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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/2115
DC FieldValueLanguage
dc.contributor.authorTran Quang Linhen_US
dc.date.accessioned2026-01-08T07:20:43Z-
dc.date.available2026-01-08T07:20:43Z-
dc.date.issued2024-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/2115-
dc.description.abstractAccurate demand forecasting is essential for supply chain efficiency, particularly in the fast-moving consumer goods (FMCG) industry, where product availability directly influences customer purchase decisions. While traditional time-series forecasting focuses on predicting aggregated demand, disaggregating forecasts to finer time horizons is crucial for tactical planning, production scheduling, and inventory management. This thesis develops a demand forecasting architecture that leverages Artificial Neural Networks (ANNs) to enhance the accuracy of demand disaggregation from higher-level forecasts (monthly/yearly) to lower-level horizons (weekly). The research includes a review of current demand planning practices and the application of deep learning in supply chain contexts, the development of an ANN-based disaggregation model, and an evaluation of its integration into production and supply chain information systems. Furthermore, the study discusses production strategies that align with the ANN-based architecture. The proposed approach aims to improve operational efficiency, reduce stock-outs, and increase supply chain reliability by optimizing forecast granularity.en_US
dc.language.isoenen_US
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0/*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectSupply chain planningen_US
dc.subjectTime-series forecastingen_US
dc.subjectDemand disaggregationen_US
dc.titleImplementation of artificial neural network for demand disaggregation to enhance demand forecasting performance in FMCG industryen_US
dc.typeThesisen_US
item.fulltextWith Fulltext-
item.languageiso639-1other-
item.grantfulltextrestricted-
Appears in Collections:Global Production Engineering & Management (GPEM)
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