Supplier selection by integrated IFDEMATEL-IFTOPSIS Method: A case study of automotive supply industry

  • Ramazan Eyüp Gergin Gumushane University, Irfan Can Köse Vocational School, Transport Services Department, Turkey
  • İskender Peker Gumushane University, Faculty of Economics and Administrative Sciences, Business Administration Department, Turkey
  • A. Cansu Gök Kısa Hitit University, Faculty of Economics and Administrative Sciences, International Trade and Logistics Department, Turkey
Keywords: Automotive Supply Industry, IFDEMATEL, Supplier Selection, IFTOPSIS

Abstract

Selecting the best supplier emerges as a crucial subject for all sectors to achieve long term collaborations in supply chains. This study object to select the most suitable supplier for a company engage in activities in the automotive supply industry. For this purpose, a five-stage Intuitionistic Fuzzy Multi-Criteria Decision Making (IFMCDM) model is conducted. Firstly, decision criteria are defined by literature research and expert group opinions. Secondly, the importance weights of these criteria are obtained by IF Decision Making Trial and Evaluation Laboratory (IFDEMATEL). Followingly, the most suitable supplier is assessed by IF Technique for Order Preference by Similarity to Ideal Solution (IFTOPSIS). In the fourth stage, Sensitivity Analysis is utilized to analyze the effect of differentiation in criterion. Lastly, a comparative analysis is carried out. The results of the study has pointed that “Price” is the most important criterion in supplier selection and “Supplier 4” is the best alternative for this case. Main contribution of this study is to integrate IFDEMATEL-IFTOPSIS method for the first time in automotive supplier selection literature and propose a specific decision framework. In addition, proposed model is found robust and valid.

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References

Adalı, E.A., & Işık, A.T. (2017). The decision making approach based on SWARA and WASPAS methods for the supplier selection problem. International Review of Economics and Management, 5 (4), 56-77.

Aksoy, A., & Öztürk, N. (2011). Supplier selection and performance evaluation in Just-In-Time production environments. Expert Systems with Applications, 38, 6351-6359. DOI: https://doi.org/10.1016/j.eswa.2010.11.104

Aloini, D., Dulmin, R., Mininno, V., & Peer, A. (2014). IF-TOPSIS Based Decision Support System for Packaging Machine Selection, Expert System with Applications, 41(5), 2157-2165. DOI: https://doi.org/10.1016/j.eswa.2013.09.014

Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12 (6), 1668-1677. DOI: https://doi.org/10.1016/j.asoc.2012.01.023

Arabsheybani, A., Paydar, M. M., & Safaei, A. S. (2018). An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk. Journal of Cleaner Production, 190, 577-591. DOI: https://doi.org/10.1016/j.jclepro.2018.04.167

Atanassov, K. (1986). Intuitionistic Fuzzy Sets, Fuzzy Sets Systems, 20, 87–96. DOI: https://doi.org/10.1016/S0165-0114(86)80034-3

Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195, 106-117. DOI: https://doi.org/10.1016/j.ijpe.2017.10.013

Ayağ, Z., & Samanlioğlu, F. (2016). An intelligent approach to supplier evaluation in automotive sector. Journal of Intelligent Manufacturing, 27(4), 889-903. DOI: https://doi.org/10.1007/s10845-014-0922-7

Bai, C., Simonov, K.S., Hadi B.A., & Sarkis, J. (2019). Social sustainable supplier evaluation and selection: A group decision-support approach. International Journal of Production Research, 57 (22), 7046-7067.

Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I. E., & Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers and Operations Research, 89, 337–347. DOI: https://doi.org/10.1016/j.cor.2016.02.015

Behret, H. (2014). Group decision making with intuitionistic fuzzy preference relations. Knowledge-Based Systems. 70, 33–43. DOI: https://doi.org/10.1016/j.knosys.2014.04.001

Biswas, T. K., & Das, M. C. (2020). Selection of the barriers of supply chain management in Indian manufacturing sectors due to COVID-19 impacts. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 1-12.

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. DOI: https://doi.org/10.1016/j.eswa.2009.03.039

Boran, F. E., Boran, K., & Menlik, T. (2012). The Evaluation of Renewable Energy Technologies for Electricity Generation in Turkey Using Intuitionistic Fuzzy TOPSIS, Energy Sources, Part B: Economics, Planning, and Policy, 7(1), 81-90. DOI: https://doi.org/10.1080/15567240903047483

Butler, J., Jia, J., & Dyer, J. (1997). Simulation techniques for the sensitivity analysis of multi-criteria decision models. European Journal of Operational Research, 103 (3), 531-546. DOI: https://doi.org/10.1016/S0377-2217(96)00307-4

Büyüközkan, G., Güleryüz, S., & Karpak, B. (2017). A new combined IF-DEMATEL and IF-ANP approach for CRM partner evaluation. International Journal of Production Economics, 191, 194-206. doi: 10.1016/j.ijpe.2017.05.012. DOI: https://doi.org/10.1016/j.ijpe.2017.05.012

Chan, F.T.S. (2003). Interactive selection model for supplier selection process: An analytical hierarchy process approach. International Journal of Production Research, 41 (15), 3549-3579. DOI: https://doi.org/10.1080/0020754031000138358

Chan, F.T.S., & Chan, H.K. (2004). Development of the supplier selection model - A case study in the advanced technology industry. Journal of Engineering Manufacture, 218 (12), 1807-1824. DOI: https://doi.org/10.1177/095440540421801213

Chan, F.T.S., & Kumar N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35 (4), 417-431. DOI: https://doi.org/10.1016/j.omega.2005.08.004

Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38 (3), 1850-1858. DOI: https://doi.org/10.1016/j.eswa.2010.07.114

Chang, K. H., & Cheng, C. H. (2011). Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. Journal of Intelligent Manufacturing, 22, 113-129. DOI: https://doi.org/10.1007/s10845-009-0266-x

Dağdeviren, M., & Eraslan, E. (2008). Supplier selection using PROMETHEE sequencing method. Journal of the Faculty of Engineering and Architecture of Gazi University, 23 (1), 69-75.

Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., & Tap, M. B. M. (2014). Supplier selection: A fuzzy-ANP approach. Procedia Computer Science, 31, 691-700. DOI: https://doi.org/10.1016/j.procs.2014.05.317

Davras, G. M., & Karaatlı, M. (2014). Aplication of AHP and FAHP methods in supplier selection process at hotel businesses. Hacettepe University Journal of Economics and Administrative Sciences, 32(1), 87-112.

Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. DOI: https://doi.org/10.1016/j.eswa.2016.06.030

Eş, A., & Kocadağ, D. (2020). Supplier selection with Entropy based MAUT and VIKOR methods: A public institution case study. Manisa Celal Bayar University Journal of Social Sciences, 18, 265-280.

Fazlollahtabar, H., & Kazemitash, N. (2021). Green supplier selection based on the information system performance evaluation using the integrated Best-Worst Method. Facta Universitatis, Series: Mechanical Engineering, 1-16. 10.22190/FUME201125029F

Fazlollahtabar, H., Mahdavi, I., Ashoori, M.T., Kaviani, S., & Amiri, N. M. (2011). A multi-objective decision-making process of supplier selection and order allocation for multi-period scheduling in an electronic market. International Journal of Advanced Manufacturing Technology, 52, 1039-1052. DOI: https://doi.org/10.1007/s00170-010-2800-6

Fei L., Deng, Y., & Hu, Y. (2019). DS-VIKOR: A new multi-criteria decision-making method for supplier selection. International Journal of Fuzzy Systems, 21 (1), 157-175. DOI: https://doi.org/10.1007/s40815-018-0543-y

Feng, J., & Gong, Z. (2020). Integrated linguistic entropy weight method and multi-objective programming model for supplier selection and order allocation in a circular economy: A case study. Journal of Cleaner Production, 277, 1-16.

Gao, H., Ju, Y., Gonzalez, E.D.R.S., & Zhang, W. (2020). Green supplier selection in electronics manufacturing: An approach based on consensus decision making. Journal of Cleaner Production, 245, 1-17.

Ghadimi, P., & Heavey, C. (2014). Sustainable supplier selection in medical device industry: Toward sustainable manufacturing. Procedia CIRP, 15, 165-170. DOI: https://doi.org/10.1016/j.procir.2014.06.096

Golmohammadi, D. (2011). Neural network application for fuzzy multi-criteria decision making problems. International Journal of Computer Integrated Manufacturing, 131, 490-504. DOI: https://doi.org/10.1016/j.ijpe.2011.01.015

Gupta, S., Soni, U., & Kumar, G. (2019). Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry. Computers & Industrial Engineering, 136, 663-680.

Hadian, H., Chahardoli, S., Golmohammadi, A. M., & Mostafaeipour, A. (2020). A practical framework for supplier selection decisions with an application to the automotive sector. International Journal of Production Research, 58(10), 2997-3014.

Han, Y., & Deng, Y. (2018). An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors. Soft Computing, 22, 5073-5090.

Hashemi, S. H., Karimi, A., & Tavana, M. (2015). An integrated green supplier selection approach with analytic network process and improved grey relational analysis. International Journal of Production Economics, 159, 178-191. DOI: https://doi.org/10.1016/j.ijpe.2014.09.027

Hruska R., Prusa P., & Babic D. (2014). The use of AHP method for selection of supplier. Transport, 29 (2), 195-203. DOI: https://doi.org/10.3846/16484142.2014.930928

Huang, H.D., & Hu, M.H. (2013) Two-stage solution approach for supplier selection: A case study in a Taiwan automotive industry, International Journal of Computer Integrated Manufacturing, 26(3), 237-251, DOI: https://doi.org/10.1080/0951192X.2012.685762

Huang, S.H., & Keskar, H. (2007). Comprehensive and configurable metrics for supplier selection. International Journal of Production Economics, 105 (2), 510-523. DOI: https://doi.org/10.1016/j.ijpe.2006.04.020

ISO 500, (2019). http://www.iso500.org.tr/500-buyuk-sanayi-kurulusu/2019/ , Accessed 11.01.2021

Jadidi O., Hong, T.S., & Firouzi, F. (2009). TOPSIS extension for multi-objective supplier selection problem under price breaks. International Journal of Management Science and Engineering Management, 4 (3), 217-229.

Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Computing and Applications, 29(7), 555-564. DOI: https://doi.org/10.1007/s00521-016-2533-z

Jiang, P., Hu, Y. C., Yen, G. F., & Tsao, S. J. (2018). Green supplier selection for sustainable development of the automotive industry using grey decision‐making. Sustainable Development, 26(6), 890-903.

Junior, F.R.L., Osiro, L., & Carpinetti, L.C.R. (2014). A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194-209. DOI: https://doi.org/10.1016/j.asoc.2014.03.014

Kahraman, C., Cebeci, U., & Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management, 16 (6), 382-394. DOI: https://doi.org/10.1108/09576050310503367

Karabıçak, Ç., Özcan, B., & Akay, M. K. (2020). Supplier selection with fuzzy analytical hierarchy process in an automotive sub industry firm. Data Science, 3 (1), 26-32.

Karande, P., Zavadskas, E. K., & Chakraborty, S. (2016). A study of ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7, 399-422. DOI: https://doi.org/10.5267/j.ijiec.2016.1.001

Kasirian, M. N., & Yusuff, R. M. (2010). Application of AHP and ANP in supplier selection process-a case in an automotive company. International Journal of Management Science and Engineering Management, 5(2), 125-135. DOI: https://doi.org/10.1080/17509653.2010.10671100

Keramati, A., Ahmadizadeh-Tourzani, N., Nazari-Shirkouhi, S., Teshnizi, E. S., & Ashjari, B. (2014). A QFD-ANP methodology for supplier selection under perspective of requirements in automotive industry. International Journal of Productivity and Quality Management, 14(4), 492-517. DOI: https://doi.org/10.1504/IJPQM.2014.065560

Keshavarzfard, R., & Makui, A. 2015. An IF-DEMATEL-AHP based on Triangular Intuitionistic Fuzzy Numbers (TIFNs). Decision Science Letters. 4, 237- 246. DOI: https://doi.org/10.5267/j.dsl.2014.11.002

Khan, S. A., Dweiri, F., & Jain, V. (2016). Integrating analytical hierarchy process and quality function deployment in automotive supplier selection. International Journal of Business Excellence, 9(2), 156-177. DOI: https://doi.org/10.1504/IJBEX.2016.074851

Kokangul, A., & Susuz, Z. (2009). Integrated analytical hierarch process and mathematical programming to supplier selection problem with quantity discount. Applied Mathematical Modelling, 33(3), 1417-1429. DOI: https://doi.org/10.1016/j.apm.2008.01.021

KPMG, (2020). https://assets.kpmg/content/dam/kpmg/tr/pdf/2020/02/sektorel-bakis-2020-otomotiv.pdf , Accessed 12.01.2021.

Kumar, S., Kumar, S., & Barman, A. G. (2018). Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Computer Science, 133, 905-912.

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, 1161-1170. DOI: https://doi.org/10.1016/j.jclepro.2010.03.020

Lee, A.H.I., Kang, H.Y. , Hsu, C.F., & Hung, H.C. (2009). A green supplier selection model for high-tech industry. Expert Systems with Applications, 36 (4), 7917-7927. DOI: https://doi.org/10.1016/j.eswa.2008.11.052

Li, Y., Hu, Y., Zhang, X., Deng, Y., & Mahadevan, S. (2014). An evidential DEMATEL method to identify critical success factors in emergency management. Applied Soft Computing, 22, 504-510. DOI: https://doi.org/10.1016/j.asoc.2014.03.042

Liao, S.K., Chang, K.L., & Tseng, T. W. (2010). Optimal selection of program suppliers for tv companies using an analytic network process ANP approach. Asia-Pacific Journal of Operational Research, 27 (6), 753-767. DOI: https://doi.org/10.1142/S0217595910002983

Lin, C.T., Chen, C.B., & Ting, Y.C. (2011). An ERP model for supplier selection in electronics industry. Expert Systems with Applications, 38 (3), 1760-1765. DOI: https://doi.org/10.1016/j.eswa.2010.07.102

Mafakheri, F., Breton, M., & Ghoniem, A. (2011). Supplier selection-order allocation: A two stage multiple criteria dynamic programming approach. International Journal of Production Economics, 132 (1), 52-57. DOI: https://doi.org/10.1016/j.ijpe.2011.03.005

Magdalena, R. (2012). Supplier selection for food industry: A combination of Taguchi loss function and fuzzy analytical hierarchy process. Asian Journal of Technology Management, 5 (1), 13-22.

Manupati, V. K., Lakshmi, G. R., Ramkumar, M., & Varela, M. L. R. (2021). An Integrated Fuzzy MCDM Approach to Supplier Selection—Indian Automotive Industry Case. In Computational Management. Springer, Cham, 473-484.

Narasimhan, R., Talluri, S., & Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: An empirical examination. Journal of Supply Chain Management, 37 (2), 28-37. DOI: https://doi.org/10.1111/j.1745-493X.2001.tb00103.x

Öztürk, M., & Paksoy, T. (2020). A combined DEMATEL-QFD-AT2 BAHP approach for green supplier selection. Journal of the Faculty of Engineering and Architecture of Gazi University, 35 (4), 2023-2044.

Pilko, H., Mandžuka, S., & Barić, D. (2017). Urban Single-Lane Roundabouts: A New Analytical Approach Using Multi-Criteria and Simultaneous Multi-Objective Optimization of Geometry Design, Efficiency and Safety, Transportation Research Part C: Emerging Technologies, 80, 257-271. DOI: https://doi.org/10.1016/j.trc.2017.04.018

Pitchipoo, P., Venkumar, P., & Rajakarunakaran, S. (2015). Grey decision model for supplier evaluation and selection in process industry: A comparative perspective. International Journal of Advanced Manufacturing Technology, 76, 2059-2069. DOI: https://doi.org/10.1007/s00170-014-6406-2

Prakash, C., & Barua, M. K. (2016). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66-78. DOI: https://doi.org/10.1016/j.spc.2016.04.001

Qin, J., Liu, X., & Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval Type-2 fuzzy environment. European Journal of Operational Research, 258 (2), 626-638. DOI: https://doi.org/10.1016/j.ejor.2016.09.059

Rajesh, G., & Malliga, P. (2013). Supplier selection based on AHP QFD methodology. Procedia Engineering, 64, 1283-1292. DOI: https://doi.org/10.1016/j.proeng.2013.09.209

Rezaei, J., Fahim, P. B. M., & Tavasszy, L. (2014). Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications, 41 (18), 8165-8179. DOI: https://doi.org/10.1016/j.eswa.2014.07.005

Sarkis, J., & Talluri, S. (2002). A model for strategic supplier selection. Journal of Supply Chain Management, 38 (1), 18-28. DOI: https://doi.org/10.1111/j.1745-493X.2002.tb00117.x

Shahroudi, K., & Rouydel, H. (2012). Using a multi-criteria decision making approach ANP-TOPSIS to evaluate suppliers in Iran’s auto industry. International Journal of Applied Operational Research, 2 (2), 37-48.

Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167. DOI: https://doi.org/10.1016/j.eswa.2011.03.027

Stević, Ž., Vasiljević, M., Puška, A., Tanackov, I., Junevičius, R., & Vesković, S. (2019). Evaluation of suppliers under uncertainty: a multiphase approach based on fuzzy AHP and fuzzy EDAS. Transport, 34(1), 52-66.

Stevic, Z., Pamucar, D., Puska, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to compromise solution (MARCOS). Computers & Industrial Engineering, 140,1-15.

Suraraksa, J., & Shin, K. S. (2019). Comparative analysis of factors for supplier selection and monitoring: The case of the automotive industry in Thailand. Sustainability, 11(4), 981.

Tosun, Ö., & Akyüz, G. (2015). A fuzzy TODIM approach for the supplier selection problem. International Journal of Computational Intelligence Systems, 8 (2), 317-329. DOI: https://doi.org/10.1080/18756891.2015.1001954

Vahdani, B., Behzadi, S., & Mousavi S.M. (2015). Artificial intelligence model based on LS-SVM for third-party logistics provider selection. International Journal of Industrial Mathematics, 7 (4), 301-311.

Vasiljević, M., Fazlollahtabar, H., Stević, Ž., & Vesković, S. (2018). A rough multicriteria approach for evaluation of the supplier criteria in automotive industry. Decision Making: Applications in Management and Engineering, 1(1), 82-96. DOI: https://doi.org/10.31181/dmame180182v

Wan, S. P., Xu, G. L., & Dong, J. Y. (2017). Supplier selection using ANP and ELECTRE II in interval 2-Tuple linguistic environment. Information Sciences, 385–386, 19–38. DOI: https://doi.org/10.1016/j.ins.2016.12.032

Wei, G. (2018). Some Similarity Measures for Picture Fuzzy Sets and their Applications, Iranian Journal of Fuzzy Systems, 15(1), 77-89. DOI: https://doi.org/10.15388/Informatica.2018.160

Wu, M.Y., & Weng, Y.C. (2010). A study of supplier selection factors for high-tech industries in the supply chain. Total Quality Management & Business Excellence, 21 (4), 391-413. DOI: https://doi.org/10.1080/14783361003606662

Xia, W., & Wu, Z. (2007). Supplier selection with multiple criteria in volume discount environments. Omega, 35 (5), 494-504. DOI: https://doi.org/10.1016/j.omega.2005.09.002

Zadeh, L.A. (1965). Fuzzy sets. Journal of Information and Control, 8, 338-353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X

Zeydan, M., Çolpan, C., & Çobanoğlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications, 38(3), 2741-2751. DOI: https://doi.org/10.1016/j.eswa.2010.08.064

Zhong, L., & Yao, L. (2017). An ELECTRE I‐Based multi‐criteria group decision making method with interval type‐2 fuzzy numbers and its application to supplier selection. Applied Soft Computing, 57, 556-576. DOI: https://doi.org/10.1016/j.asoc.2017.04.001

Zimmer, K., Fröhling, M., Breun, P., & Schultmann, F. (2017). Assessing social risks of global supply chains: A quantitative analytical approach and its application to supplier selection in the German automotive ındustry. Journal of Cleaner Production,149, 96-109. DOI: https://doi.org/10.1016/j.jclepro.2017.02.041

Published
2021-12-21
How to Cite
Gergin, R. E., Peker, İskender, & Gök Kısa, A. C. (2021). Supplier selection by integrated IFDEMATEL-IFTOPSIS Method: A case study of automotive supply industry. Decision Making: Applications in Management and Engineering. https://doi.org/10.31181/dmame211221075g
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Regular articles