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<ArticleSet>
<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>Fuzzy Optimization and Modeling Journal (FOMJ)</JournalTitle>
<Issn>2676-7007</Issn>
<Volume>5</Volume>
<Issue>1</Issue>
<PubDate PubStatus="epublish">
<Year>2024</Year>
<Month>06</Month>
<Day>26</Day>
</PubDate>
</Journal>
<ArticleTitle>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</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage>13</FirstPage>
<LastPage>26</LastPage>
<ELocationID EIdType="doi">10.71808/fomj.2024.1002655</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Hossein</FirstName>
<LastName>Mohammadi Dolat-Abadi</LastName>
<Affiliation>Farabi College, Department of Industrial Engineering, University of Tehran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
<Author>
<FirstName>Amirsadra</FirstName>
<LastName>Sadat</LastName>
<Affiliation>industrial engineering, Farabi College, Department of Industrial Engineering, University of Tehran, Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2024</Year>
<Month>06</Month>
<Day>26</Day>
</PubDate>
</History>
<Abstract>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.</Abstract>
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<Param Name="value">K-means Method</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Fuzzy TOPSIS</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Customer Eelationship System</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Metals Supply Chain</Param>
</Object>
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</Article>
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