Journal
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
Volume 34, Issue 2, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CP.1943-5487.0000871
Keywords
Decision support; Equipment management; Bayesian inference; Dynamic data analytics
Funding
- Graham Industrial Services LP
- Natural Science and Engineering Research Council of Canada (NSERC) [CRDPJ 492657]
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Successful acquisition and disposal of construction equipment-a capital-intensive business process-requires both experience and expertise, particularly when assessing fair market values of equipment. Although advanced predictive models capable of assessing equipment residual market value have been developed, these models cannot be automatically updated with new market data, rendering them less and less accurate over time. This study proposes a Bayesian inference-based method capable of integrating historical and dynamic data to more dependably predict the likelihood of acquiring equipment at bargain values (i.e., lower than market). This method is intended to better inform practitioners of ideal times, locations, and makes/models of equipment to purchase or sell. Historical data of a commonly used piece of construction equipment, the CAT 320 Excavator, were used to demonstrate the feasibility and validity of the proposed approach, which was found capable of generating dependable, representative results.
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