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Construction Equipment’s Residual Market Value Estimation Using Machine Learning

In: Operational Research in the Era of Digital Transformation and Business Analytics

Author

Listed:
  • Kleopatra Petroutsatou

    (Aristotle University of Thessaloniki)

  • Ilias Ladopoulos

    (Aristotle University of Thessaloniki)

  • Marios Polyzos

    (Aristotle University of Thessaloniki)

Abstract

This study focuses on the identification of the patterns, in which the residual market value (RMV) of construction equipment (CE) is being evolved through time. One of the nine foundational technology advances that Industry 4.0 has brought to humanity is the use of big data analytics, through Machine Learning (ML) techniques. In the domain of CE, this entity of data exists for many decades. Yet, the knowledge that could be extracted from this data is untapped, while great CE manufacturers, owners or dealers, are unstoppably gathering tons of information, concerning ownership, operation and maintenance costs. This study focuses on the ownership cost and more specifically on the identification of the patterns, in which the RMV of CE is being evolved through time. RMV of a machine when sold at any point in its life is an unknown that depends on many factors. This study presents a prediction model for RMV of excavators. A database is created using market information from equipment owners, CE manufacturers, CE auctions and it is used as a “test bed” for the prediction model. The model was developed with the use of RapidMiner Studio software. The results reached a very good level of accuracy in estimating residual market values.

Suggested Citation

  • Kleopatra Petroutsatou & Ilias Ladopoulos & Marios Polyzos, 2023. "Construction Equipment’s Residual Market Value Estimation Using Machine Learning," Springer Proceedings in Business and Economics, in: Nikolaos F. Matsatsinis & Fotis C. Kitsios & Michael A. Madas & Maria I. Kamariotou (ed.), Operational Research in the Era of Digital Transformation and Business Analytics, pages 195-203, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-24294-6_21
    DOI: 10.1007/978-3-031-24294-6_21
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