Customer Clustering by a Novel Combined TOPSIS-Fuzzy and K-Means Approach To Design an Efficient Customer Relationship System: A Real-life Case Study in the Copper Industry
محورهای موضوعی : Fuzzy Optimization and Modeling Journal
Hossein Mohammadi Dolat-Abadi
1
(
Farabi College, Department of Industrial Engineering, University of Tehran
)
Amirsadra Sadat
2
(
industrial engineering, Farabi College, Department of Industrial Engineering, University of Tehran, Iran
)
کلید واژه: K-means Method, Fuzzy TOPSIS, Customer Eelationship System, Metals Supply Chain,
چکیده مقاله :
The contemporary application of analytical approaches, including clustering, classification, and ranking, in customer analysis empowers supply chain members to effectively align their organizational and commercial objectives. This study introduces a clustering model designed to scrutinize customers within a metal supply chain, defining optimal strategies tailored to each cluster. These strategies contribute to the implementation of a comprehensive customer relationship system, fostering competitiveness in the market. To achieve this goal, the initial step involves the review, cleaning, and normalization of the company’s customer data. These data comprise scores in eleven criteria aspects for each customer, encompassing aspects such as good account status, absence of bounced checks, timely payment, legal status, presence of personal or governmental support, reputation, brand value, internal business managers' comments, each customer's share of total purchases, and production capacity. Expert-derived weights are assigned to these criteria. Subsequently, the k-means clustering technique is employed and validated through the silhouette score. Post clustering, the Fuzzy TOPSIS method is utilized to rank the clusters, determining their respective positions. Finally, strategies and approaches for each cluster are formulated, considering factors such as monetary credit allocation, discount rates, and trust levels in product sales. Overall, this research pioneers a comprehensive framework that goes beyond traditional models, offering a strategic roadmap for supply chain members to navigate a competitive market, standardize communication, and foster long-term relationships with customers.
The contemporary application of analytical approaches, including clustering, classification, and ranking, in customer analysis empowers supply chain members to effectively align their organizational and commercial objectives. This study introduces a clustering model designed to scrutinize customers within a metal supply chain, defining optimal strategies tailored to each cluster. These strategies contribute to the implementation of a comprehensive customer relationship system, fostering competitiveness in the market. To achieve this goal, the initial step involves the review, cleaning, and normalization of the company’s customer data. These data comprise scores in eleven criteria aspects for each customer, encompassing aspects such as good account status, absence of bounced checks, timely payment, legal status, presence of personal or governmental support, reputation, brand value, internal business managers' comments, each customer's share of total purchases, and production capacity. Expert-derived weights are assigned to these criteria. Subsequently, the k-means clustering technique is employed and validated through the silhouette score. Post clustering, the Fuzzy TOPSIS method is utilized to rank the clusters, determining their respective positions. Finally, strategies and approaches for each cluster are formulated, considering factors such as monetary credit allocation, discount rates, and trust levels in product sales. Overall, this research pioneers a comprehensive framework that goes beyond traditional models, offering a strategic roadmap for supply chain members to navigate a competitive market, standardize communication, and foster long-term relationships with customers.
1. Afsar, A., Houshdar Mahjoub, R., & Minaie Bidgoli, B. (2014). Customer credit clustering for presenting appropriate facilities. Management Researches in Iran, 17(4), 1-24.
2. Andayani, U., Efendi, S., Siregar, N., & Syahputra, M. (2021). Determination System for House Improvement Recipients In Serdang Bedagai By Using Clustering K-Means Method And Višekriterijumsko Kompromisno Rangiranje (Vikor). Paper presented at the Journal of Physics: Conference Series.
3. Ansari, A., & Riasi, A. J. I. J. o. B. (2016). Customer clustering using a combination of fuzzy c-means and genetic algorithms. International Journal of Business Management, 11(7), 59-66.
4. Bottou, L., & Bengio, Y. (1994). Convergence properties of the k-means algorithms. Advances in neural information processing systems, 7.
5. Gocer, F., & Sener, N. (2022). Spherical fuzzy extension of AHP‐ARAS methods integrated with modified k‐means clustering for logistics hub location problem. Expert Systems, 39(2), e12886.
6. Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences 622, 178-210.
7. Jyothirmai, B., Rajendra, P., Kumari, D. A., & Aparna, N. High performance of Cluster-Based Strategy for reducing Delays in Wireless Sensor Networks. Journal of the Maharaja Sayajirao University of Baroda.
8. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis machine intelligence, 24(7), 881-892.
9. Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis: John Wiley & Sons.
10. Khadivar, A., & Mojibian, F. (2022). Workshops clustering using a combination approach of data mining and MCDM. Modern Researches in Decision Making.
11. Kumar, S., Suhaib, M., & Asjad, M. (2021). Narrowing the barriers to Industry 4.0 practices through PCA-Fuzzy AHP-K means. Journal of Advances in Management Research, 18(2), 200-226.
12. Llamazares, B. (2019). Using interval weights in MADM problems. Computers Industrial Engineering, 136, 345-354.
13. Mahdiraji, H. A., Kazimieras Zavadskas, E., Kazeminia, A., & Abbasi Kamardi, A. (2019). Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach. Economic research-Ekonomska istraživanja, 32(1), 2882-2892.
14. Moradi Fard, M., Thonet, T., & Gaussier, E. (2020). Deep k-Means: Jointly clustering with k-Means and learning representations. Pattern Recognition Letters, 138, 185-192.
15. Moubayed, A., Injadat, M., Shami, A., & Lutfiyya, H. (2020). Student engagement level in an e-learning environment: Clustering using k-means. American Journal of Distance Education, 34(2), 137-156.
16. Namvar, M., Gholamian, M. R., & KhakAbi, S. (2010). A two phase clustering method for intelligent customer segmentation. Paper presented at the 2010 International conference on intelligent systems, modelling and simulation.
17. Ng, R. T., & Han, J. (1994). E cient and E ective clustering methods for spatial data mining. Paper presented at the Proceedings of VLDB.
18. Özari, Ç., & Can, E. N. (2023). Financial Performance Evaluating and Ranking Approach for Banks in Bist Sustainability Index Using Topsis and K-Means Clustering Method. Academic Journal of Interdisciplinary Studies.
19. Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of interactive marketing, 18(3), 5-14.
20. Raharja, M. A., & Surya, I. K. A. (2022). Clustering Customer For Determine Market Strategy Using K-Means And TOPSIS: Case Study. Paper presented at the Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business.
21. Rajagopal, D. S. (2011). Customer data clustering using data mining technique. arXiv preprint arXiv:.
22. Razini, E., & Rasti, M. (2015). Assessing the competitive capacity of Iran's Copper Industry (Case Study of National Iranian Copper Company). Quarterly Journal of Business Research, 76, 81-51.
23. Sadeghi, M., Naghedi, R., Behzadian, K., Shamshirgaran, A., Tabrizi, M. R., & Maknoon, R. (2022). Customisation of green buildings assessment tools based on climatic zoning and experts judgement using K-means clustering and fuzzy AHP. Building Environment, 223, 109473.
24. Shahri, M. M., Jahromi, A. E., & Houshmand, M. (2021). Failure Mode and Effect Analysis using an integrated approach of clustering and MCDM under pythagorean fuzzy environment. Journal of Loss Prevention in the Process Industries, 72, 104591.
25. Syakur, M., Khotimah, B., Rochman, E., & Satoto, B. D. (2018). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. Paper presented at the IOP conference series: materials science and engineering.
26. Valipour, M., Yousefi, S., Jahangoshai Rezaee, M., & Saberi, M. (2022). A clustering-based approach for prioritizing health, safety and environment risks integrating fuzzy C-means and hybrid decision-making methods. Stochastic Environmental Research Risk Assessment, 36(3), 919-938.
27. Wang, Y., Ma, X., Lao, Y., & Wang, Y. (2014). A fuzzy-based customer clustering approach with hierarchical structure for logistics network optimization. Expert systems with applications, 41(2), 521-534.
28. Wu, J., & Lin, Z. (2005). Research on customer segmentation model by clustering. Paper presented at the Proceedings of the 7th international conference on Electronic commerce.
29. Yankelovich, D., & Meer, D. (2006). Rediscovering market segmentation. Harvard business review, 84(2), 122.
