An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain

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dc.authorid0000-0003-0793-1601
dc.contributor.authorKilimci, Zeynep Hilal
dc.contributor.authorAkyüz, A. Okay
dc.contributor.authorUysal, Mitat
dc.contributor.authorAkyokuş, Selim
dc.contributor.authorUysal, Mustafa Ozan
dc.contributor.authorAtak Bülbül, Berna
dc.contributor.authorEkmiş, Mehmet Ali
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T19:51:32Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T19:51:32Z
dc.date.issued2019
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.descriptionWOS: 000464724700001
dc.description.abstractDemand forecasting is one of the main issues of supply chains. It aimed to optimize stocks, reduce costs, and increase sales, profit, and customer loyalty. For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. In this work, an intelligent demand forecasting system is developed. This improved model is based on the analysis and interpretation of the historical data by using different forecasting methods which include time series analysis techniques, support vector regression algorithm, and deep learning models. To the best of our knowledge, this is the first study to blend the deep learning methodology, support vector regression algorithm, and different time series analysis models by a novel decision integration strategy for demand forecasting approach. The other novelty of this work is the adaptation of boosting ensemble strategy to demand forecasting system by implementing a novel decision integration model. The developed system is applied and tested on real life data obtained from SOK Market in Turkey which operates as a fast-growing company with 6700 stores, 1500 products, and 23 distribution centers. A wide range of comparative and extensive experiments demonstrate that the proposed demand forecasting system exhibits noteworthy results compared to the state-of-art studies. Unlike the state-of-art studies, inclusion of support vector regression, deep learning model, and a novel integration strategy to the proposed forecasting system ensures significant accuracy improvement.
dc.description.sponsorshipOBASE Research & Development Centeren_US
dc.description.sponsorshipThis work is supported by OBASE Research & Development Centeren_US
dc.identifier.citationKilimci, Z. H., Akyüz, A. O., Uysal, M., Akyokuş, S., Uysal, M. O., Atak Bülbül, B. ... Ekmiş, M. A. (2019). An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain. Complexity. https://dx.doi.org/10.1155/2019/9067367
dc.identifier.doi10.1155/2019/9067367
dc.identifier.issn1076-2787
dc.identifier.issn1099-0526
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.1155/2019/9067367
dc.identifier.urihttps://hdl.handle.net/20.500.12511/2235
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-Hindawi
dc.relation.ispartofComplexityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectDemand Forecasting
dc.subjectDeep Learning
dc.subjectIntegration Strategy
dc.titleAn improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain
dc.typeArticle

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