The use of parametric cost estimating and risk management techniques to improve project cost estimates during feasibility studies

M.Ing. (Engineering Management)“A robust set of estimates puts a project on a firm footing from day 1, allowing the project manager to apply the right level of resources at the appropriate time. If the plan has been based on poor estimates, problems will occur during the execution of the project …” This statement places great importance on the ability to estimate costs as accurately as practicable early during a project life cycle. Many techniques have been proposed with the aim of aiding with the production of early cost estimates,which have acceptable accuracies necessary for Feasibility Study purposes. One such technique is Parametric Cost Estimating for developing Parametric Cost Models used in producing these conceptual estimates.At the heart of Parametric Cost Estimating Technique, is a fundamental statistical technique commonly known as Linear Regression Analysis.The problem that the research addresses is that of the general misconception found to prevail within project houses that some engineering systems are too complex to model using the Parametric Cost Estimating Technique. The objectives of this research are to investigate and demonstrate the effectiveness of this technique in predicting the costs of a system for Feasibility Study purposes. The objectives were achieved by conducting a secondary literature review of case studies of similar Parametric Cost Models that were developed by others for engineering systems of varying complexities. A second method used in achieving the objectives included formulating a case study in which a Parametric Cost Model was developed to illustrate the concept and to prove that the accuracies produced by the model meet the requirements for Feasibility Studies.The research was limited to initial project costs required for Feasibility Studies,ignoring the effects of qualitative factors,focusing only on the acquisition costs and not the total life cycle costs of the system.The case study was developed for a passenger motor vehicle as the system of interest because sufficient cost data in the form of vehicle retail price and performance specifications is publicly available in car magazines making it possible to build a meaningful Parametric Cost Model. The Parametric Cost Model was developed using Microsoft Excel 2007 and had a Mean Absolute Error Rate of 10.9% and the range of accuracy obtained, -20% to 10% with 67% confidence level and -30% to 30% with 95% confidence level, conforming to a Class 4 estimate which meets the accuracy requirements for a Feasibility Study