A New Hybrid Approach for Product Management in E-Commerce

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dc.authoridOzsahin, Metin/0000-0001-9989-526X
dc.authoridAkcan, Serap/0000-0003-2621-9142
dc.authoridYuregir, Oya H./0000-0002-9607-8149
dc.contributor.authorYuregir, Hacire Oya
dc.contributor.authorOzsahin, Metin
dc.contributor.authorAkcan Yetgin, Serap
dc.date.accessioned2025-03-17T12:25:18Z
dc.date.available2025-03-17T12:25:18Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractNowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based-genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm.
dc.identifier.doi10.3390/app14135735
dc.identifier.issn2076-3417
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85198449531
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app14135735
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1600
dc.identifier.volume14
dc.identifier.wosWOS:001269442500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofApplied Sciences-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectbig data
dc.subjectdata mining
dc.subjecte-commerce
dc.subjectdemand forecasting
dc.subjectgenetic algorithm
dc.subjectcluster analysis
dc.titleA New Hybrid Approach for Product Management in E-Commerce
dc.typeArticle

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