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IJMLC 2021 Vol.11(1): 28-33 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1010

Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning

O. Kurasova, V. Marcinkevičius, V. Medvedev, and B. Mikulskienė

Abstract—Accurate cost estimation at the early stage of a construction project is a key factor in the success of most projects. Many difficulties arise when estimating the cost during the early design stage in customized furniture manufacturing. It is important to estimate the product cost in the earlier manufacturing phase. The cost estimation is related to the prediction of the cost, which commonly includes calculation of the materials, labor, sales, overhead, and other costs. Historical data of the previously manufactured products can be used in the cost estimation process of the new products. In this paper, we propose an early cost estimation approach, which is based on machine learning techniques. The experimental investigation based on the real customized furniture manufacturing data is performed, results are presented, and insights are given.

Index Terms—Cost estimation, customized furniture manufacturing, analogous estimating, machine learning, prediction.

All authors are with the Mykolas Romeris University, Ateities str. 20, LT-08303 Vilnius, Lithuania (e-mail: olga.kurasova@gmail.com).

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Cite: O. Kurasova, V. Marcinkevičius, V. Medvedev, and B. Mikulskienė, "Early Cost Estimation in Customized Furniture Manufacturing Using Machine Learning," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 28-33, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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