Year 2021, Volume 13 , Issue 1, Pages 87 - 103 2021-01-18

Sezgisel Bulanık Kalite Fonksiyon Yayılımı ve Bir Uygulama
Intuitionistic Fuzzy Quality Function Deployment and an Application

Müge BULUT [1] , Ümit SAKALLI [2]


Günümüzde hızla gelişmekte olan endüstriyel ürünlerdeki yenilikler, müşteri isteklerine cevap verebilmek, pazar koşullarına uyum sağlayabilme kabiliyeti ve rekabet açısından önemlidir. Müşteri ihtiyaç ve beklentileri, piyasada yer alan ürünler ve geliştirilmekte olan ürünler üzerinde büyük rol oynamaktadır. Kalite fonksiyon yayılımı, müşteri isteklerini dinleyerek ürün geliştirmede odaklanılması gereken teknik gereksinimlerin cevabını sunmaktadır. Bu araştırma, endüstriyel prizlerin ürün geliştirilmesi üzerine odaklanmaktadır. Çalışmada ürün geliştirme için bir araç olan kalite fonksiyon yayılımı modeli önerilmiştir. Çalışmanın amacı belirsizlik ortamında müşteri istekleri ve teknik gereksinimler arasındaki ilişkiyi en doğru şekilde incelemek ve ürün geliştirilmesi açısından odaklanılması gereken en önemli teknik gereksinimi ortaya koyabilmektir. Geleneksel kalite fonksiyon yayılımında uzman görüşlerini alma, müşteri istekleri önem derecesini ağırlıklandırmada ve teknik gereksinimleri önem derecelerine göre sıralamada sınırlılıkları açısından eleştiriler yer almaktadır. Bu sınırlılıklar ile başa çıkabilmek ve sözel değişkenlerde belirsizliğin üstesinden gelebilmek için çalışmada müşteri istekleri ağırlıklandırılırken sezgisel bulanık AHP, müşteri istekleri ve teknik gereksinimlerin birbirleri ile ilişkileri incelenirken sezgisel bulanık VIKOR yöntemine başvurulmuş teknik gereksinimler yöntem sonucunda sıralanmıştır.
Today,innovations in rapidly developing industrial products are important in terms of their ability to respond to customer requirements,to adapt to market conditions and competition. Quality function deployment provides the answer to the technical characteristics that should focus on product development by consedering to customer requirements.This research focuses on product development of industrial sockets.In the study, quality function deployment method,which is a tool for product development, was applied. The aim of the study is to examine the relationship between customer requirements and technical characteristics in the uncertainty environment in the most accurate way and to reveal the most important technical characteristics that should be focused on in terms of product development. In order to deal with these limitations, intuitionistic fuzzy AHP, while investigating the relationship between customer requirements and technical characteristics, were listed as a result of the technical requirements applied to the intuitionistic fuzzy VIKOR method.
  • Abdullah, L., & Najib, L. (2014). A new preference scale of intuitionistic fuzzy analytic hierarchy process in multi-criteria decision making problems. Journal of Intelligent & Fuzzy Systems, 26(2), 1039-1049.
  • Abdullah, L., Sunadia, J., & Imran, T. (2009). A new analytic hierarchy process in multi-attribute group decision making. International Journal of Soft Computing, 4(5), 208-214.
  • Akbaş, H., & Bilgen, B. (2017). An integrated fuzzy qfd and topsis methodology for choosing the ideal gas fuel at wwtps. Energy, 125, 484-497.
  • Akman, G., & Özcan, B. (2011). A fuzzy qfd approach to determine customer needs for driving mirror. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 19(10), 1-21.
  • Ariff, H., Salit, M. S., Ismail, N., & Nukman, Y. (2008). Use of analytical hierarchy process (ahp) for selecting the best design concept. Jurnal Teknologi, 49(1), 1-18.
  • Başkır, M. B. (2017). 4-Aşamalı bulanık kalite fonksiyon yayılımı yaklaşımı ile tedarikçi seçimi. Verimlilik Dergisi, (4), 81-110.
  • Başkır, M. B. Y., & Öksoy, D. T. D. (2011). Bulanık kalite fonksiyon yayılımı yaklaşımının iyileştirilmesi ve uygulamaları, Doktora Tezi, Ankara Üniversitesi.
  • Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141-164.
  • Bhuvanesh Kumar, M., & Parameshwaran, R. (2018). Fuzzy integrated qfd, fmea framework for the selection of lean tools in a manufacturing organisation. Production Planning & Control, 29(5), 403-417.
  • Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with topsis method. Expert Systems with Applications, 36(8), 11363-11368.
  • Brown, S. L., & Eisenhardt, K. M. (1995). Product development: Past research, present findings, and future directions. Academy of management review, 20(2), 343-378.
  • Buckley, J. J., & Uppuluri, V. R. R. (1985). Fuzzy hierarchical analysis. In Uncertainty in Risk Assessment, Risk Management, and Decision Making, 389-401. Springer, Boston, MA.
  • Büyüközkan, G., & Uztürk, D. (2019). Smart fridge design with ınterval-valued ıntuitionistic fuzzy qfd. In International Conference on Intelligent and Fuzzy Systems 1170-1179, Springer, Cham.
  • Chaghooshi, A. J., Khorasani, A., & Mesbah, M. (2015). Determine the correlation between supplier‘s evaluation criteria and customer‘s wants in automotive supply chain: by the approach of fuzzy-qfd and house of quality. Global Journal of Management Studies and Researches, 2(1), 48-59.
  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy ahp. European Journal of Operational Research, 95(3), 649-655.
  • Chatterjee, K., Kar, M. B., & Kar, S. (2013). Strategic decisions using Intuitionistic fuzzy vikor method for information system (is) outsourcing. International Symposium on Computational and Business Intelligence, 123-126.
  • Çinpolat, S. (2007). Kalite fonksiyon göçerimi ve hizmet sektöründe uygulanması. Yüksek Lisans Tezi, İstanbul Üniversitesi.
  • Demirer, A. (2017). Güneş enerjisi santrali yer seçimi probleminin analitik hiyerarşi prosesi yardımı ile değerlendirilmesi, Yüksek Lisans Tezi, Beykent Üniversitesi.
  • Deveci, M., Öner, S. C., Canıtez, F., & Öner, M. (2019). Evaluation of service quality in public bus transportation using interval-valued intuitionistic fuzzy qfd methodology. Research in Transportation Business & Management, 100387.
  • Dincer, H., Yüksel, S., & Martinez, L. (2019). Balanced scorecard-based analysis about european energy investment policies: A hybrid hesitant fuzzy decision-making approach with quality function deployment. Expert Systems with Applications, 115, 152-171.
  • Durdudiler, M. (2006). Perakende sektöründe tedarikçi performans değerlemesinde ahp ve bulanık ahp uygulaması, Yüksek Lisans Tezi, Yıldız Teknik Üniversitesi.
  • Feili, H., Qomi, M., Farzooghi, A., & Lashgari, H. (2018). Identification of design requirements in automotive glass manufacturing using fuzzy qfd. Internatiol Conference on Research in Engineering Science and Technology.
  • Feili, H., Qomi, M., Zadrafi, S., & Asadi, A. (2018). Assessment of design and customer requirements in cinema industry (highest-grossing films) using fuzzy qfd. 7th International Conference of Science and Engineering.
  • Franceschini, F. (2016). Advanced quality function deployment. CRC Press.
  • Ghadimi, P. Azadnia, A. H., & Azadnia, A. H.,. (2017). An integrated approach of fuzzy quality function deployment and fuzzy multi-objective programming to sustainable supplier selection and order allocation. Journal of Optimization in Industrial Engineering, 11(1), 1-22.
  • Huang, J., You, X. Y., Liu, H. C., & Si, S. L. (2019). New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory. International Journal of Production Research, 57(5), 1283-1299.
  • Jian, S., Xiu-yan, P., Ying, X., Pei-Lei, W., & Na-ji, M. (2016). A new method combining qfd with ıntuitionistic fuzzy sets for web services selection. International Journal of Multimedia and Ubiquitous Engineering, 11(11), 107-118.
  • Khorheh, M.A., & Davarzani, H., (2013). A novel application of intuitionistic fuzzy sets theory in medical science: Bacillus colonies recognition. Artif. Intell. Research, 2, 1-17.
  • Kumar, K., & Kumanan, S. (2011). An integrated fuzzy qfd and ahp approach for facility location selection. IUP Journal of Supply Chain Management, 8(4), 31-41.
  • Kutlu Gündoğdu, F., Kahraman, C., & Karaşan, A. (2019). Spherical fuzzy vikor method and its application to waste management. International Conference on Intelligent and Fuzzy Systems, 997-1005.
  • Lee, A. H., Kang, H. Y., Lin, C. Y., & Chen, J. S. (2017). A novel fuzzy quality function deployment framework. Quality technology & quantitative management, 14(1), 44-73.
  • Li, M., Jin, L., & Wang, J. (2014). A new MCDM method combining qfd with topsis for knowledge management system selection from the user's perspective in intuitionistic fuzzy environment. Applied soft computing, 21, 28-37.
  • Li, X., & Song, W. (2016). A rough vikor-based qfd for prioritizing design attributes of product-related service. Mathematical Problems in Engineering, 2016.
  • Liao, H., & Xu, Z. (2013). A vikor-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making, 12(4), 373-392.
  • Mayyas, A., Shen, Q., Mayyas, A., Shan, D., Qattawi, A., & Omar, M. (2011). Using quality function deployment and analytical hierarchy process for material selection of body-in-white. Materials & Design, 32(5), 2771-2782.
  • Mazur, G. H. & Akao, Y. (2003). The leading edge in qfd: Past, present and future. International Journal of Quality & Reliability Management, 20(1), 20-35.
  • Nilay Yücenur, G., Altun, G., & Erdem, M. Determining design attributes of a small household appliance using fuzzy vikor-based qfd method. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9(1), 272-284.
  • Onar, S. Ç., Büyüközkan, G., Öztayşi, B., & Kahraman, C. (2016). A new hesitant fuzzy qfd approach: an application to computer workstation selection. Applied Soft Computing, 46, 1-16.
  • Opricovic, S. (1998). Multi-criteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, 2(1) 5-21.
  • Piengang, F. C. N., Beauregard, Y., & Kenné, J. P. (2019). An aps software selection methodology integrating experts and decisions-maker’s opinions on selection criteria: A case study. Cogent Engineering, 6(1), 1594509.
  • Pur, M. M., & Tabriz, A. A. (2012). SWOT analysis using of modified fuzzy qfd-a case study for strategy formulation in petrokaran film factory. Procedia-Social and Behavioral Sciences, 41, 322-333.
  • Rossetto, S. &Franceschini, F., (1995). Qfd: the problem of comparing technical/engineering design requirements. Research in Engineering Design, 7(4), 270-278.
  • Sadiq, R., & Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (ıf-ahp). Stochastic Environmental Research and Risk Assessment, 23(1), 75-91.
  • Silavi, T., Malek, M. R., & Delavar, M. R. (2006). Multicriteria map overlay in geospatial information system via intuitionistic fuzzy ahp method. In Applied Artificial Intelligence, 401-408.
  • Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy sets and systems, 114(3), 505-518.
  • Tavakoli, M., & Pasha, N. (2015). Integrating fuzzy quality function deployment and linear goal programming for supplier selection. Uncertain Supply Chain Management, 3(1), 1-10.
  • Van De Poel, I. (2007). Methodological problems in qfd and directions for future development. Research in engineering design, 18(1), 21-36.
  • Van Laarhoven, P. J., & Pedrycz, W. (1983). A fuzzy extension of Saaty's priority theory. Fuzzy Sets and Systems, 11(1-3), 229-241.
  • Vinodh, S., & Rathod, G. (2012). Application of fuzzy logic-based environmental conscious qfd to rotary switch: A case study. Clean Technologies and Environmental Policy, 14(2), 319-332.
  • Vongvit, R., Kongprasert, N., Fournaise, T., & Collange, T. (2017). Integration of fuzzy-qfd and triz methodology for product development. In 2017 3rd International Conference on Control, Automation and Robotics, 326-329.
  • Wang, H., Qian, G., & Feng, X. (2011). An intuitionistic fuzzy ahp based on synthesis of eigenvectors and its application. Information Technology Journal, 10(10), 1850-1866.
  • Wang, H., Qian, G., & Feng, X. (2011). An intuitionistic fuzzy ahp based on synthesis of eigenvectors and its application. Information Technology Journal, 10(10), 1850-1866.
  • Wang, T. C., Liang, J. L., & Ho, C. Y. (2006). Multi-criteria decision analysis by using fuzzy vikor. International Conference on Service Systems and Service Management 2, 901-906.
  • Wu, S. M., Liu, H. C., & Wang, L. E. (2017). Hesitant fuzzy integrated mcdm approach for quality function deployment: A case study in electric vehicle. International Journal of Production Research, 55(15), 4436-4449.
  • Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179-1187.
  • Yaralıoğlu, K. (2001). Performans değerlendirmede analitik hiyerarşi proses. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 16(1), 129-142.
  • Yenginol, F. (2000). Yeni ürün geliştirmede müşteri istek ve ihtiyaçlarını teknik karakteristiklere dönüştürmeyi sağlayan bir yöntem: Kalite fonksiyon göçerimi. Doktora Tezi, Dokuz Eylül Üniversitesi, İzmir.
  • Yenginol, F. (2008). Neden kalite fonksiyon" göçerimi” ?. Dokuz Eylül Üniversitesi İşletme Fakültesi Dergisi, 9(1), 7-15.
  • Zadeh, L. A. (1965). Fuzzy sets, Information and Control, vol. 8. Google Scholar Digital Library, 338-353.
Primary Language tr
Subjects Industrial Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-5353-2591
Author: Müge BULUT (Primary Author)
Institution: KIRIKKALE ÜNİVERSİTESİ
Country: Turkey


Orcid: 0000-0002-1695-3151
Author: Ümit SAKALLI
Institution: KIRIKKALE ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date : January 18, 2021

APA Bulut, M , Sakallı, Ü . (2021). Sezgisel Bulanık Kalite Fonksiyon Yayılımı ve Bir Uygulama . International Journal of Engineering Research and Development , 13 (1) , 87-103 . DOI: 10.29137/umagd.730775