Manufacturing Cost Prediction Through Data Mining

Andrea Díaz, Simón Fernández, Laura Guerra, Eleazar Díaz

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

This paper explores the feasibility of predicting manufacturing costs of a product with nonparametric methods through data mining. A dataset consisting of qualitative and quantitative attributes was collected from one of the largest menswear manufacturers in North America. Products with similar characteristics in this dataset were analyzed and clustered together using the k-medoids algorithm and a cost estimation regression model was established for each cluster. This allowed an assessment of the best cluster to assign a new product and then predict its manufacturing cost based on its regression model. Rousseeuw’s silhouette width was applied to determine the optimal number of clusters as 18 and then polynomial regression models were assigned to determine the manufacturing cost of each cluster. The k-medoids clustering algorithm proved to be compatible with the dataset which included mixed categorical and numerical variables. Predictive costing through data analysis will lay the foundations for properly understanding and structuring manufacturing costs.

Idioma originalInglés
Título de la publicación alojadaDevelopments and Advances in Defense and Security - Proceedings of MICRADS 2020
EditoresÁlvaro Rocha, Manolo Paredes-Calderón, Teresa Guarda
EditorialSpringer
Páginas251-258
Número de páginas8
ISBN (versión impresa)9789811548741
DOI
EstadoPublicada - 2020
Publicado de forma externa
EventoMultidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020 - Quito, Ecuador
Duración: 13 may. 202015 may. 2020

Serie de la publicación

NombreSmart Innovation, Systems and Technologies
Volumen181
ISSN (versión impresa)2190-3018
ISSN (versión digital)2190-3026

Conferencia

ConferenciaMultidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020
País/TerritorioEcuador
CiudadQuito
Período13/05/2015/05/20

Nota bibliográfica

Publisher Copyright:
© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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