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The International Conference on Artificial Intelligence and Applied Mathematics in Engineering

ICAIAME 2019: Artificial Intelligence and Applied Mathematics in Engineering Problems pp 226–251Cite as

A Novel Hybrid Model for Vendor Selection in a Supply Chain by Using Artificial Intelligence Techniques Case Study: Petroleum Companies

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 43))

Abstract

Oil is an important strategic material which is associated with vital and major components of national security and economy of each country. In this context, supplier selection in a supply chain of oil companies has a direct effect on immunization and optimization of the production cycle, refining and distribution of petroleum, gas and petroleum products in oil producer and exporter countries. Therefore, creating and owning a purposeful and intelligent process for evaluation and analysis of suppliers is one of the inevitable needs and concerns of these countries. Many of the methods which are currently widely applied in the management of oil companies utilize traditional supplier selection methods which are unfortunately limited to individual and subjective evaluation in weighing decision maker’s criteria, incorrect assessment rules, and inefficient decision-making methods. In this paper, with an in-depth look at the supplier selection in supply chain management of oil companies project, a novel model has been proposed based on an object-oriented framework. This model which finally leads to optimal selection and ranking of suppliers, reducing the time and cost in the selection process and also reduced human errors by using data mining techniques and neural networks in the reasoning method cycle based on the case. The proposed model was implemented on data bank information of the Oil Company. Finally, the results of the proposed model are compared with several other models. Results show that using reduced errors, improved accuracy, and efficiency the proposed model has been able to have a good performance in the supplier selection.

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References

  1. De Boer, L., Labro, E., Morlacchi, P.: A review of methods supporting supplier selection. Eur. J. Purchasing Supply Manage. 7(2), 75–89 (2001)

    Article  Google Scholar 

  2. Hong, G.H., Ha, S.H.: Evaluating supply partner’s capability for seasonal products using machine learning techniques. Comput. Ind. Eng. 54(4), 721–736 (2008)

    Article  Google Scholar 

  3. Timmerman, E.: An approach to vendor performance evaluation. IEEE Eng. Manage. Rev. 15(3), 14–20 (1987)

    Article  Google Scholar 

  4. Weber, C.A., Current, J.R., Desai, A.: Non-cooperative negotiation strategies for vendor selection. Eur. J. Oper. Res. 108(1), 208–223 (1998)

    Article  MATH  Google Scholar 

  5. Weber, C.A., Desai, A.: Determination of paths to vendor market efficiency using parallel coordinates representation: a negotiation tool for buyers. Eur. J. Oper. Res. 90(1), 142–155 (1996)

    Article  MATH  Google Scholar 

  6. Weber, C.A., Current, J., Desai, A.: An optimization approach to determining the number of vendors to employ. Supply Chain Manage. Int. J. 5(2), 90–98 (2000)

    Article  Google Scholar 

  7. Liu, J., Ding, F.Y., Lall, V.: Using data envelopment analysis to compare suppliers for supplier selection and performance improvement. Supply Chain Manage. Int. J. 5(3), 143–150 (2000)

    Article  Google Scholar 

  8. Parthiban, P., Zubar, H.A., Katakar, P.: Vendor selection problem: a multi-criteria approach based on strategic decisions. Int. J. Prod. Res. 51(5), 1535–1548 (2013)

    Article  Google Scholar 

  9. Holt, G.D.: Which contractor selection methodology? Int. J. Project Manage. 16(3), 153–164 (1998)

    Article  Google Scholar 

  10. Lin, R.H., Chuang, C.L., Liou, J.J., Wu, G.D.: An integrated method for finding key suppliers in SCM. Expert Syst. Appl. 36(3), 6461–6465 (2009)

    Article  Google Scholar 

  11. Humphreys, P., McIvor, R., Chan, F.: Using case-based reasoning to evaluate supplier environmental management performance. Expert Syst. Appl. 25(2), 141–153 (2003)

    Article  Google Scholar 

  12. Choy, K.L., Lee, W.B., Lau, H., Lu, D., Lo, V.: Design of an intelligent supplier relationship management system for new product development. Int. J. Comput. Integr. Manuf. 17(8), 692–715 (2004)

    Article  Google Scholar 

  13. Choy, K.L., Lee, W., Lo, V.: Design of a case based intelligent supplier relationship management system—the integration of supplier rating system and product coding system. Expert Syst. Appl. 25(1), 87–100 (2003)

    Article  Google Scholar 

  14. Lee, E.K., Ha, S., Kim, S.K.: Supplier selection and management system considering relationships in supply chain management. IEEE Trans. Eng. Manage. 48(3), 307–318 (2001)

    Article  Google Scholar 

  15. Liu, Y., Yu, F., Su, S.Y., Lam, H.: A cost–benefit evaluation server for decision support in e-business. Decis. Support Syst. 36(1), 81–97 (2003)

    Article  Google Scholar 

  16. Wang, G., Huang, S.H., Dismukes, J.P.: Product-driven supply chain selection using integrated multi-criteria decision-making methodology. Int. J. Prod. Econ. 91(1), 1–15 (2004)

    Article  Google Scholar 

  17. Çebi, F., Bayraktar, D.: An integrated approach for supplier selection. Logistics Inf. Manage. 16(6), 395–400 (2003)

    Article  Google Scholar 

  18. Kilic, H.S.: An integrated approach for supplier selection in multi-item/multi-supplier environment. Appl. Math. Model. 37(14–15), 7752–7763 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  19. Kumar, S.K., Tiwari, M.K., Babiceanu, R.F.: Minimisation of supply chain cost with embedded risk using computational intelligence approaches. Int. J. Prod. Res. 48(13), 3717–3739 (2010)

    Article  MATH  Google Scholar 

  20. Kumar, M., Vrat, P., Shankar, R.: A fuzzy goal programming approach for vendor selection problem in a supply chain. Comput. Ind. Eng. 46(1), 69–85 (2004)

    Article  Google Scholar 

  21. Kumar, M., Vrat, P., Shankar, R.: A fuzzy programming approach for vendor selection problem in a supply chain. Int. J. Prod. Econ. 101(2), 273–285 (2006)

    Article  Google Scholar 

  22. Kumar, A., Jain, V., Kumar, S.: A comprehensive environment friendly approach for supplier selection. Omega 42(1), 109–123 (2014)

    Article  MathSciNet  Google Scholar 

  23. Weber, C.A., Current, J.R., Benton, W.C.: Vendor selection criteria and methods. Eur. J. Oper. Res. 50(1), 2–18 (1991)

    Article  MATH  Google Scholar 

  24. Das, A., Narasimhan, R., Talluri, S.: Supplier integration—finding an optimal configuration. J. Oper. Manage. 24(5), 563–582 (2006)

    Article  Google Scholar 

  25. Chai, J., Liu, J.N., Ngai, E.W.: Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst. Appl. 40(10), 3872–3885 (2013)

    Article  Google Scholar 

  26. Saaty, T.L.: What is the analytic hierarchy process? Springer (1988)

    Google Scholar 

  27. De Boer, L., van der Wegen, L., Telgen, J.: Outranking methods in support of supplier selection. Eur. J. Purchasing Supply Manage. 4(2–3), 109–118 (1998)

    Article  Google Scholar 

  28. Roodhooft, F., Konings, J.: Vendor selection and evaluation an activity based costing approach. Eur. J. Oper. Res. 96(1), 97–102 (1997)

    Article  MATH  Google Scholar 

  29. Monczka, R.M., Trecha, S.J.: Cost-based supplier performance evaluation. J. Purchasing Mater. Manag. 24(1), 2–7 (1988)

    Google Scholar 

  30. Degraeve, Z., Labro, E., Roodhooft, F.: An evaluation of vendor selection models from a total cost of ownership perspective. Eur. J. Oper. Res. 125(1), 34–58 (2000)

    Article  MATH  Google Scholar 

  31. Gaballa, A.A.: Minimum cost allocation of tenders. J. Oper. Res. Soc. 25(3), 389–398 (1974)

    Article  MATH  Google Scholar 

  32. Ghodsypour, S.H., O’brien, C.: The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. Int. J. Prod. Econ. 73(1), 15–27 (2001)

    Article  Google Scholar 

  33. Weber, C.A., Current, J.R.: A multiobjective approach to vendor selection. Eur. J. Oper. Res. 68(2), 173–184 (1993)

    Article  MATH  Google Scholar 

  34. Zhao, K., Yu, X.: A case based reasoning approach on supplier selection in petroleum enterprises. Expert Syst. Appl. 38(6), 6839–6847 (2011)

    Article  Google Scholar 

  35. Li, H., Sun, J.: Case-based reasoning ensemble and business application: a computational approach from multiple case representations driven by randomness. Expert Syst. Appl. 39(3), 3298–3310 (2012)

    Article  Google Scholar 

  36. Schank, R.C.: Dynamic Memory: A Theory of Reminding and Learning in Computers and People, vol. 240. University Press, Cambridge (1982)

    Google Scholar 

  37. Aamodt, A., Sandtorv, H.A., Winnem, O.M.: Combining case based reasoning and data mining-a way of revealing and reusing rams experience. In: Safety and Reliability; Proceedings of ESREL, vol. 98, pp. 16–19 (1998)

    Google Scholar 

  38. Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S.: The impact of supply chain management practices on competitive advantage and organizational performance. Omega 34(2), 107–124 (2006)

    Article  Google Scholar 

  39. Huang, G., Li, X., He, J., Li, X.: Data mining via minimal spanning tree clustering for prolonging lifetime of wireless sensor networks. Int. J. Inf. Technol. Decis. Making 6(02), 235–251 (2007)

    Article  MATH  Google Scholar 

  40. Kim, K.S., Han, I.: The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases. Expert Syst. Appl. 21(3), 147–156 (2001)

    Article  MathSciNet  Google Scholar 

  41. Mosaddar, D., Shojaie, A.A.: A data mining model to identify inefficient maintenance activities. Int. J. Syst. Assur. Eng. Manage. 4(2), 182–192 (2013)

    Article  Google Scholar 

  42. Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehousing 5(4), 13–22 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics Institute, Gazi University, Ankara, Turkey

    Mohsen Jafari Nodeh

  2. Department of Management Information Systems, Karadeniz Technical University, Trabzon, Turkey

    M. Hanefi Calp

  3. Department of Industrial Design Engineering, Gazi University, Ankara, Turkey

    İsmail Şahin

Authors
  1. Mohsen Jafari Nodeh
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  2. M. Hanefi Calp
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  3. İsmail Şahin
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Corresponding author

Correspondence to M. Hanefi Calp .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Suleyman Demirel University, Isparta, Isparta, Turkey

    Utku Kose

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Nodeh, M.J., Calp, M.H., Şahin, İ. (2020). A Novel Hybrid Model for Vendor Selection in a Supply Chain by Using Artificial Intelligence Techniques Case Study: Petroleum Companies. In: Hemanth, D., Kose, U. (eds) Artificial Intelligence and Applied Mathematics in Engineering Problems. ICAIAME 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 43. Springer, Cham. https://doi.org/10.1007/978-3-030-36178-5_19

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