Trost, S M (1998) A quantitative model for predicting the accuracy of early cost estimates for construction projects in the process industry. PhD thesis, Oklahoma State University, USA.
Abstract
Purpose and objectives. The importance of accurate estimates during the early stages of capital projects has been widely recognized for many years. Early project estimates represent a key ingredient in business unit decisions and often become the basis for a project's ultimate finding. However, a stark contrast arises when comparing the importance of early estimates with the amount of information typically available during the preparation of an early estimate. Such limited scope definition often leads to questionable estimate accuracy. Even so, very few quantitative methods are available that enable estimators and business managers to objectively evaluate the accuracy of early estimates. The primary objective of this study was to establish such a model. To accomplish this objective, quantitative data were collected from completed construction projects in the process industry. Methods of analysis. Each of the respondents was asked to assign a one-to-five rating for each of forty-five potential drivers of estimate accuracy for a given estimate. The data were analyzed using factor analysis and regression analysis. The factor analysis was used to group the forty-five elements into eleven orthogonal factors. Regression analysis was performed on the eleven factors to determine a suitable model for predicting estimate accuracy. The resulting model, known as the Estimate Score procedure, allows die project team to score an estimate and then predict its accuracy based on the Estimate Score. In addition, a computer software tool, the Estimate Score Program, or ESP, was developed to automate the Estimate Score procedure. Findings and conclusions. The regression analysis identified five of the eleven factors that were significant at the α = 10% level. The five factors, in order of significance, were basic process design, team experience and cost information, time allowed to prepare the estimate, site requirements and bidding and labor climate. These five factors represent twenty-three of the forty-five elements and together account for almost seventy-six percent of the Estimate Score.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Oberlender, G D |
| Uncontrolled Keywords: | accuracy; project team; bidding; cost information; factor analysis; regression analysis; construction project; estimator |
| Index terms: | cost information, factor analysis, capital project, project team, regression analysis, estimate, cost estimate, manager, process design, accuracy, estimator, quantitative method, construction project, program, bidding |
| Subjects: | data analysis and analytics, bidding, financial and cost management, project delivery, strategic project management, profession, design methods, practitioner, statistical analysis, accounting and finance, software systems, production management, professional development |
| Topics: | Cost Management, Project Management, Research Practice, Information Management, Roles and Professions, Procurement, Design Practice, Digital Applications |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here