^st	5^th	4^th	3^rd	2^nd

C_i	Decision-maker 1						Decision-maker 2
C_i	F₁	F₂	F₃	F₄	F₅	F₆	F₁	F₂	F₃	F₄	F₅	F₆
C₁	[VL]	[VL]	[VL]	[VL]	[VL]	[VL]	[M,H]	[M,H]	[M,H]	[M,H]	[M,H]	[P]
C₂	[L,M]	[L,M]	[L,M]	[L,M]	[L,M]	[VL]	[L,M]	[P]	[M,H]	[L,M]	[L,M]	[VL,L,M]
C₃	[M,H]	[M,H]	[L,M]	[M,H]	[M,H]	[VL,L]	[M,H]	[P]	[M,H]	[M,H]	[H,VH]	[M,H,VH]
C₄	[L,M]	[M,H]	[H,VH]	[H,VH]	[H,VH]	[M,H]	[L,M]	[H,VH]	[M,H]	[M,H]	[L,M]	[L,M]
C₅	[VL,L]	[H,VH]	[M,H]	[M,H]	[M,H]	[M,H]	[L,M,H]	[L,M,H]	[L,M]	[L,M]	[L,M,H]	[H,VH]
C₆	[M,H]	[M,H]	[M,H]	[H,VH]	[M,H]	[H,VH]	[L,M]	[L,M]	[VL,L]	[L,M]	[L,M]	[L,M]
C₇	[M,H]	[M,H]	[M,H]	[H,VH]	[M,H]	[H,VH]	[M,H]	[M,H]	[L,M]	[M,H]	[M,H]	[H,VH]
C₈	[L,M]	[P]	[M,H]	[M,H]	[M,H]	[M,H]	[L,M]	[H,VH]	[L,M]	[M,H]	[M,H]	[M,H]
C₉	[L,M]	[P]	[M,H]	[M,H]	[M,H]	[M,H]	[L,M]	[M,H]	[L,M]	[M,H]	[M,H]	[M,H]
C₁₀	[L,M]	[M,H]	[L,M]	[VL,L]	[VL,L]	[H,VH]	[M,H]	[M,H]	[VL,L]	[L,M]	[L,M]	[H,VH]
C₁₁	[M,H]	[H,VH]	[M,H]	[M,H]	[M,H]	[M,H]	[H,VH]	[H,VH]	[M,H]	[M,H]	[M,H]	[P]
C₁₂	[P]	[P]	[P]	[P]	[P]	[P]	[P]	[P]	[P]	[P]	[P]	[P]
C₁₃	[L,M]	[H,VH]	[L,M]	[M,H]	[M,H]	[P]	[L,M]	[M,H]	[L,M]	[L,M]	[L,M]	[M,H]
C₁₄	[L,M]	[H,VH]	[L,M]	[L,M]	[L,M]	[P]	[P]	[P]	[H,VH]	[M,H]	[M,H]	[P]
C₁₅	[VH]	[VH]	[VH]	[VH]	[VH]	[VH]	[P]	[P]	[P]	[P]	[P]	[P]
C₁₆	[VL]	[H,VH]	[M,H]	[M,H]	[M,H]	[H,VH]	[VL,L]	[M,H]	[L,M]	[L,M]	[M,H]	[H,VH]
C₁₇	[M,H]	[H,VH]	[M,H]	[M,H]	[M,H]	[H,VH]	[L,M]	[H,VH]	[L,M]	[M,H]	[M,H]	[H,VH]
C₁₈	[M,H]	[H,VH]	[M,H]	[L,M]	[L,M]	[P]	[M,H]	[L,M]	[VL,L]	[L,M]	[L,M]	[H,VH]
C₁₉	[M,H]	[M,H]	[M,H]	[M,H]	[M,H]	[P]	[L,M]	[L,M]	[L,M]	[L,M]	[L,M]	[H,VH]

Supplier	F₁	F₂	F₃	F₄	F₅	F₆
Normalized RC_i	0.285	0.776	0.311	0.406	0.395	0.783
Ranking	6^th	2^nd	5^th	3^rd	4^th	1^st

Criteria	Scenario 1		Scenario 2		Scenario 3
Criteria	Decision-maker 1	Decision-maker 2	Decision-maker 1	Decision-maker 2	Decision-maker 1	Decision-maker 2
C₁: Effort to reduce material waste	[P]	[P]	[M]	[M]	[M]	[M]
C₂: Ability to work in a team	[M]	[M]	[P]	[P]	[M]	[M]
C₃: Commitment to quality	[M]	[M]	[P]	[P]	[M]	[M]
C₄: Willingness to share information, ideas, technology, and cost savings	[M]	[M]	[P]	[P]	[M]	[M]
C₅: Long-term relationship	[M]	[M]	[P]	[P]	[M]	[M]
C₆: Honesty	[M]	[M]	[M]	[M]	[P]	[P]
C₇: Safety auditorship	[M]	[M]	[M]	[M]	[P]	[P]
C₈: Ease of communication	[M]	[M]	[M]	[M]	[P]	[P]
C₉: Compliance procedures	[M]	[M]	[M]	[M]	[P]	[P]

Criteria	Scenario 1		Scenario 2		Scenario 3
Criteria	Decision-maker 1	Decision-maker 2	Decision-maker 1	Decision-maker 2	Decision-maker 1	Decision-maker 2
C₁: Environmental certifications	[P]	[P]	[M]	[M]	[M]	[M]
C₂: Proper waste disposal	[P]	[P]	[M]	[M]	[M]	[M]
C₃: Disposal of hazardous materials	[P]	[P]	[M]	[M]	[M]	[M]
C₄: On-time shipment	[M]	[M]	[P]	[P]	[M]	[M]
C₅: Technological capability	[M]	[M]	[P]	[P]	[M]	[M]
C₆: Quick problem solving	[M]	[M]	[P]	[P]	[M]	[M]
C₇: Technical knowledge	[M]	[M]	[P]	[P]	[M]	[M]
C₈: Productivity and efficiency	[M]	[M]	[P]	[P]	[M]	[M]
C₉: Quality	[M]	[M]	[P]	[P]	[M]	[M]
C₁₀: Average training time per employee	[M]	[M]	[M]	[M]	[P]	[P]
C₁₁: Employees’ healthcare	[M]	[M]	[M]	[M]	[P]	[P]
C₁₂: Child labor	[M]	[M]	[M]	[M]	[P]	[P]
C₁₃: Work conditions	[M]	[M]	[M]	[M]	[P]	[P]
C₁₄: Safety training	[M]	[M]	[M]	[M]	[P]	[P]
C₁₅: Number of accidents	[M]	[M]	[M]	[M]	[P]	[P]
C₁₆: Employee satisfaction	[M]	[M]	[M]	[M]	[P]	[P]
C₁₇: Company reputation	[M]	[M]	[M]	[M]	[P]	[P]
C₁₈: Technical structure	[M]	[M]	[P]	[P]	[M]	[M]
C₁₉: Financial situation	[M]	[M]	[P]	[P]	[M]	[M]

F_i	Proposed model		Scenario 1		Scenario 2		Scenario 3
F_i	Willingness	Capabilities	Willingness	Capabilities	Willingness	Capabilities	Willingness	Capabilities
F₁	0.242	0.284	0.250	0.420	0.218	0.233	0.309	0.348
F₂	0.821	0.793	0.798	0.874	0.793	0.734	0.834	0.715
F₃	0.277	0.354	0.250	0.420	0.255	0.376	0.309	0.326
F₄	0.449	0.363	0.634	0.420	0.534	0.409	0.309	0.348
F₅	0.651	0.388	0.634	0.500	0.631	0.409	0.656	0.370
F₆	0.539	0.769	0.432	0.275	0.583	0.806	0.540	0.832

Supplier	Application case	Scenario 1	Scenario 2	Scenario 3
F₁	1	1	1	1
F₂	4	4	4	4
F₃	1	1	1	1
F₄	1	2	2	1
F₅	2	2	2	2
F₆	4	1	4	4

[1] * Corresponding Author

Brasil

Brasil

A Hesitant Fuzzy Linguistic TOPSIS Model to Support Supplier Segmentation

ABSTRACT

Objective:

Proposal:

Conclusion: