Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM)
Today evaluation of customers to classify the quality of providing services is one of the main challenges of decision-makers in different organizations. It is difficult to respond to all customers’ demands because of increasing volume of them. On the other hand, in current competitive markets, custo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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Allameh Tabataba'i University Press
2016-03-01
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| Series: | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| Subjects: | |
| Online Access: | https://jims.atu.ac.ir/article_3914_e07ab333c3abd941cf573c4eb48de497.pdf |
| _version_ | 1797367725347045376 |
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| author | Ali Bonyadi Naeini Saeed Yousef Mohammad Ali Faezirad |
| author_facet | Ali Bonyadi Naeini Saeed Yousef Mohammad Ali Faezirad |
| author_sort | Ali Bonyadi Naeini |
| collection | DOAJ |
| description | Today evaluation of customers to classify the quality of providing services is one of the main challenges of decision-makers in different organizations. It is difficult to respond to all customers’ demands because of increasing volume of them. On the other hand, in current competitive markets, customers are considered as capital of organizations. This issue results in purposefully study on different groups of customers in competitive markets. One of the effective ways to study the customers and provide the optimism service to them is grouping the market and clustering the customers. In this research first customers classified in appropriate clusters using neural network techniques SOM in order to provide purposefully service , so each customer can deliver proper service according to its cluster. Then by the proposed model in the paper the membership of each client in the appropriate cluster can be predicted by using DEA-DA technique. This model has provided dynamic clustering process for organizations so that by which new customers will be assessed at any moment and their proper cluster is determined with reasonable accuracy. |
| first_indexed | 2024-03-08T17:22:19Z |
| format | Article |
| id | doaj.art-fd4d49da6c1141c9b7ed334a5d4f648b |
| institution | Directory Open Access Journal |
| issn | 2251-8029 2476-602X |
| language | fas |
| last_indexed | 2024-03-08T17:22:19Z |
| publishDate | 2016-03-01 |
| publisher | Allameh Tabataba'i University Press |
| record_format | Article |
| series | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
| spelling | doaj.art-fd4d49da6c1141c9b7ed334a5d4f648b2024-01-03T04:44:22ZfasAllameh Tabataba'i University PressMuṭāli̒āt-i Mudīriyyat-i Ṣan̒atī2251-80292476-602X2016-03-01144016518710.22054/jims.2016.39143914Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM)Ali Bonyadi Naeini0Saeed Yousef1Mohammad Ali Faezirad2استادیار دانشکده مهندسی پیشرفت، دانشگاه علم و صنعت ایرانکارشناسی ارشد مدیریت صنعتی دانشگاه علامه طباطباییدانشجوی دکتری مدیریت تحقیق در عملیات، دانشگاه فردوسی مشهدToday evaluation of customers to classify the quality of providing services is one of the main challenges of decision-makers in different organizations. It is difficult to respond to all customers’ demands because of increasing volume of them. On the other hand, in current competitive markets, customers are considered as capital of organizations. This issue results in purposefully study on different groups of customers in competitive markets. One of the effective ways to study the customers and provide the optimism service to them is grouping the market and clustering the customers. In this research first customers classified in appropriate clusters using neural network techniques SOM in order to provide purposefully service , so each customer can deliver proper service according to its cluster. Then by the proposed model in the paper the membership of each client in the appropriate cluster can be predicted by using DEA-DA technique. This model has provided dynamic clustering process for organizations so that by which new customers will be assessed at any moment and their proper cluster is determined with reasonable accuracy.https://jims.atu.ac.ir/article_3914_e07ab333c3abd941cf573c4eb48de497.pdfclusteringdiscriminant analysis (da)data envelopment analysis (dea)artificial neural networkself-organizing map (som) |
| spellingShingle | Ali Bonyadi Naeini Saeed Yousef Mohammad Ali Faezirad Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM) Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī clustering discriminant analysis (da) data envelopment analysis (dea) artificial neural network self-organizing map (som) |
| title | Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM) |
| title_full | Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM) |
| title_fullStr | Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM) |
| title_full_unstemmed | Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM) |
| title_short | Dynamic clustering of customers by Data Envelopment Analysis (DEA) - Discriminant Analysis (DA) and Artificial Neural Network (SOM) |
| title_sort | dynamic clustering of customers by data envelopment analysis dea discriminant analysis da and artificial neural network som |
| topic | clustering discriminant analysis (da) data envelopment analysis (dea) artificial neural network self-organizing map (som) |
| url | https://jims.atu.ac.ir/article_3914_e07ab333c3abd941cf573c4eb48de497.pdf |
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