Sustainable Supplier Selection with Adaptive Network- Based Fuzzy Inference System (ANFİS)

Öz

Choosing the right sustainable supplier is a critical decision problem for businesses. Due to the increase in the number of competitors, the increase in evaluation criteria and the difficulty in defining evaluation criteria, the need for reliable methodologies that work with high accuracy increases as the complexity of the selection problem increases. This article discusses a new approach to the problem of sustainable supplier selection based on the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods. In the study, firstly the sustainable supplier selection criteria have been reduced to four criteria by the ANFIS method. For sustainable supplier performance evaluation, ANFIS model and ANN model were developed. Multiple regression analysis was performed to compare the performance success of both models. The model with the highest success rate was determined as ANFIS model. At the end of the study, the most suitable sustainable supplier selection was made with ANFIS model.

Anahtar Kelimeler

• Adaptive Network-Based Fuzzy Inference System (ANFIS)
• Artificial Neural Network (ANN)
• Supplier Selection
• Sustainability

Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFIS) ile Sürdürülebilir Tedarikçi Seçimi

Öz

Doğru sürdürülebilir tedarikçiyi seçmek işletmeler için kritik bir karar problemidir. Rekabetin artması, karar vermede dikkate alınacak değerlendirme kriterlerinin tanımlanmasının zorlaşması ve değerlendirme kriterlerinin artması nedeniyle, doğru tedarikçinin seçimi karmaşık hale gelmiş, bu nedenle yüksek doğrulukla çalışan güvenilir metodolojilere olan ihtiyaç da artmıştır. Bu makalede, Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFIS) ve Yapay Sinir Ağı (YSA) yöntemlerine dayalı olarak sürdürülebilir tedarikçi seçimi sorununa yeni bir yaklaşım getirilmesi amaçlanmıştır. Çalışmada öncelikle sürdürülebilir tedarikçi seçim kriterleri ANFIS yöntemiyle dört kritere indirilmiştir. Sürdürülebilir tedarikçi performans değerlendirme için, ANFIS modeli ve YSA modeli geliştirilmiştir. Her iki modelin performans başarısını karşılaştırmak maksadıyla çoklu regresyon analizi gerçekleştirilmiştir. En yüksek başarı oranına sahip model ANFIS modeli olarak belirlenmiştir. Çalışmanın sonunda ANFIS modeli ile en uygun sürdürülebilir tedarikçi seçimi yapılmıştır.

Anahtar Kelimeler

• Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi (ANFIS)
• Yapay Sinir Ağı (YSA)
• Tedarikçi Seçimi
• Sürdürülebilirlik

Kaynakça

1. Abbasi A. & Asgari M. S. (2014). Supplier selection using neuro-fuzzy inference system and fuzzy delphi. International Journal of Operations and Logistics Management, 3/4, 351- 371.
2. Abdulshahed, Ali (2015). The application of ANN and ANFIS prediction models for thermal error compensation on CNC machine tools. Doctoral thesis, University of Huddersfield.
3. Al-Hmouz, Shen H. & Member, S. (2012). Modeling and simulation of an adaptive neuro- fuzzy inference system (ANFIS) for mobile learning. IEEE Transactions on Learning Technologies, 5/3,226-23 Allison, P.D. (1999). Multiple regression. Pine Forge Press, Inc.
4. Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: a ranking model based on fuzzy inference system. Applied Soft Computing, 12, 1668- 1677.
5. Ataseven, B. (2013). Yapay sinir ağları ile öngörü modellemesi. Marmara Üniversitesi Sosyal Bilimler Enstitüsü Öneri Dergisi, 10/39, 101-115.
6. Awasthi, A., Govindan, K. & Gold, S. (2018). Multi-tier sustainable global supplier selection using a Fuzzy AHP-VIKOR based approach. International Journal of Production Ecnomics, 195, 106-117.
7. Aylı, E. & Ulucak, O. (2020). Yapay sinir ağları ve uyarlamalı sinirsel bulanık çıkarım sistemi ile francis tipi türbinler için verim tahminlenmesi. Isı Bilimi ve Tekniği Dergisi, 40/1, 87-97.
8. Bahadori, M., Hosseini, S.M., Teymourzadeh, E., Ravangard, R., Raadabadi, M. & Alimohammadzadeh, K. (2020). A Supplier selection model for hospitals using a combination of artificial neural network and fuzzy VIKOR, International Journal of Healthcare Management, 13/4, 286-294.

9. Bai, C., & Sarkis, J. (2010). Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics, 124/1, 252-264.
10. Barak, S. & Sadegh, S.S (2016). Forecasting energy consumption using ensemble ARIMA- ANFIS hybrid algoritm. International Journal of Electrical Power and Energy Systems,82,92-104.
11. Bayır, F. (2006). Yapay sinir ağları ve tahmin modellemesi üzerine bir uygulama. İstanbul Üniversitesi Sosyal Bilimler Enstitüsü İşletme Anabilim Dalı Sayısal Yöntemler Bilim Dalı, Yüksek Lisans Tezi.
12. César, Manuel Braz and Barros, Rui Carneiro (2016). "ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers" Open Engineering, vol. 6, no. 1.
13. Chen, R. J. C., Bloomfield, P. & Fu, J.S. (2003). An evaluation of alternative forecasting methods to recreation visitation. Journal of Leisure Research, 35/4,441-454.
14. Chowdary, B. V. (2007). Back‐propagation artificial neural network approach for machining centre selection. Journal of Manufacturing Technology Management, 18/3, 315–332.
15. Çuhadar, M., Güngör, İ. & Göksu, A. (2009). Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi, Antalya iline yönelik bir uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14/1, 99-114.
16. Durbin, J. & Watson, G.S (1971). Testing for serial correlation in least squares regression III Biometrika, 58/1-1-19.
17. Ecer, F. (2021). Sürdürülebilir tedarikçi seçimi: FUCOM sübjektif ağırlıklandırma yöntemi temelli maırca yaklaşımı. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 8(1), 26-48.
18. Fallahpour, A., Olugu, E. U., Musa, S. N., Wong, K. Y. & Nori S. (2017). A decision support model for sustainable supplier selection in sustainable supply chain management. Computers and Industrial Engineering, 105, 391-410.
19. Field, A. (2013). Discovering statistics using IBM SPSS statitics. SAGE Publications, Inc
20. Gegovska, T., Koker, R. & Cakar, T. (2020). Green supplier selection using fuzzy multiple-criteria decision-making methods and artificial neural networks, Computational Intelligence and Neuroscience, 20,26.
21. Genç, T. & Paksoy, S. (2016). Prediction of exchange rafe of R/USD by using lagged models of ANN. International Journal of Decision Science, 7/2,49-56.
22. Ghadimi, P., & Heavey, C. (2014). Sustainable supplier selection in medical device industry: toward sustainable manufacturing. Procedia CIRP, 15, 165-170.
23. Golmohammadi, D., Creese, C. R., Valian, H., Kolassa, J. (2009). Supplier selection based on a neural network model using genetic algorithm. IEEE Trans Neural Network, 20/9, 1504- 1519.
24. Guarnieri, P. & Trojan, F. (2019). Decision making on supplier selection based on social, ethical and environmental criteria: a study in the textile industry. Resources, Conversation and Recycling, 141, 347-361.
25. Gujarati, D. N. (2001). Temel Ekonometri. Çev. Ümit Şenesen ve Göktürk Şenesen, 2. Baskı, Literatür Yayıncılık, .
26. Güneri, A. F., Ertay T. & Yücel A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert System with Applications, 38, 14907- 14917.
27. Hair, J. F., Hult, G.T. & Sarstedt, M. (2014). A primer on partial least square structural equation modelin (PLS-SEM). SAGE Publications, Inc.
28. Hamdan, I.K.A., Aziguli, W., Zhang, D. et al.(2023). Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS. Int J Syst Assur Eng Manag 14 (Suppl. 1), 549–568.
29. Haykin, S. (1999). Neural network a comprehensive foundation. Pearson Prentice Hall.
30. Hoseini, S.A., Fallahpour, A., Wong, K.Y., Mahdiyar, A., Saberi, M. & Durdyev, S. (2021). Sustainable supplier selection in construction industry through hybrid fuzzy-based approaches. Sustainability, 13, 1413.
31. Hu, Clark (2002). Advanced tourism demand forecasting: ANN and box-jenkins modelling, Purdue University, MI, USA.
32. Jang, J-S. R. & Gulley, N. (1997). MATLAB fuzzy logic toolbox. fuzzy logic toolbox user’s guide 1, Copyright 1984, The MathWorks, Inc.
33. Jang, Jyh-Shing R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23/3, 665:685.
34. Jang, Jyh-Shing R. (1996). Input selection for ANFIS learning. IEEE International Conferance on Fuzzy Systems, Proceedings of IEEE 5th International Fuzzy System.
35. Ji, L. & Gallo, K. (2006). An agreement coefficient for image comparison. Photogrammetric Engineering and Remote Sensing, 72/7,823:833.
36. Kannan, D. (2017). Role of multiple stakeholders and the critical success factor theory for the sustainable supplier selection process, International Journal of Production Economics, 195, 391-418.
37. Karahan, M. (2015). Turizm talebinin yapay sinir ağları yöntemiyle tahmin edilmesi. Süleyman Demirel Üniversitesi, İktisadi ve İdari Bilimler Fakültesi Dergisi, 20/2, 195-209.
38. Khalili-Damghani, K., Hosein, D. & Sadi-Nezhad, S. (2013). A two-stage approach based on ANFIS and fuzzy goal programming for supplier selection. Int. J. of Applied Decision Sciences, 6. ied Decision Sciences.
39. Kılıç, S. B., Paksoy S. & Genç T. (2014). Forecasting the direction of BİST 100 returns with artificial neural network models. International Journal of Latest Trends in Finance and Economic Sciences, 4, 759-765.
40. Kul, S. (2014). İstatistik sonuçlarının yorumu: P değeri güven aralığı nedir. Türk Toraks Derneği, 11-13.
41. Kuo, R. J., Wang, Y. C. & Tien, F.C. (2010). Integration of artificial neural network and MADA methods for green supplier selection. Journal of Cleaner Production, 18/12, 1161-1170.
42. Lin, R-H., Chuang, C-L., Liou, J J-H. & Wu, G-D (2009). An integrated method for finding key suppliers in SCM. Expert Systems with Applications, 36, 6461-6465.
43. Lin, T. C. & Lee C. S. (1991). Neural network based fuzzy logic control and decision system. IEEE Transactions on Computers,40/12,1320-1336.
44. Luo, Xinxing, Wu, C., Rosenberg, D. & Barnes, D. (2009). Supplier selection in agile supply chains: an information-processing model and an illustration. Journal of Purchasing and Supply Management, 15/4, 249-262.
45. Luthra, S., Govindan, K., Kannan, D., Mangla, S. K. & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686-1698.
46. May, R. J., Maier H. R. & Dandy G. C. (2010). Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Networks, 23/2, 283–294.
47. Nikolaou, I. E., Evangelinos, K. I., & Allan, S. (2013). A reverse logistics social responsibility evaluation framework based on the triple bottom line approach. Journal of Cleaner Production, 56, 173-184.
48. Okwu, M. O. & Tartibu, L. K. (2020). Sustainable supplier selection in the retail industry: a TOPSIS- and ANFIS- based evaluating methodology. International Journal of Engineering Business Management, 12/1, 1-14.
49. Paksoy, S. (2017). Predicting gold returns by hybrid markov chain process. AİBÜ Sosyal Bilimler Enstitüsü Dergisi, 1/17, 29-49.
50. Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com
51. Sarkis, J. & Talluri, S., 2002. A Model for strategic supplier selection. Journal of Supply Chain Management 38/1, 18–28.
52. Septiyana, D., Rahman, M. A., Ariff, T. F. M., Sukindar, N. A., & Adesta, E. Y. T. (2023). Enhancing sustainability index parameter using ANFIS computational intelligencemodel. IIUM Engineering Journal, 24(2), 258–268.
53. Suparta, W. & Alhasa, K.M., (2016). Modeling of tropospheric delays using ANFIS. Springer Briefs in Meterology, Springer International Publishing.
54. Toprak, B., Katmiş, Ş. Z., Bektaş, D., Çakmak, D., vd. (2024). Otomotiv sektöründe sürdürülebilir tedarik zinciri yönetiminin bibliyometrik analiz ile incelenmesi. Sürdürülebilir Çevre Dergisi, 4(1), 1-18.
55. Tortum, A., Yayla, N. & Gökdağ, M. (2009). The modeling of mode choices of intercity freight transportation with the artifical neural networks and adaptice neuro-fuzzy inference system. Expert System with Applications, 36, 6199-6217.
56. Unver, Ö. (1996). Uygulamalı istatistik yöntemler. İkinci Baskı, Siyasal Kitabevi.
57. Vupa, Ö. & Gürünlü Alma, Ö. (2008). Doğrusal regresyon çözümlemesinde çoklu bağlantı probleminin sapan değer içeren küçük örneklemlerde bir simülasyon çalışması ile saptanması ve sonuçları. Sabancı Üniversitesi Fen Edebiyat Fakültesi Dergisi, 32, 41-51.
58. Wang, J. (2021). Supplier evaluation and selection based on bp neural network. Application of Intelligent Systems in Multi-modal Information Analytics, 627-634.
59. Wang, Y. & Wu, C-C. (2004), Current understanding of tropical cyclone structure and intensity changes - a review. Meteor. Atmos. Phys., 87, 257–278.
60. Wayan, S. & Alhasa, K.M (2016). Modeling of tropospheric delays using. Springer.
61. Weber, C. A., Current, J. R., & Benton, W. (1991). Vendor selection criteria and methods. European Journal of Operational Research, 50/1, 2-18.
62. Wei, S., Zhang, J. & Li, Z. (1997). A Supplier–selecting system using a neural network. IEEE International Conference on Intelligent Processing Systems, 468- 471.
63. Wu, D. (2009). Supplier Selection: A Hybrid model using DEA, decision tree and neural network, Expert Systems with Applications, 36/5, 9105-9112.
64. Yücel, A. (2010). Tedarikçi seçimi probleminde bütünleşik sinirsel bulanık mantık yaklaşımı. İstanbul Üniversitesi Endüstri Mühendisliği Bölümü, Doktora Tezi.
65. Yüzer, Ali F. (2004). İstatistik. Anadolu Üniversitesi Yayını. Yayın No:1448, Eskişehir.
66. Zhang, J.; Yang, D., Li, Q., Lev, B. & Ma, Y. (2021). Research on sustainable supplier selection based on the rough DEMATEL and FVIKOR methods. Sustainability, 13, 88.