Predictive model based on machine learning for raw material purchasing management in the retail sector.

Julio C. Antunez; Johnny D. Salazar; Pedro S. Castañeda

doi:10.1145/3677454.3677456

Predictive model based on machine learning for raw material purchasing management in the retail sector.

Julio C. Antunez
, Johnny D. Salazar
, Pedro S. Castañeda

Research output: Chapter in Book/Report › Conference contribution › peer-review

Abstract

Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.

Original language	American English
Title of host publication	ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning
Subtitle of host publication	Conference Proceeding
Publisher	Association for Computing Machinery
Pages	6-11
Number of pages	6
ISBN (Electronic)	9798400717116
DOIs	https://doi.org/10.1145/3677454.3677456
State	Indexed - 28 Jun 2024
Externally published	Yes
Event	2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024 - Hangzhou, China Duration: 28 Jun 2024 → 30 Jun 2024

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024
Country/Territory	China
City	Hangzhou
Period	28/06/24 → 30/06/24

Bibliographical note

Publisher Copyright:
© 2024 ACM.

Keywords

Inventory management
Model interpretation
SMEs

Access to Document

10.1145/3677454.3677456

Cite this

Antunez, J. C., Salazar, J. D., & Castañeda, P. S. (2024). Predictive model based on machine learning for raw material purchasing management in the retail sector. In ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning: Conference Proceeding (pp. 6-11). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3677454.3677456

Antunez, Julio C. ; Salazar, Johnny D. ; Castañeda, Pedro S. / Predictive model based on machine learning for raw material purchasing management in the retail sector. ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning: Conference Proceeding. Association for Computing Machinery, 2024. pp. 6-11 (ACM International Conference Proceeding Series).

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title = "Predictive model based on machine learning for raw material purchasing management in the retail sector.",

abstract = "Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.",

keywords = "Inventory management, Model interpretation, SMEs",

author = "Antunez, \{Julio C.\} and Salazar, \{Johnny D.\} and Casta{\~n}eda, \{Pedro S.\}",

note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024 ; Conference date: 28-06-2024 Through 30-06-2024",

year = "2024",

month = jun,

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doi = "10.1145/3677454.3677456",

language = "American English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "6--11",

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Antunez, JC, Salazar, JD & Castañeda, PS 2024, Predictive model based on machine learning for raw material purchasing management in the retail sector. in ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning: Conference Proceeding. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 6-11, 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024, Hangzhou, China, 28/06/24. https://doi.org/10.1145/3677454.3677456

Predictive model based on machine learning for raw material purchasing management in the retail sector. / Antunez, Julio C.; Salazar, Johnny D.; Castañeda, Pedro S.
ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning: Conference Proceeding. Association for Computing Machinery, 2024. p. 6-11 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report › Conference contribution › peer-review

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AU - Castañeda, Pedro S.

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Y1 - 2024/6/28

N2 - Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.

AB - Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.

KW - Inventory management

KW - Model interpretation

KW - SMEs

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DO - 10.1145/3677454.3677456

M3 - Conference contribution

AN - SCOPUS:85202845935

T3 - ACM International Conference Proceeding Series

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EP - 11

BT - ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning

PB - Association for Computing Machinery

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Antunez JC, Salazar JD, Castañeda PS. Predictive model based on machine learning for raw material purchasing management in the retail sector. In ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning: Conference Proceeding. Association for Computing Machinery. 2024. p. 6-11. (ACM International Conference Proceeding Series). doi: 10.1145/3677454.3677456

Predictive model based on machine learning for raw material purchasing management in the retail sector.

Abstract

Publication series

Conference

Bibliographical note

Keywords

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