Construction Equipment’s Residual Market Value Estimation Using Machine Learning

Kleopatra Petroutsatou (), Ilias Ladopoulos and Marios Polyzos
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Kleopatra Petroutsatou: Aristotle University of Thessaloniki
Ilias Ladopoulos: Aristotle University of Thessaloniki
Marios Polyzos: Aristotle University of Thessaloniki

A chapter in Operational Research in the Era of Digital Transformation and Business Analytics, 2023, pp 195-203 from Springer

Abstract: 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.

Keywords: Construction equipment; Machine learning; Residual market value; Excavator (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-24294-6_21

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DOI: 10.1007/978-3-031-24294-6_21

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