A Machine Learning Approach for Prediction of Corrugated Cases Prices

Even though machine learning is widely spread among different industries, its application in the fast-moving consumer goods (FMCG) business is not a common practice and even today it remains in its early stages. Moreover, to our knowledge, there has never been a systematic approach to predict pac...

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Autor principal: Mera, Ignacio
Otros Autores: Cornejo, Magdalena
Formato: info:eu-repo/semantics/other acceptedVersion
Lenguaje:Inglés
Publicado: 2023
Materias:
Algorithms
Sales
Competition
Prices
Machine Learning
Supply Chain
Acceso en línea:https://repositorio.utdt.edu/handle/20.500.13098/11585
Aporte de:
Repositorio Digital Universidad Torcuato Di Tella de Universidad Torcuato Di Tella
id I57-R163-20.500.13098-11585
record_format dspace
institution Universidad Torcuato Di Tella
institution_str I-57
repository_str R-163
collection Repositorio Digital Universidad Torcuato Di Tella
language Inglés
orig_language_str_mv eng
topic Algorithms
Sales
Competition
Prices
Machine Learning
Supply Chain
spellingShingle Algorithms
Sales
Competition
Prices
Machine Learning
Supply Chain
Mera, Ignacio
A Machine Learning Approach for Prediction of Corrugated Cases Prices
topic_facet Algorithms
Sales
Competition
Prices
Machine Learning
Supply Chain
description Even though machine learning is widely spread among different industries, its application in the fast-moving consumer goods (FMCG) business is not a common practice and even today it remains in its early stages. Moreover, to our knowledge, there has never been a systematic approach to predict packaging materials costs in this kind of markets using machine learning algorithms, from the buyer’s perspective. On the other hand, the FMCG business is a highly competitive environment, in which profitability depends not only upon sales, but also upon keeping healthy product margins. This means not only setting the right prices that consumers are willing to pay, but also getting the lowest possible costs in the supply chain. Cases usually represent between 15% and 25% of the total packaging cost, being a material with functional requirements that usually does not add value to the consumer. Therefore, it is of high importance to maintain low cases prices to achieve competitivity. In this work we propose a machine learning approach for prediction of prices of a corrugated cases portfolio of a big FMCG firm in LATAM, to understand if real prices are higher or lower than what is suggested by the model. In this way, anomalies in the dataset will be unveiled, which might become opportunities for further negotiations and costs reductions.
author2 Cornejo, Magdalena
author_facet Cornejo, Magdalena
Mera, Ignacio
format info:eu-repo/semantics/other
acceptedVersion
author Mera, Ignacio
author_sort Mera, Ignacio
title A Machine Learning Approach for Prediction of Corrugated Cases Prices
title_short A Machine Learning Approach for Prediction of Corrugated Cases Prices
title_full A Machine Learning Approach for Prediction of Corrugated Cases Prices
title_fullStr A Machine Learning Approach for Prediction of Corrugated Cases Prices
title_full_unstemmed A Machine Learning Approach for Prediction of Corrugated Cases Prices
title_sort machine learning approach for prediction of corrugated cases prices
publishDate 2023
url https://repositorio.utdt.edu/handle/20.500.13098/11585
work_keys_str_mv AT meraignacio amachinelearningapproachforpredictionofcorrugatedcasesprices
AT meraignacio machinelearningapproachforpredictionofcorrugatedcasesprices
bdutipo_str Repositorios
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