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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Formato: | info:eu-repo/semantics/other acceptedVersion |
Lenguaje: | Inglés |
Publicado: |
2023
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Materias: | |
Acceso en línea: | https://repositorio.utdt.edu/handle/20.500.13098/11585 |
Aporte de: |
id |
I57-R163-20.500.13098-11585 |
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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 |
_version_ |
1764820542582423552 |