Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach

Bibliographic Details
Main Author: Wandersleb, Tobias Theodor
Publication Date: 2021
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/140130
Summary: VOIDS is a data science student entrepreneurial project that aims to integrate marketing into demand planning, helping companies to achieve the most accurate way of planning and shaping future demand. The following work applies this vision in a lean agile start-up framework, implementing state-of-the-art deep learning time series forecasting techniques for the global consumer electronic brand Samsung. This work implements Google AI’s Temporal Fusion Transformer to forecast demand. Novel algorithms then optimize forecasts related to Samsung’s operational processes and context and benchmark against a customized measure of accuracy, achieving a 11,4-pp. absolute increase. Further, applicability to demand shaping is discussed.
id RCAP_a2a490acb48891cd7ce6bd2ff3b6fcdb
oai_identifier_str oai:run.unl.pt:10362/140130
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approachMarketingForecastingMachine learningPythonBusiness analyticsDigital transformationDeep learningDemand planningConsumer electronicsLean startupDomínio/Área Científica::Ciências Sociais::Economia e GestãoVOIDS is a data science student entrepreneurial project that aims to integrate marketing into demand planning, helping companies to achieve the most accurate way of planning and shaping future demand. The following work applies this vision in a lean agile start-up framework, implementing state-of-the-art deep learning time series forecasting techniques for the global consumer electronic brand Samsung. This work implements Google AI’s Temporal Fusion Transformer to forecast demand. Novel algorithms then optimize forecasts related to Samsung’s operational processes and context and benchmark against a customized measure of accuracy, achieving a 11,4-pp. absolute increase. Further, applicability to demand shaping is discussed.Han, QiweiMeixedo, BonifácioRUNWandersleb, Tobias Theodor2022-01-212021-12-172027-12-17T00:00:00Z2022-01-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140130TID:202997480enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:02:30Zoai:run.unl.pt:10362/140130Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:33:24.835606Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
title Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
spellingShingle Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
Wandersleb, Tobias Theodor
Marketing
Forecasting
Machine learning
Python
Business analytics
Digital transformation
Deep learning
Demand planning
Consumer electronics
Lean startup
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
title_full Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
title_fullStr Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
title_full_unstemmed Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
title_sort Samsung field lab Voids: demand forecasting for consumer electronic goods - a transformer-based deep learning approach
author Wandersleb, Tobias Theodor
author_facet Wandersleb, Tobias Theodor
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
Meixedo, Bonifácio
RUN
dc.contributor.author.fl_str_mv Wandersleb, Tobias Theodor
dc.subject.por.fl_str_mv Marketing
Forecasting
Machine learning
Python
Business analytics
Digital transformation
Deep learning
Demand planning
Consumer electronics
Lean startup
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Marketing
Forecasting
Machine learning
Python
Business analytics
Digital transformation
Deep learning
Demand planning
Consumer electronics
Lean startup
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description VOIDS is a data science student entrepreneurial project that aims to integrate marketing into demand planning, helping companies to achieve the most accurate way of planning and shaping future demand. The following work applies this vision in a lean agile start-up framework, implementing state-of-the-art deep learning time series forecasting techniques for the global consumer electronic brand Samsung. This work implements Google AI’s Temporal Fusion Transformer to forecast demand. Novel algorithms then optimize forecasts related to Samsung’s operational processes and context and benchmark against a customized measure of accuracy, achieving a 11,4-pp. absolute increase. Further, applicability to demand shaping is discussed.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-17
2022-01-21
2022-01-21T00:00:00Z
2027-12-17T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/140130
TID:202997480
url http://hdl.handle.net/10362/140130
identifier_str_mv TID:202997480
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596787820068864

Similar Items