Predictive models to support quoting of fixed fee consulting projects
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Amy Cook Thesis (PDF 9MB) |
Description
This thesis tackled a problem faced by consulting companies in the construction industry, where a significant proportion of projects result in losses. This occurs despite managers’ best efforts to price and execute projects profitably. Several machine learning and statistical techniques were applied to a case study company’s historic timesheet, client, and invoicing data in order to predict loss-making projects. The algorithms were tested in a simulated business decision-making scenario and the best model improved profits by 9%. The work from this research makes a step towards helping businesses reduce risk by integrating their data into financial decisions.
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| ID Code: | 104557 |
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| Item Type: | QUT Thesis (Masters by Research) |
| Supervisor: | Mengersen, Kerrie & Wu, Paul P. |
| Keywords: | Cost Estimation, Consulting, Effort Estimation, Machine Learning, Statistics, Profitability, Prediction, Fixed Price, Regression, Decision Trees |
| DOI: | 10.5204/thesis.eprints.104557 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 28 Mar 2017 22:55 |
| Last Modified: | 20 Oct 2017 14:43 |
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