Çatı Matrisi Korelasyon Probleminin Çözümü İçin Kalite Fonksiyonları Göçeriminin Bulanık Bilişsel Haritalar İle Bütünleştirilmesi

Year 2022, Volume: 22 Issue: 2, 117 - 138, 28.04.2022

Gül Emel , Gülcan Petriçli , Cem Kayguluoğlu

https://doi.org/10.21121/eab.787075

Cited By: 2

Abstract

Çatı matrisi, Kalite Fonksiyon Göçerimi yönteminin kalite evindeki mühendislik karakteristiklerinin (MK) kendi aralarındaki korelasyonlarını temsil etmektedir. Alan yazındaki bir çok çalışmada korelasyonlar nitel olarak ölçülmekte ve bu yüzden analizlerde göz ardı edilmektedir. Fakat korelasyonların dikkate alınmaması, gereksiz iyileştirmelere, ürün performansının düşmesine ve müşteri gereksinimlerinin (MG) karşılanamamasına neden olabilmektedir. Bu nedenle bu çalışma, çatı korelasyonlarının nicel olarak değerlendirilebilmesini sağlayan bir yaklaşım önermeyi amaçlamaktadır. Söz konusu amaç için Bulanık Bilişsel Haritalar (BBH) yöntemi kullanılmıştır. Ek olarak, MG'ler ve MK'ler arasındaki ilişkileri incelemek için Aksiyomatik Tasarım (AT) ve ilişkilerin değerlendirilmesinde tutarlılık kontrolü için Genişletilmiş Bulanık Analitik Hiyerarşi Süreci (GBAHS) kullanılmıştır. Önerilen yaklaşım, otomotiv sanayisinde faaliyet gösteren sac-metal kalıp üreten bir üretim işletmesinin kalıplar için genellenmiş MG'leri ve MK'lerinin önceliklendirilmesi problemine uygulanmıştır. Elde edilen sonuçlara göre BBH'ler, kantitatif çatı matrisinin pratik bir şekilde analiz edilmesini sağlayan etkili bir yöntemdir. Kare tipi çatı matrisi, BBH’lerin komşuluk matrisinin kullanımını desteklemekte ve MK’ler arasındaki asimetrik ilişkilerin başarı ile temsil edilmesini sağlamaktadır. Çalışmada ele alınan önceliklendirme probleminde korelasyonların analize dahil edilmesi ise nihai sıralamanın değişmesine neden olmuş ve ayrıca en yönetilebilir MK'lerin, tatmin edilebilmesi en olası olan MG'lerin ve en kritik/en az yönetilebilir MK'lerin belirlenmesine yardımcı olmuştur.

Keywords

Kalite Fonksiyonları Göçerimi, Bulanık Bilişsel Haritalar, Asimetrik Kare Çatı Matrisi, Bağımsızlık Aksiyomu, Genişletilmiş Bulanık Analitik Hiyerarşi Süreci

Integrating Quality Function Deployment with Fuzzy Cognitive Maps for Resolving Correlation Issues in the Roof Matrix

Abstract

The roof matrix represents correlations among engineering characteristics (EC) in the house of quality (HoQ) in Quality Functions Deployment (QFD). Correlations are usually measured qualitatively and omitted in the analysis. However, ignoring them may cause duplication of effort, decreased product performance, and unsatisfied customer requirements (CR). Hence, this paper intends to propose an approach to considering the correlations quantitatively. Fuzzy Cognitive Maps (FCM) were used for this purpose. Additionally, Axiomatic Design (AD), for examining relationships between CRs and ECs, and Fuzzy Analytic Hierarchy Process (FAHP) with the Extent Analysis (EA) were used for checking the consistency of the evaluations. The proposed approach was applied in a sheet metal die making company for ranking CRs and ECs. Results show that FCM enables analyzing the quantitative roof matrix practically. The square roof matrix that also supports FCM’s adjacency matrix structure successfully represents asymmetric relationships among ECs. Integrating the correlations into the analysis resulted in a change in the final ranking. It also helped to determine the most manageable ECs, better satisfiable CRs, and most critical/least manageable ECs.

Keywords

Quality Function Deployment, Fuzzy Cognitive Maps, Asymmetric Square Roof Matrix, Independence Axiom, Fuzzy Analytic Hierarchy Process, Extent Analysis

References

• Abastante, F., & Lami, I. (2012). Quality function deployment (QFD) and analytic network process (ANP): an application to analyze a cohousing intervention. J Appl Oper Res, 4(January 2012), 14–27.
• Arsenyan, J.; Büyüközkan, G. (2016). An integrated fuzzy approach for information technology planning in collaborative product development. International Journal of Production Research, 54(11), 3149–3169.
• Bencherif, F., Mouss, L. H., & Benaicha, S. (2013). Fuzzy relative importance of customer requirements in improving product development. In 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO) (pp. 1–6).
• Besterfield, D. H., Besterfield-Michna, C., Besterfield, G. H., Besterfield-Sacre, M., Urdhwareshe, H., & Urdhwareshe, R. (2011). Total Quality Management (3rd edition). Pearson.
• Carnevalli, J. A., Miguel, P. A. C., & Calarge, F. A. (2010). Axiomatic design application for minimising the difficulties of QFD usage. International Journal of Production Economics, 125(1), 1–12. https://doi.org/10.1016/J.IJPE.2010.01.002
• Cauchick Miguel, P. A., Carnevalli, J. A., & Calarge, F. A. (2007). Using axiomatic design for minimizing QFD application difficulties in NDP: Research proposal and preliminary definition of first and second hierarchical levels. Product: Management & Development, 5(December), 127–132.
• Cavallini, C., Citti, P., Costanzo, L., & Giorgetti, A. (2013). An axiomatic approach to managing the information content in QFD: Applications in material selection. In ICAD2013 The 7th International Conference on Axiomatic Design. Worcester.
• Çebi, S., & Kahraman, C. (2010). Determining design characteristics of automobile seats based on fuzzy axiomatic design principles. International Journal of Computational Intelligence Systems, 3(1), 43–55. https://doi.org/10.2991/ijcis.2010.3.1.5
• Çebi, S., & Kahraman, C. (2011). Bulanık aksiyomlarla tasarıma dayalı otomobil göstergesi tasarımı. İTÜ Dergisi/D Mühendislik, 10(2), 27–38.
• Çelik, M., Çebi, S., Kahraman, C., & Er, I. D. (2009). An integrated fuzzy QFD model proposal on routing of shipping investment decisions in crude oil tanker market. Expert Systems with Applications, 36, 6227–6235. https://doi.org/10.1016/j.eswa.2008.07.031
• Chan, L.-K., & Wu, M.-L. (2002). Quality function deployment: A comprehensive review of its concepts and methods. Quality Engneering, 15(1), 23–35.
• Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.
• Christoforou, A., & Andreou, A. S. (2017). A framework for static and dynamic analysis of multi-layer fuzzy cognitive maps. Neurocomputing, 232(September 2016), 133–145.
• El-Haik, B., & Wasiloff, J. M. (2004). Axiomatic design quality engineerimg - A transmission planetary sace study. In The 3rd International Conference on Axiomatic Design (pp. 1–8). Seoul.
• Erkarslan, Ö., & Yılmaz, H. (2011). Optimization of the product design through quality function deployment (QFD) and analytical hierarchy process (AHP): A case study in a seramic washbasin. METU JFA, 28(1), 1–22. https://doi.org/10.4305/METU.JFA
• Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., & Bello, R. (2017). A review on methods and software for fuzzy cognitive maps. Artificial Intelligence Review, 1–31.
• Goncalves-Coelho, A. M., Mourao, A. J. F., & Pereira, Z. L. (2005). Improving the use of QFD with axiomatic Design. Concurrent Engineering, 13(3), 233–239.
• Groumpos, P. P. (2010). Fuzzy cognitive maps: Basic theories and their application to complex systems. In M. Glykas (Ed.), Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications (pp. 1–22). Springer.
• Hugo Torres, V., Rios, J., Vizan, A., & Pérez, J. M. (2010). Integration of design tools and knowledge capture into a CAD system: A case study. Concurrent Engineering: Research and Applications, 18(4), 311–324. https://doi.org/10.1177/1063293X10389788
• Iqbal, Z., Grigg, N. P., Govindaraju, K., & Campbell-Allen, N. M. (2015). A distance-based methodology for increased extraction of information from the roof matrices in QFD studies. International Journal of Production Research, 54(11), 1–17.
• Karsak, E. E. (2004). Fuzzy multiple objective programming framework to prioritize design requirements in quality function deployment. Computers and Industrial Engineering, 47(2–3), 149–163. https://doi.org/10.1016/j.cie.2004.06.001
• Kordi, M. (2008). Comparison of fuzzy and crisp analytic hierarchy process ( AHP ) methods for spatial multicriteria decision analysis in GIS. Decision Analysis, (September), 1–55.
• Kwong, C. K., & Bai, H. (2003). Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IIE Transactions, 35(7), 619–626. https://doi.org/10.1080/07408170304355
• Kwong, C. K., Chen, Y., Bai, H., & Chan, D. S. K. (2007). A methodology of determining aggregated importance of engineering characteristics in QFD. Computers and Industrial Engineering, 53(4), 667–679. https://doi.org/10.1016/j.cie.2007.06.008
• Li, Y. L., Tang, J. F., Chin, K. S., Han, Y., & Luo, X. G. (2012). A rough set approach for estimating correlation measures in quality function deployment. Information Sciences, 189, 126–142. https://doi.org/10.1016/j.ins.2011.12.002
• Li, Y. L., Tang, J. F., & Luo, X. G. (2010). An ECI-based methodology for determining the final importance ratings of customer requirements in MP product improvement. Expert Systems with Applications, 37(9), 6240–6250. https://doi.org/10.1016/j.eswa.2010.02.100
• Liu, H. T. (2011). Product design and selection using fuzzy QFD and fuzzy MCDM approaches. Applied Mathematical Modelling, 35(1), 482–496.
• Manchulenko, N. (2001). Applying Axiomatic Design Principles to The House of Quality.
• Mazur, G. H. (1997). Annual Quality Congress Transactions. In Voice of customer analysis: a modern system of front-end QFD tools, with case studies (pp. 486–495).
• Mistarihi, M. Z., Okour, R. A., & Mumani, A. A. (2020). An integration of a QFD model with Fuzzy-ANP approach for determining the importance weights for engineering characteristics of the proposed wheelchair design. Applied Soft Computing Journal, 90.
• Moskowitz, H., & Kim, K. J. (1997). QFD optimizer: A novice friendly quality function deployment decision support system for optimizing product designs. Computers and Industrial Engineering, 32(3), 641–655. https://doi.org/10.1016/S0360-8352(96)00309-9
• Oguztimur, S. (2011). Why Fuzzy Analytic Hierarchy Process Approach for Transport Problems? European Regional Science Association/IDEAS.
• Olewnik, A. T., & Lewis, T. (2005). On Validating Engineering Design Decision Support Tools. Concurrent Engineering, 13(2), 111–122.
• Özgener, Ş. (2003). Quality function deployment: A teamwork approach. Total Quality Management and Business Excellence, 14(9), 969–979.
• Papageorgiou, E. I. (2012). Learning algorithms for fuzzy cognitive maps - A review study. In IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews (Vol. 42, pp. 150–163). IEEE. https://doi.org/10.1109/TSMCC.2011.2138694
• Papageorgiou, E. I., & Salmeron, J. L. (2013). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems.
• Reich, Y., & Levy, E. (2004). Managing product design quality under resource constraints. International Journal of Production Research, 42(13), 2555–2572.
• Reich, Y., & Paz, A. (2008). Managing product quality, risk, and resources through resource quality function deployment. Journal of Engineering Design, 19(3), 249–267.
• Sanayei, A., Farid Mousavi, S., & Yazdankhah, A. (2010). Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37(1), 24–30. https://doi.org/10.1016/j.eswa.2009.04.063
• Srichetta, P., & Thurachon, W. (2012). Applying fuzzy analytic hierarchy process to evaluate and select product of notebook computers. International Journal of Modeling and Optimization, 2(2), 168–173. https://doi.org/10.7763/IJMO.2012.V2.105
• Stach, W., Kurgan, L., & Pedrycz, W. (2010). Expert-based and computational methods for developing fuzzy cognitive maps. In M.
• Glykas (Ed.), Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications. Springer.
• Taglia, A. Del, & Campatelli, G. (2006). Axiomatic design & Qfd: A case study of a reverse engineering. In Proceedings of ICAD 2006 4th International Conference on Axiomatic Design (pp. 1–6).
• Tseng, C. C., & Torng, C. C. (2011). Prioritization determination of project tasks in QFD process using design structure matrix. Journal of Quality, 18(2), 137–154.
• Upadhyay, R. K., Hans Raj, K., & Dwivedi, S. N. (2012). Fuzzy quality function deployment (FQFD) to assess student requirement in engineering institutions: An Indian prospective. 2012 IEEE International Technology Management Conference, 2(5), 364–368.
• van Aartsengel, A., & Kurtoğlu, S. (2013). Handbook on Continuous Improvement Transformation: The Lean Six Sigma Framework and Systematic Methodology for Implementation. Springer. https://doi.org/10.1007/978-3-642-35901-9