Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Enrique Ayala Mora i Marina Herrera Insuela

Detalhes bibliográficos
Autor: Segura i Pons, Jordi
Formato: tesis de maestría
Fecha de publicación:2023
País:España
Recursos:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/214427
Acesso em linha:https://hdl.handle.net/2445/214427
Access Level:acceso abierto
Palavra-chave:Aprenentatge automàtic
Gestió de vendes
Preus
Treballs de fi de màster
Machine learning
Sales management
Pricing
Master's thesis
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spelling Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric IberiaSegura i Pons, JordiAprenentatge automàticGestió de vendesPreusTreballs de fi de màsterMachine learningSales managementPricingMaster's thesisTreballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Enrique Ayala Mora i Marina Herrera InsuelaIn the increasingly competitive global business milieu, product pricing optimization and accurate sales forecasting are paramount. This MSc thesis probes these critical areas in relation to Schneider Electric Iberia, a front-runner in the digital conversion of energy management and automation. Our emphasis is on the adoption of cutting-edge data science methodologies, including econometrics, Machine Learning, causality analysis, and Deep Learning, with a goal to both predict sales and optimize price-points considering the demand elasticity of diverse products across various markets. The thesis initiates with an in-depth analysis of Schneider Electric’s extant pricing and sales forecasting systems, proceeding to the selection of suitable data science techniques for enhancement. Utilizing these methods, we devise and deploy a pricing optimization model aimed at augmenting revenue or sales volume. This model’s potential is then harnessed for sales forecasting, measuring its influence on the company’s operations in aspects like efficiency, profitability, and strategic decision-making amplifications. Our methodology pivots on comprehensive data collection, meticulous preprocessing, and insightful exploratory data analysis. We leverage the benefits of Graph Causal Models for price optimization and the innovative Temporal Fusion Transformer (TFT) for sales forecasting, conjuring a formidable tool for strategic planning. The optimized prices and predictive sales model converge on an interactive Tableau dashboard, endowing Schneider Electric Iberia with a user-friendly, accessible platform for data-driven decision making. This study aims to empower Schneider Electric Iberia, while also making a noteworthy contribution to the wider field of industrial technology and the deployment of AI in product pricing and sales forecasting.Ayala Mora, EnriqueHerrera Insuela, Marina2023info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2445/214427Màster Oficial - Fonaments de la Ciència de Dadesreponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaIngléscc-by-nc-nd (c) Jordi Segura i Pons, 2023http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2144272026-05-27T06:46:51Z
dc.title.none.fl_str_mv Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
title Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
spellingShingle Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
Segura i Pons, Jordi
Aprenentatge automàtic
Gestió de vendes
Preus
Treballs de fi de màster
Machine learning
Sales management
Pricing
Master's thesis
title_short Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
title_full Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
title_fullStr Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
title_full_unstemmed Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
title_sort Optimizing Product pricing and sales forecasting through advanced data science: a case study at Schneider Electric Iberia
dc.creator.none.fl_str_mv Segura i Pons, Jordi
author Segura i Pons, Jordi
author_facet Segura i Pons, Jordi
author_role author
dc.contributor.none.fl_str_mv Ayala Mora, Enrique
Herrera Insuela, Marina
dc.subject.none.fl_str_mv Aprenentatge automàtic
Gestió de vendes
Preus
Treballs de fi de màster
Machine learning
Sales management
Pricing
Master's thesis
topic Aprenentatge automàtic
Gestió de vendes
Preus
Treballs de fi de màster
Machine learning
Sales management
Pricing
Master's thesis
description Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Enrique Ayala Mora i Marina Herrera Insuela
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/214427
url https://hdl.handle.net/2445/214427
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Jordi Segura i Pons, 2023
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Jordi Segura i Pons, 2023
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Màster Oficial - Fonaments de la Ciència de Dades
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869423292412067840
score 15.198674

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