SUPPLIER SELECTION AMONG DIFFERENT SCALE CONSTRUCTION COMPANIES USING FUZZY AHP AND FUZZY TOPSIS

Year 2022, Volume: 9 Issue: 1, 35 - 48, 31.01.2022

Ayşe Karabayır Ahmet Reha Botsalı

https://doi.org/10.31202/ecjse.933617

Cited By: 1

Abstract

Supplier selection is an important strategic decision for construction companies due to long-term project durations and high costs. In the construction sector, supplier selection decisions should take several criteria and alternatives into account. Along with the high number of alternative suppliers, choosing the best method to evaluate the alternative suppliers is another critical step. In this study, we compare the effectiveness of different multi-criteria decision‐making methods for selecting the most convenient supplier for construction companies. In this respect, we study the integration of powerful multi-criteria decision‐making methods, Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). We use Fuzzy AHP for the calculation of decision criteria weights, and then we apply Fuzzy TOPSIS for ranking the alternatives. Several interviews were made with the experts at construction companies and as a result of those interviews, 7 main criteria and 24 sub-criteria for comparing alternative suppliers of the companies were determined. We test our approach for two construction companies and use an integrated Fuzzy AHP and Fuzzy TOPSIS method to find the best supplier for the selected criteria. The contribution of this study is not limited to the multi-criteria decision-making methods for the supplier selection problem in the construction sector, but also we make a comparison of the supplier selection decisions of two different sized companies having the same supplier pool, and we show the effect of company based criteria weights in supplier selection.

Keywords

: Fuzzy AHP, Fuzzy TOPSIS, Supply Chain

Farklı büyüklükteki İnşaat Firmaları için Bulanık AHP ve Bulanık TOPSIS Yöntemleriyle Tedarikçi Seçimi

Abstract

İnşaat firmaları için tedarikçi seçimi, uzun dönemli proje süreleri ve yüksek maliyetler açısından stratejik bir karardır. İnşaat sektöründe tedarikçi seçimi yapılırken pek çok kriter ve alternatif dikkate alınmalıdır. Alternatif tedarikçilerin sayısının fazla olmasının yanı sıra alternatif tedarikçileri kıyaslamak çin en iyi metodun seçimi de kritik bir karardır. Bu çalışmada, inşaat firmaları için en iyi tedarikçiyi seçme sürecinde farklı çok kriterli karar verme metotlarının etkinliğini kıyaslamaktayız. Bu nedenle, çok kriterli karar verme problemi çözümünde oldukça etkili olduğu bilinen bulanık AHP ve bulanık TOPSIS metotlarının entegrasyonunu incelemekteyiz. Bulanık AHP metodu ile kriter ağırlıklarını hesaplayıp bulanık TOPSIS metodu ile alternatifleri sıralamaktayız. İnşaat sektöründeki çok sayıda uzman ile yaptığımız görüşmeler sonucunda inşaat firmalarının tedarikçilerinin seçimi için 7 ana ve 24 alt kriter belirlenmiştir. Yaklaşımımızı iki inşaat firması için belirlenen kriterlere göre en iyi tedarikçiyi seçmek için bulanık AHP ve bulanık TOPSIS metotlarını bütünleşik olarak kullanıp test etmekteyiz. Bu çalışmanın literatüre katkısı sadece çok kriterli karar verme metotlarının inşaat sektöründe uygulanmasıyla sınırlı olmayıp aynı zamanda aynı tedarikçi havuzunu kullanan farklı ölçekteki iki inşaat firmasının tedarikçi seçimleri kıyaslanmakta ve firmalara göre belirlenen kriter ağırlıklarının tedarikçi seçimine etkisi gösterilmektedir.

Keywords

Bulanık AHP, Bulanık TOPSIS, Tedarik Zinciri

References

• 1. Lee, H.L. and C. Billington, Managing supply chain inventory: pitfalls and opportunities. Sloan management review, 1992. 33(3): p. 65.
• 2. Özdemir, A.İ., Tedarik zinciri yönetiminin gelişimi, süreçleri ve yararları. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2004. 23.
• 3. Chen, C.-T., C.-T. Lin, and S.-F. Huang, A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 2006. 102(2): p. 289-301.
• 4. Akman, G. and A. Alkan, Tedarik Zinciri Yönetiminde Bulanık AHP yöntemi kullanılarak tedarikçilerin performansının ölçülmesi: Otomotiv Yan Sanayiinde bir uygulama. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 2006. 5(9): p. 23-46.
• 5. Lima Junior, F.R., L. Osiro, and L.C.R. Carpinetti, A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 2014. 21: p. 194-209.
• 6. Chai, J., J.N.K. Liu, and E.W.T. Ngai, Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 2013. 40(10): p. 3872-3885.
• 7. Mani, V., R. Agrawal, and V. Sharma, Supplier selection using social sustainability: AHP based approach in India. International Strategic Management Review, 2014. 2(2): p. 98-112.
• 8. Deng, X., et al., Supplier selection using AHP methodology extended by D numbers. Expert Systems with Applications, 2014. 41(1): p. 156-167.
• 9. Mohammaditabar, D., S.H. Ghodsypour, and A. Hafezalkotob, A game theoretic analysis in capacity-constrained supplier-selection and cooperation by considering the total supply chain inventory costs. International Journal of Production Economics, 2016. 181: p. 87-97.
• 10. Hosseini, S. and K. Barker, A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 2016. 180: p. 68-87.
• 11. Lima-Junior, F.R. and L.C.R. Carpinetti, A multicriteria approach based on fuzzy QFD for choosing criteria for supplier selection. Computers & Industrial Engineering, 2016. 101: p. 269-285.
• 12. Wu, Y., et al., Supplier selection in nuclear power industry with extended VIKOR method under linguistic information. Applied Soft Computing, 2016. 48: p. 444-457.
• 13. Rezaei, J., et al., A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 2016. 135: p. 577-588.
• 14. Jain, V., et al., Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications, 2016. 29(7): p. 555-564.
• 15. Dobos, I. and G. Vörösmarty, Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA). International Journal of Production Economics, 2018.
• 16. Hamdan, S. and A. Cheaitou, Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach. Computers & Operations Research, 2017. 81: p. 282-304.
• 17. Ortiz-Barrios, M.A., et al., Strategic hybrid approach for selecting suppliers of high-density polyethylene. Journal of Multi-Criteria Decision Analysis, 2017. 24(5-6): p. 296-316.
• 18. Banaeian, N., et al., Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research, 2018. 89: p. 337-347.
• 19. Awasthi, A., K. Govindan, and S. Gold, Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 2018. 195: p. 106-117.
• 20. Kahraman, C., U. Cebeci, and Z. Ulukan, Multi‐criteria supplier selection using fuzzy AHP. Logistics Information Management, 2003. 16(6): p. 382-394.
• 21. Gold, S. and A. Awasthi, Sustainable global supplier selection extended towards sustainability risks from (1+ n) th tier suppliers using fuzzy AHP based approach. IFAC-PapersOnLine, 2015. 48(3): p. 966-971.
• 22. Bohner, C. and S. Minner, Supplier selection under failure risk, quantity and business volume discounts. Computers & Industrial Engineering, 2017. 104: p. 145-155.
• 23. Ahmad, M.T. and S. Mondal, Dynamic supplier selection model under two-echelon supply network. Expert Systems with Applications, 2016. 65: p. 255-270.
• 24. Sodenkamp, M.A., M. Tavana, and D. Di Caprio, Modeling synergies in multi-criteria supplier selection and order allocation: An application to commodity trading. European Journal of Operational Research, 2016. 254(3): p. 859-874.
• 25. Dickson, G.W., An analysis of vendor selection systems and decisions. Journal of Purchasing, 1966. 2(1): p. 5-17.
• 26. Weber, C.A., J.R. Current, and W. Benton, Vendor selection criteria and methods. European journal of operational research, 1991. 50(1): p. 2-18.
• 27. Choi, T.Y. and J.L. Hartley, An exploration of supplier selection practices across the supply chain. Journal of operations management, 1996. 14(4): p. 333-343.
• 28. Gunasekaran, A., C. Patel, and E. Tirtiroglu, Performance measures and metrics in a supply chain environment. International journal of operations & production Management, 2001.
• 29. Wu, M.-Y. and Y.-C. Weng, A study of supplier selection factors for high-tech industries in the supply chain. Total Quality Management, 2010. 21(4): p. 391-413.
• 30. Chang, B., C.-W. Chang, and C.-H. Wu, Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 2011. 38(3): p. 1850-1858.
• 31. Dweiri, F., et al., Designing an integrated AHP based decision support system for supplier selection in the automotive industry. Expert Systems with Applications, 2016. 62: p. 273-283.
• 32. Büyüközkan, G. and F. Göçer, Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied Soft Computing, 2017. 52: p. 1222-1238.
• 33. Ho, W., X. Xu, and P.K. Dey, Multi-criteria decision-making approaches for supplier evaluation and selection: A literature review. European Journal of operational research, 2010. 202(1): p. 16-24.
• 34. Chen, C.-T., Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 2000.
• 35. Kannan, D., A.B.L.d.S. Jabbour, and C.J.C. Jabbour, Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 2014. 233(2): p. 432-447.
• 36. Kilincci, O. and S.A. Onal, Fuzzy AHP approach for supplier selection in a washing machine company. Expert systems with Applications, 2011. 38(8): p. 9656-9664.
• 37. Stević, Ž., Z. Božičković, and B. Mićić, Optimization of the import of Chipboard-a case study. International Journal of Engineering, Business and Enterprise Applications, 2015. 14(1): p. 19-23.
• 38. Chaising, S. and P. Temdee. Application of a hybrid multi-criteria decision making approach for selecting of raw material supplier for Small and Medium Enterprises. in 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). 2017.
• 39. Gupta, H. and M.K. Barua, Supplier selection among SMEs on the basis of their green innovation ability using BWM and fuzzy TOPSIS. Journal of Cleaner Production, 2017. 152: p. 242-258.
• 40. Van Laarhoven, P.J. and W. Pedrycz, A fuzzy extension of Saaty's priority theory. Fuzzy sets and Systems, 1983. 11(1-3): p. 229-241.
• 41. Buckley, J.J., Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 1985. 17(3): p. 233-247.
• 42. Boender, C.G.E., J.G. de Graan, and F.A. Lootsma, Multi-criteria decision analysis with fuzzy pairwise comparisons. Fuzzy Sets and Systems, 1989. 29(2): p. 133-143.
• 43. Chang, D.-Y., Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 1996. 95(3): p. 649-655.
• 44. Cheng, C.-H., Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research, 1997. 96(2): p. 343-350.
• 45. Büyüközkan, G., C. Kahraman, and D. Ruan, A fuzzy multi-criteria decision approach for software development strategy selection. International Journal of General Systems, 2004. 33(2-3): p. 259-280.
• 46. AK, M.F., A Multi criteria decision making methodology based novel model for supplier selection. El-Cezeri , 7 (2) , 410-423 . DOI: 10.31202/ecjse.648856.