Identifying and Evaluating Effective Factors in Green Supplier Selection using Association Rules Analysis

Document Type : Original Manuscript

Authors

Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

10.22094/joie.2020.575685.1589

Abstract

Nowadays companies measure suppliers on the basis of a variety of factors and criteria that affect the supplier's selection issue. This paper intended to identify the key effective criteria for selection of green suppliers through an efficient algorithm callediterative process mining or i-PM. Green data were collected first by reviewing the previous studies to identify various environmental criteria. Then, the suppliers were evaluated and ranked on the basis of those criteria. The score table derived for the green criteria was one of the inputs to the algorithm. Moreover, membership functions and minimum support values ​​were specified for each criterion as another input to the algorithm. The supplier ranking index was also obtained based on the score assigned to supplier's performance. Then, the hidden relationships between data were discovered and association rules were achieved and analyzed to identify the most important green criterion for selecting green suppliers.

Graphical Abstract

Identifying and Evaluating Effective Factors in Green Supplier Selection using Association Rules Analysis

Highlights

  • This paper intended to identify the key effective criteria for selection of green suppliers.
  • An efficient data mining algorithm callediterative process mining or i-PM. The i-PM was adopted in this research.
  • The hidden relationships between the data of green criteria and supplier ranking index are investigated.
  • The criteria with the most significant correlation with the supplier's ranking index were identified as the most important green criterion for selecting green suppliers.

Keywords


Adibi, M. A., & Shahrabi, J. (2015). Online anomaly detection based on support vector clustering. International Journal of Computational Intelligence Systems, 8(4), 735-746.
Alinezhad, A., Adibi, M. A., & Tohidi, A. (2019). Classification of Streaming Fuzzy DEA Using Self-Organizing Map. Journal of Optimization in Industrial Engineering, 12(1), 53-61.
Chang, B., & Hung, H. F. (2010). A study of using RST to create the supplier selection model and decision-making rules. Expert Systems with Applications, 37(12), 8284-8295.
Chen, C. H., Lan, G. C., Hong, T. P., & Lin, S. B. (2016). Mining fuzzy temporal association rules by item lifespans. Applied Soft Computing, 41, 265-274.
Chen, L. Y., & Wang, T. C. (2009). Optimizing partners’ choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR. International Journal of Production Economics, 120(1), 233-242.
Delgado, M., SáNchez, D., MartıN-Bautista, M. J., & Vila, M. A. (2001). Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine, 21(1-3), 241-245.
Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3), 6978-6985.
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.
Haeri, S. A. S., & Rezaei, J. (2019). A grey-based green supplier selection model for uncertain environments. Journal of cleaner production, 221, 768-784.
Hong, T. P., & Chen, J. B. (1999). Finding relevant attributes and membership functions. Fuzzy Sets and systems, 103(3), 389-404.
Hong, T. P., Lin, K. Y., & Wang, S. L. (2003). Fuzzy data mining for interesting generalized association rules. Fuzzy sets and systems, 138(2), 255-269.
Jain, R., Singh, A. R., Yadav, H. C., & Mishra, P. K. (2014). Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection. Journal of Intelligent Manufacturing, 25(1), 165-175.
Kamsu-Foguem, B., Rigal, F., & Mauget, F. (2013). Mining association rules for the quality improvement of the production process. Expert systems with applications, 40(4), 1034-1045.
Kumari, R., & Mishra, A. R. (2020). Multi-criteria COPRAS Method Based on Parametric Measures for Intuitionistic Fuzzy Sets: Application of Green Supplier Selection. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 1-18.
Lau, H. C., Ho, G. T., Chu, K. F., Ho, W., & Lee, C. K. (2009). Development of an intelligent quality management system using fuzzy association rules. Expert Systems with Applications, 36(2), 1801-1815.
Lau, H. C., Ho, G. T., Zhao, Y., & Chung, N. S. H. (2009). Development of a process mining system for supporting knowledge discovery in a supply chain network. International Journal of Production Economics, 122(1), 176-187.
Lee, A. H., 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.
Lin, R. H., Chuang, C. L., Liou, J. J., & Wu, G. D. (2009). An integrated method for finding key suppliers in SCM. Expert Systems with Applications, 36(3), 6461-6465.
Liu, B., Hsu, W., & Ma, Y. (1999, August). Mining association rules with multiple minimum supports. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 337-341). ACM.
Manohar, H. L., & Kumar, R. G. (2020). A neural networks model for green supplier selection. International Journal of Services and Operations Management, 35(1), 1-11.
Noci, G. (1997). Designing green vendor rating systems for the assessment of a supplier's environmental performance. European Journal of Purchasing & Supply Management, 3(2), 103-114.
Prosman, E. J., & Sacchi, R. (2018). New environmental supplier selection criteria for circular supply chains: Lessons from a consequential LCA study on waste recovery. Journal of Cleaner Production, 172, 2782-2792.
Sahebjamnia, N., Goodarzian, F., & Hajiaghaei-Keshteli, M. (2020). Optimization of Multi-period Three-echelon Citrus Supply Chain Problem. Journal of Optimization in Industrial Engineering, 13(1), 39-53.
Salleb-Aouissi, A., Vrain, C., & Nortet, C. (2007, January). QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules. In IJCAI (Vol. 7, pp. 1035-1040).
Tsay, Y. J., & Chiang, J. Y. (2005). CBAR: an efficient method for mining association rules. Knowledge-Based Systems, 18(2-3), 99-105.
Tseng, M. L., & Chiu, A. S. (2013). Evaluating firm's green supply chain management in linguistic preferences. Journal of cleaner production, 40, 22-31.
Tuzkaya, G., Ozgen, A., Ozgen, D., & Tuzkaya, U. R. (2009). Environmental performance evaluation of suppliers: A hybrid fuzzy multi-criteria decision approach. International Journal of Environmental Science & Technology, 6(3), 477-490.
Wang, L., Meng, J., Xu, P., & Peng, K. (2018). Mining temporal association rules with frequent item sets tree. Applied Soft Computing, 62, 817-829.
Yeh, W. C., & Chuang, M. C. (2011). Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Systems with applications, 38(4), 4244-4253.
Yuan, X. (2017, March). An improved Apriori algorithm for mining association rules. In AIP conference proceedings (Vol. 1820, No. 1, p. 080005). AIP Publishing LLC.