Research Article
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Year 2020, Volume: 30 Issue: 1, 61 - 72, 25.03.2020
https://doi.org/10.32710/tekstilvekonfeksiyon.569884

Abstract

References

  • Schwab, K. (2017). The fourth industrial revolution. Davos: World Economic Forum, 51-59
  • Yildiz, A. (2018). Digital supply chain integrated with industry 4.0. Business & Management Studies: An International Journal, 6(4), 1215-1230.
  • https://kemptechnologies.com/blog/the-4th-industrial-revolution/
  • Hobsbawm, E. (2010). Age of revolution: 1789-1848. Hachette UK.
  • Aksoy, S. (2017). Değişen teknolojiler ve endüstri 4.0: endüstri 4.0’ı anlamaya dair bir giriş. SAV Katkı, 4, 34-4.
  • Ovaci, C. (2017). Endüstri 4.0 çağında açık inovasyon. Maliye Finans Yazilari. (Özel Sayı), 113-132.
  • Bağcı, E. (2018). Endüstri 4.0: Yeni üretim tarzını anlamak. Gümüshane University Electronic Journal of the Institute of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 9(24).
  • Toker, K. (2018). Endüstri 4.0 ve Sürdürülebilirliğe Etkileri. Institute of Business Administration-Management Journal/Isletme Iktisadi Enstitüsü Yönetim Dergisi, 29(84), 51-64.
  • Fırat, S. Ü., & Fırat, O. Z. (2017). Sanayi 4.0 devrimi üzerine karşılaştırmalı bir inceleme: Kavramlar, küresel gelişmeler ve Türkiye. Toprak İşveren Dergisi, 114(2017), 10-23.
  • Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394.
  • Rojko, A. (2017). Industry 4.0 concept: background and overview. International Journal of Interactive Mobile Technologies (iJIM), 11(5), 77-90.
  • Tsai, W. H. (2018). Green production planning and control for the textile industry by using mathematical programming and industry 4.0 techniques. Energies, 11(8), 2072.
  • Tansan, B., Gökbulut, A., Targotay, Ç., & Eren, T. (2016). Türkiye’nin küresel rekabetçiliği için bir gereklilik olarak sanayi 4.0 gelişmekte olan ekonomi perspektifi. TÜSİAD Raporu.
  • https://www.manufacturersalliance.co.uk/2018/03/08/sme-manufacturers-adopting-industry-4-0-technologies/
  • Bulut, E., & Akçacı, T. (2017). Endüstri 4.0 ve inovasyon göstergeleri kapsamında Türkiye analizi. ASSAM Uluslararası Hakemli Dergi, 4(7), 55-77.
  • Tsai, W. H., & Lai, S. Y. (2018). Green production planning and control model with abc under industry 4.0 for the paper industry. Sustainability, 10(8), 2932.
  • Duarte, A. Y. S., Sanches, R. A., & Dedini, F. G. (2018). Assessment and technological forecasting in the textile industry: From first industrial revolution to the Industry 4.0. Strategic Design Research Journal, 11(3), 193-202.
  • Chen, Z., & Xing, M. (2015, October). Upgrading of textile manufacturing based on Industry 4.0. In 5th International Conference on Advanced Design and Manufacturing Engineering. Atlantis Press.
  • Alçın, S. (2016). Üretim için yeni bir izlek: sanayi 4.0. Journal of Life Economics, 3(2), 19-30.
  • Özbek, A., (2005), Örnek işletmeler bazında türk hazır giyim sanayinin yapısı, ihracatı ve geleceği, Marmara Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
  • Özbek A., (2009), Türk hazır giyim sanayinin örnek ürün bazında (denim pantolon) gelecekteki ihracat performansının incelenmesi, Marmara Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi, İstanbul.
  • Özbek, A. (2018). Türk hazır giyim ticaretinin alt sektörler bazında analizi. Uluslararası Beşeri Bilimler ve Eğitim Dergisi, 4(7), 161-183.
  • Hanson, L. (2019, Ocak 9). Whichplm. 5 reasons to adopt digital workflows across the supply chain: https://www.whichplm.com/5-reasons-to-adopt-digital-workflows-across-the-supply-chain/
  • Wang, B., & Ha-Brookshire, J. (2018). Perceived usefulness and perceived ease of use of new technologies described by chinese textile and apparel firm owners and managers, International Textile and Apparel Association (ITAA) Annual Conference Proceedings. 60, 1-3.
  • Ngai, E. W. T., Peng, S., Alexander, P., & Moon, K. K. (2014). Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles. Expert Systems with Applications, 41(1), 81-91.
  • Kagermann, H. „How Industrie 4.0 will coin the economy of the future?. The results of the german High-tech strategy's and Strategic initiative Industrie, 4.
  • Chiarello, F., Trivelli, L., Bonaccorsi, A., & Fantoni, G. (2018). Extracting and mapping industry 4.0 technologies using wikipedia. Computers in Industry, 100, 244-257.
  • Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.
  • Omitola, T., & Wills, G. (2018). Towards mapping the security challenges of the internet of things (IOT) supply chain. Procedia Computer Science, 126, 441-450.
  • Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems, 86, 614-628.
  • Alicke, K., Rexhausen, D., & Seyfert, A. (2017). Supply chain 4.0 in consumer goods. Mckinsey & Company, 1-11.
  • Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925-953.
  • Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168-179.
  • Nagy, G., Illés, B., & Bányai, Á. (2018). Impact of Industry 4.0 on production logistics, XXIII International Conference on Manufacturing (Manufacturing 2018), IOP Conf. Series: Materials Science and Engineering, 448, 1-9.
  • Bailey, G., Moss, C., & Whittaker, J. (2015). Digital supply chains: a frontside Flip, The Center for Global Enterprise.
  • Korpela, K., Hallikas, J., & Dahlberg, T. (2017, January). Digital supply chain transformation toward blockchain integration. In proceedings of the 50th Hawaii international conference on system sciences, 4182-4191.
  • Farahani, P., Meier, C., & Wilke, J. (2017). Digital supply chain management agenda for the automotive supplier industry. In Shaping the digital enterprise (pp. 157-172). Springer, Cham.
  • Yıldız, A., Karakoyun, F., & Parlak, İ. E. (2018). Mühendislik Alanında Akademik Araştırmalar. Endüstri 4.0 temelli dijital tedarik zinciri, 1, 416-426, Gece Kitaplığı, Ankara.
  • Guarraia, P., Gerstenhaber, G., Athanassiou, M., & Boutot, P. H. (2015). The intangible benefits of a digital supply chain. Bain & Company, 1-2.
  • Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
  • Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2019). Digital Supply Chain, Smart Operations and Industry 4.0. In Global Supply Chain and Operations Management (pp. 481-526). Springer, Cham.
  • Chan, H. K., Griffin, J., Lim, J. J., Zeng, F., & Chiu, A. S. (2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics, 205, 156-162.
  • Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manufacturing, 13, 1245-1252.
  • Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part E, 114 (2018) 343–349.
  • Stank, T., Scott, S., & Hazen, B. (2018). A savvy guide to the digital supply chain, The Global Supply Chain Institute White Papers, 1-56.
  • Büyüközkan, G., & Göçer, F. (2018). An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain. Applied Soft Computing, 69, 634-654.
  • Baykasoğlu, A., & Gölcük, İ. (2017). Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Systems with Applications, 70, 37-51
  • Deveci, M., Demirel, N. Ç., & Ahmetoğlu, E. (2017). Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. Journal of Air Transport Management, 59, 83-99.
  • Deveci, M., Canıtez, F., & Gökaşar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777-791.
  • Celik, E., & Akyuz, E. (2018). An interval type-2 fuzzy AHP and TOPSIS methods for decision-making problems in maritime transportation engineering: the case of ship loader. Ocean Engineering, 155, 371-381.
  • Yildiz, A., Karakoyun, F., & Parlak, I. E. (2018). The most suitable mobile RFID reader selection by using interval type-2 fuzzy topsis method. Sigma: Journal of Engineering & Natural Sciences, 36(3), 717-729.
  • Yildiz, A. (2016). Interval type 2-fuzzy TOPSIS and fuzzy TOPSIS method in supplier selection in garment industry/Metoda fuzzy TOPSIS Interval tip 2 si metoda fuzzy TOPSIS în selectarea furnizorului din industria de confectii. Industria Textila, 67(5), 322.
  • Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Systems with applications, 37(1), 824-833.
  • Lee, L. W., & Chen, S. M. (2008, July). A new method for fuzzy multiple attributes group decision-making based on the arithmetic operations of interval type-2 fuzzy sets. In 2008 International Conference on Machine Learning and Cybernetics(Vol. 6, pp. 3084-3089). IEEE.
  • Liao, T. W. (2015). Two interval type 2 fuzzy TOPSIS material selection methods. Materials & Design, 88, 1088-1099.
  • Mousakhani, S., Nazari-Shirkouhi, S., & Bozorgi-Amiri, A. (2017). A novel interval type-2 fuzzy evaluation model based group decision analysis for green supplier selection problems: A case study of battery industry. Journal of cleaner production, 168, 205-218.
  • Görener, A., Ayvaz, B., Kuşakcı, A. O., & Altınok, E. (2017). A hybrid type-2 fuzzy based supplier performance evaluation methodology: The Turkish Airlines technic case. Applied Soft Computing, 56, 436-445.
  • Kilic, M., & Kaya, İ. (2015). Investment project evaluation by a decision making methodology based on type-2 fuzzy sets. Applied Soft Computing, 27, 399-410.
  • Akyuz, E., & Celik, E. (2018). A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill. Maritime Policy & Management, 45(8), 979-994.

DIGITAL SUPPLIER SELECTION FOR A GARMENT BUSINESS USING INTERVAL TYPE-2 FUZZY TOPSIS

Year 2020, Volume: 30 Issue: 1, 61 - 72, 25.03.2020
https://doi.org/10.32710/tekstilvekonfeksiyon.569884

Abstract



Recent advances in digital technology and manufacturing have
changed the industry dramatically. Fundamentally, Industry 4.0 has begun to
affect many areas, aiming at improving production and engineering processes,
improving the quality of products and services, optimizing the relationship
between customers and organizations, bringing new business opportunities and
providing economic benefits. One of those areas is the supply chain management,
which greatly affects business productivity. Industry 4.0 has provided a highly
efficient digital supply chain that has established a smart connection between
supply, production, logistics and customers. This has promoted the digitization
of suppliers resulting in an increase in the performance of parent companies,
which, therefore, wish to identify and use the suppliers that best use digital
technologies. However, it is an uncertain decision problem which has many
criteria in order to determine these suppliers. Therefore, this problem can be
solved by multi-criteria decision-making methods that can best model
uncertainties.




In this study, it is aimed to select
the best one among the suppliers which are digitalized by using the industry
4.0 technologies from the suppliers of a parent firm operating in the garment
industry. In order to solve the selection problem, the interval type-2 fuzzy
TOPSIS method, which includes a interval of type-2 fuzzy sets and which can
model the uncertainties very well in solving fuzzy multi-criteria decision
making problems, was used. At the end of the study, alternatives were listed
according to closeness indexes and the best digital supplier selection was made
according to the results of sensitivity analysis and necessary evaluations were
made. As a result, the selection model used in this paper, which is the first
study in the literature on the selection of digital suppliers and which is not
included in the selection of traditional suppliers, can contribute to
researchers and practitioners by using them for other
industries.




References

  • Schwab, K. (2017). The fourth industrial revolution. Davos: World Economic Forum, 51-59
  • Yildiz, A. (2018). Digital supply chain integrated with industry 4.0. Business & Management Studies: An International Journal, 6(4), 1215-1230.
  • https://kemptechnologies.com/blog/the-4th-industrial-revolution/
  • Hobsbawm, E. (2010). Age of revolution: 1789-1848. Hachette UK.
  • Aksoy, S. (2017). Değişen teknolojiler ve endüstri 4.0: endüstri 4.0’ı anlamaya dair bir giriş. SAV Katkı, 4, 34-4.
  • Ovaci, C. (2017). Endüstri 4.0 çağında açık inovasyon. Maliye Finans Yazilari. (Özel Sayı), 113-132.
  • Bağcı, E. (2018). Endüstri 4.0: Yeni üretim tarzını anlamak. Gümüshane University Electronic Journal of the Institute of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 9(24).
  • Toker, K. (2018). Endüstri 4.0 ve Sürdürülebilirliğe Etkileri. Institute of Business Administration-Management Journal/Isletme Iktisadi Enstitüsü Yönetim Dergisi, 29(84), 51-64.
  • Fırat, S. Ü., & Fırat, O. Z. (2017). Sanayi 4.0 devrimi üzerine karşılaştırmalı bir inceleme: Kavramlar, küresel gelişmeler ve Türkiye. Toprak İşveren Dergisi, 114(2017), 10-23.
  • Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394.
  • Rojko, A. (2017). Industry 4.0 concept: background and overview. International Journal of Interactive Mobile Technologies (iJIM), 11(5), 77-90.
  • Tsai, W. H. (2018). Green production planning and control for the textile industry by using mathematical programming and industry 4.0 techniques. Energies, 11(8), 2072.
  • Tansan, B., Gökbulut, A., Targotay, Ç., & Eren, T. (2016). Türkiye’nin küresel rekabetçiliği için bir gereklilik olarak sanayi 4.0 gelişmekte olan ekonomi perspektifi. TÜSİAD Raporu.
  • https://www.manufacturersalliance.co.uk/2018/03/08/sme-manufacturers-adopting-industry-4-0-technologies/
  • Bulut, E., & Akçacı, T. (2017). Endüstri 4.0 ve inovasyon göstergeleri kapsamında Türkiye analizi. ASSAM Uluslararası Hakemli Dergi, 4(7), 55-77.
  • Tsai, W. H., & Lai, S. Y. (2018). Green production planning and control model with abc under industry 4.0 for the paper industry. Sustainability, 10(8), 2932.
  • Duarte, A. Y. S., Sanches, R. A., & Dedini, F. G. (2018). Assessment and technological forecasting in the textile industry: From first industrial revolution to the Industry 4.0. Strategic Design Research Journal, 11(3), 193-202.
  • Chen, Z., & Xing, M. (2015, October). Upgrading of textile manufacturing based on Industry 4.0. In 5th International Conference on Advanced Design and Manufacturing Engineering. Atlantis Press.
  • Alçın, S. (2016). Üretim için yeni bir izlek: sanayi 4.0. Journal of Life Economics, 3(2), 19-30.
  • Özbek, A., (2005), Örnek işletmeler bazında türk hazır giyim sanayinin yapısı, ihracatı ve geleceği, Marmara Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, İstanbul.
  • Özbek A., (2009), Türk hazır giyim sanayinin örnek ürün bazında (denim pantolon) gelecekteki ihracat performansının incelenmesi, Marmara Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi, İstanbul.
  • Özbek, A. (2018). Türk hazır giyim ticaretinin alt sektörler bazında analizi. Uluslararası Beşeri Bilimler ve Eğitim Dergisi, 4(7), 161-183.
  • Hanson, L. (2019, Ocak 9). Whichplm. 5 reasons to adopt digital workflows across the supply chain: https://www.whichplm.com/5-reasons-to-adopt-digital-workflows-across-the-supply-chain/
  • Wang, B., & Ha-Brookshire, J. (2018). Perceived usefulness and perceived ease of use of new technologies described by chinese textile and apparel firm owners and managers, International Textile and Apparel Association (ITAA) Annual Conference Proceedings. 60, 1-3.
  • Ngai, E. W. T., Peng, S., Alexander, P., & Moon, K. K. (2014). Decision support and intelligent systems in the textile and apparel supply chain: An academic review of research articles. Expert Systems with Applications, 41(1), 81-91.
  • Kagermann, H. „How Industrie 4.0 will coin the economy of the future?. The results of the german High-tech strategy's and Strategic initiative Industrie, 4.
  • Chiarello, F., Trivelli, L., Bonaccorsi, A., & Fantoni, G. (2018). Extracting and mapping industry 4.0 technologies using wikipedia. Computers in Industry, 100, 244-257.
  • Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.
  • Omitola, T., & Wills, G. (2018). Towards mapping the security challenges of the internet of things (IOT) supply chain. Procedia Computer Science, 126, 441-450.
  • Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems, 86, 614-628.
  • Alicke, K., Rexhausen, D., & Seyfert, A. (2017). Supply chain 4.0 in consumer goods. Mckinsey & Company, 1-11.
  • Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925-953.
  • Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168-179.
  • Nagy, G., Illés, B., & Bányai, Á. (2018). Impact of Industry 4.0 on production logistics, XXIII International Conference on Manufacturing (Manufacturing 2018), IOP Conf. Series: Materials Science and Engineering, 448, 1-9.
  • Bailey, G., Moss, C., & Whittaker, J. (2015). Digital supply chains: a frontside Flip, The Center for Global Enterprise.
  • Korpela, K., Hallikas, J., & Dahlberg, T. (2017, January). Digital supply chain transformation toward blockchain integration. In proceedings of the 50th Hawaii international conference on system sciences, 4182-4191.
  • Farahani, P., Meier, C., & Wilke, J. (2017). Digital supply chain management agenda for the automotive supplier industry. In Shaping the digital enterprise (pp. 157-172). Springer, Cham.
  • Yıldız, A., Karakoyun, F., & Parlak, İ. E. (2018). Mühendislik Alanında Akademik Araştırmalar. Endüstri 4.0 temelli dijital tedarik zinciri, 1, 416-426, Gece Kitaplığı, Ankara.
  • Guarraia, P., Gerstenhaber, G., Athanassiou, M., & Boutot, P. H. (2015). The intangible benefits of a digital supply chain. Bain & Company, 1-2.
  • Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
  • Ivanov, D., Tsipoulanidis, A., & Schönberger, J. (2019). Digital Supply Chain, Smart Operations and Industry 4.0. In Global Supply Chain and Operations Management (pp. 481-526). Springer, Cham.
  • Chan, H. K., Griffin, J., Lim, J. J., Zeng, F., & Chiu, A. S. (2018). The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives. International Journal of Production Economics, 205, 156-162.
  • Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manufacturing, 13, 1245-1252.
  • Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research Part E, 114 (2018) 343–349.
  • Stank, T., Scott, S., & Hazen, B. (2018). A savvy guide to the digital supply chain, The Global Supply Chain Institute White Papers, 1-56.
  • Büyüközkan, G., & Göçer, F. (2018). An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain. Applied Soft Computing, 69, 634-654.
  • Baykasoğlu, A., & Gölcük, İ. (2017). Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Systems with Applications, 70, 37-51
  • Deveci, M., Demirel, N. Ç., & Ahmetoğlu, E. (2017). Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey-North American region destinations. Journal of Air Transport Management, 59, 83-99.
  • Deveci, M., Canıtez, F., & Gökaşar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777-791.
  • Celik, E., & Akyuz, E. (2018). An interval type-2 fuzzy AHP and TOPSIS methods for decision-making problems in maritime transportation engineering: the case of ship loader. Ocean Engineering, 155, 371-381.
  • Yildiz, A., Karakoyun, F., & Parlak, I. E. (2018). The most suitable mobile RFID reader selection by using interval type-2 fuzzy topsis method. Sigma: Journal of Engineering & Natural Sciences, 36(3), 717-729.
  • Yildiz, A. (2016). Interval type 2-fuzzy TOPSIS and fuzzy TOPSIS method in supplier selection in garment industry/Metoda fuzzy TOPSIS Interval tip 2 si metoda fuzzy TOPSIS în selectarea furnizorului din industria de confectii. Industria Textila, 67(5), 322.
  • Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. Expert Systems with applications, 37(1), 824-833.
  • Lee, L. W., & Chen, S. M. (2008, July). A new method for fuzzy multiple attributes group decision-making based on the arithmetic operations of interval type-2 fuzzy sets. In 2008 International Conference on Machine Learning and Cybernetics(Vol. 6, pp. 3084-3089). IEEE.
  • Liao, T. W. (2015). Two interval type 2 fuzzy TOPSIS material selection methods. Materials & Design, 88, 1088-1099.
  • Mousakhani, S., Nazari-Shirkouhi, S., & Bozorgi-Amiri, A. (2017). A novel interval type-2 fuzzy evaluation model based group decision analysis for green supplier selection problems: A case study of battery industry. Journal of cleaner production, 168, 205-218.
  • Görener, A., Ayvaz, B., Kuşakcı, A. O., & Altınok, E. (2017). A hybrid type-2 fuzzy based supplier performance evaluation methodology: The Turkish Airlines technic case. Applied Soft Computing, 56, 436-445.
  • Kilic, M., & Kaya, İ. (2015). Investment project evaluation by a decision making methodology based on type-2 fuzzy sets. Applied Soft Computing, 27, 399-410.
  • Akyuz, E., & Celik, E. (2018). A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill. Maritime Policy & Management, 45(8), 979-994.
There are 59 citations in total.

Details

Primary Language English
Subjects Wearable Materials
Journal Section Articles
Authors

Ahmet Özbek

Aytaç Yıldız

Publication Date March 25, 2020
Submission Date May 24, 2019
Acceptance Date February 17, 2020
Published in Issue Year 2020 Volume: 30 Issue: 1

Cite

APA Özbek, A., & Yıldız, A. (2020). DIGITAL SUPPLIER SELECTION FOR A GARMENT BUSINESS USING INTERVAL TYPE-2 FUZZY TOPSIS. Textile and Apparel, 30(1), 61-72. https://doi.org/10.32710/tekstilvekonfeksiyon.569884

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