Customer Clustering via Combined TOPSIS-Fuzzy -K-MEANS Method to Design an Efficient Customer Relationship System: A Real-life Case Study in the Copper Industry
محورهای موضوعی : Fuzzy Optimization and Modeling JournalHossein Mohammadi Dolat-Abadi 1 , Amirsadra Sadat 2
1 - Farabi College, Department of Industrial Engineering, University of Tehran
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.
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