• Author / Creator
    Chiteri, Martin
  • Cash is the most important resource at the disposal of a construction company, and management of cash has a direct impact on long- and short-term company performance. Accurately predicting the amount of cash-flow expected from particular construction operations, however, remains challenging due to the dynamic nature of construction projects, which often changes as progress is made, when projects come to an end, as new projects begin, or when costs for different items vary. Various models for improved cash flow management have been proposed, each with varying levels of success. The first part of this study focuses on calculating cash-flows expected from the useful life of non-operated pieces of equipment. An entire fleet of 5 039 assets is analyzed with their ownership costs projected over their economic lives. A non-linear optimization is performed to determine the ideal amounts of ownership recovery given an organization’s internal rate of return. Suitable data was available for 3 914 pieces of equipment; sums of their ownership costs are grouped by equipment category and by associated yard. Reliably assessing fair market prices of equipment is required during asset disposal, for establishing rental rates, to estimate the financial position of their firms during financial audits, and, for companies operating with refurbished equipment, making purchases. Residual value analysis is another aspect of equipment management that faces many uncertainties. The second part of this research analyses several data mining algorithms to estimate market prices of a group of ¾ ton trucks to determine which algorithm produces the best data model for this type of residual value prediction. The analysis is based on historical data from auction and resale transactions for that equipment category combined with economic inflation data. Attributes of equipment, including age, year of manufacture, service meter reading, location of transaction, and inflation ii rate during sale, are considered. Of the four algorithms applied, the random forest algorithm offered the best performance followed by the multiple linear regression algorithm. The artificial neural network and k-nearest neighbor algorithms resulted in the lowest performance. A multiple linear regression method was chosen due to its ease of interpretation and relatively high accuracy, and a generic system that predicts equipment market values using the multiple linear regression algorithm was built. Both studies have demonstrated that automation of processes involved in equipment management can provide benefits in practice, such as the ability to analyze large volumes of data quickly and accurately. The techniques are also flexible and can easily incorporate information from various sources. The results obtained are more objective and can form a better basis in the process of decision making as compared to using personal experience and rules-of-thumb.

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    Master of Science
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