?url_ver=Z39.88-2004&rft_id=10.5204%2Fthesis.eprints.104557&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Predictive+models+to+support+quoting+of+fixed+fee+consulting+projects&rft.creator=Cook%2C+Amy+W.&rft.subject=Cost+Estimation&rft.subject=Consulting&rft.subject=Effort+Estimation&rft.subject=Machine+Learning&rft.subject=Statistics&rft.subject=Profitability&rft.subject=Prediction&rft.subject=Fixed+Price&rft.subject=Regression&rft.subject=Decision+Trees&rft.description=This+thesis+tackled+a+problem+faced+by+consulting+companies+in+the+construction+industry%2C+where+a+significant+proportion+of+projects+result+in+losses.+This+occurs+despite+managers%E2%80%99+best+efforts+to+price+and+execute+projects+profitably.+Several+machine+learning+and+statistical+techniques+were+applied+to+a+case+study+company%E2%80%99s+historic+timesheet%2C+client%2C+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%25.+The+work+from+this+research+makes+a+step+towards+helping+businesses+reduce+risk+by+integrating+their+data+into+financial+decisions.&rft.publisher=Queensland+University+of+Technology&rft.date=2017&rft.type=Thesis&rft.format=application%2Fpdf&rft.relation=https%3A%2F%2Feprints.qut.edu.au%2F104557%2F1%2FAmy_Cook_Thesis.pdf&rft.rights=free_to_read&rft.relation=doi%3A10.5204%2Fthesis.eprints.104557&rft.relation=Cook%2C+Amy+W.+(2017)+Predictive+models+to+support+quoting+of+fixed+fee+consulting+projects.+Masters+by+Research+thesis%2C+Queensland+University+of+Technology.&rft.id_number=https%3A%2F%2Feprints.qut.edu.au%2F104557%2F&rft.identifier=Science+%26+Engineering+Faculty%3B+School+of+Mathematical+Sciences