Manufacturing Cost Prediction Through Data Mining

Andrea Díaz, Simón Fernández, Laura Guerra, Eleazar Díaz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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.

Original languageEnglish
Title of host publicationDevelopments and Advances in Defense and Security - Proceedings of MICRADS 2020
EditorsÁlvaro Rocha, Manolo Paredes-Calderón, Teresa Guarda
Number of pages8
ISBN (Print)9789811548741
StatePublished - 2020
Externally publishedYes
EventMultidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020 - Quito, Ecuador
Duration: 13 May 202015 May 2020

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


ConferenceMultidisciplinary International Conference of Research Applied to Defense and Security, MICRADS 2020

Bibliographical note

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


  • Clusters
  • Data mining
  • Manufacturing cost
  • Predictive model


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