Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/3727
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dc.contributor.authorDeğirmencioğlu, Y.-
dc.contributor.authorAkyurt, İ.Z.-
dc.date.accessioned2023-03-03T13:34:24Z-
dc.date.available2023-03-03T13:34:24Z-
dc.date.issued2023-
dc.identifier.isbn9781000846836-
dc.identifier.isbn9781032018430-
dc.identifier.urihttps://doi.org/10.1201/9781003180302-4-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/3727-
dc.description.abstractForecasting processes take into account past consumer behavior as well as current trends. This method requires not only predicting future demand but also explaining previous demand behavior and the factors influencing that behavior. As the amount of data in businesses grows, it becomes increasingly challenging to create accurate and timely predictions. For businesses that wish to maintain their competitiveness, making accurate and quick predictions is unavoidable. New technologies such as powerful artificial intelligence algorithms, cloud computing, machine learning, data mining, and simulation facilitate the classification, sorting, processing, and interpretation of data. Thus, managers can make dynamic, fast, and satisfactory forecasts. © 2023 selection and editorial matter, Turan Paksoy and Muhammet Deveci; individual chapters, the contributors.en_US
dc.language.isoenen_US
dc.publisherCRC Pressen_US
dc.relation.ispartofSmart and Sustainable Operations and Supply Chain Management in Industry 4.0en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleForecastingen_US
dc.typeBook Parten_US
dc.identifier.doi10.1201/9781003180302-4-
dc.identifier.scopus2-s2.0-85148124504en_US
dc.departmentKTUNen_US
dc.identifier.startpage77en_US
dc.identifier.endpage100en_US
dc.institutionauthor-
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.authorscopusid58104531900-
dc.authorscopusid57203954687-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairetypeBook Part-
item.grantfulltextembargo_20300101-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.09. Department of Industrial Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
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