Retail forecasting under the influence of promotional discounts
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/22165 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
id |
RCAP_81b05427c35d6e840a1ab8594e711a64 |
---|---|
oai_identifier_str |
oai:run.unl.pt:10362/22165 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Retail forecasting under the influence of promotional discountsStorage allocationBig DataPhantom stockMachine LearningAlgorithmDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAs we observe a rise of competitive pressure in retail business, major players control the market using promotions to attract and increase the fidelity of customers. These promotions cause a significant decrease in profit margin hence forcing a stronger focus on logistic processes efficiency. Forced to make use of stochastic tools in order to better allocate resources for the future events, those responsible for assortment strategy frequently choose time series based algorithms and simple extrapolation of historical data, under the assumption that these events can be considered continuous, smooth and possibly periodic. While computationally light, these algorithms are subject to greater uncertainty due to the simplistic approach. Meanwhile, the explosive growth of information and availability of data brought by improved automatic collection systems allow new and more complex approaches.These tackle the high dimensionality problem, focused on retrieving knowledge from potentially rich sources of information. The work developed in this thesis aims to develop a comprehensive and scalable solution using machine learning algorithms to forecast daily sales of articles in a retail store, under the influence of discounts, as to support logistic storage allocation operations. This is done with the purpose of decreasing costs related to stock warehousing while simultaneously decreasing stock-outs as they directly influence client satisfaction with the brand. The development of a successful automatic modelling system would simultaneously allow retailers to optimize their promotional schedules based on the expected results of different simulations. Using real data from one of the biggest retailers in Portugal, this project’s falls into the definition of Big Data due to extensive historical databases which cannot be simultaneously processed. The presence of discrepancies between registered stock and physical availability - Phantom stock - will be considered as well as relevant external events which affect the sales.Vanneschi, LeonardoRUNCarreira, André Neves de Almeida Roque2017-07-26T11:01:35Z2017-07-212017-07-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/22165TID:201719886enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:09:30Zoai:run.unl.pt:10362/22165Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:27:10.194763Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Retail forecasting under the influence of promotional discounts |
title |
Retail forecasting under the influence of promotional discounts |
spellingShingle |
Retail forecasting under the influence of promotional discounts Carreira, André Neves de Almeida Roque Storage allocation Big Data Phantom stock Machine Learning Algorithm |
title_short |
Retail forecasting under the influence of promotional discounts |
title_full |
Retail forecasting under the influence of promotional discounts |
title_fullStr |
Retail forecasting under the influence of promotional discounts |
title_full_unstemmed |
Retail forecasting under the influence of promotional discounts |
title_sort |
Retail forecasting under the influence of promotional discounts |
author |
Carreira, André Neves de Almeida Roque |
author_facet |
Carreira, André Neves de Almeida Roque |
author_role |
author |
dc.contributor.none.fl_str_mv |
Vanneschi, Leonardo RUN |
dc.contributor.author.fl_str_mv |
Carreira, André Neves de Almeida Roque |
dc.subject.por.fl_str_mv |
Storage allocation Big Data Phantom stock Machine Learning Algorithm |
topic |
Storage allocation Big Data Phantom stock Machine Learning Algorithm |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-26T11:01:35Z 2017-07-21 2017-07-21T00:00:00Z |
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/10362/22165 TID:201719886 |
url |
http://hdl.handle.net/10362/22165 |
identifier_str_mv |
TID:201719886 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137900539936768 |