The projects in oil and chemical (O&C) industry often experience problems during their execution, because of those problems, some of the project ends with large cost and schedule overruns. The poor performance of projects not only affects the strategic objective of project’s owner but also poses a dual threat to engineering and construction (E&C) companies. They negatively affect their profit margins and their business objectives. Given the strict budget constraints imposed by the present global economic situation, owners and stakeholders expect their projects to be delivered cost effectively and efficiently. Therefore, it is important for E&C companies to strive for improvement in their project management practices. The current thesis research is a step in direction to introduce a new concept for improvement in performance management practices. For that purpose, the research introduces “early detection of project problems” as the main instrument and uses the quantitative information from past project to develop a body of knowledge and first conceptual model to predict the future performance of projects at their early stages. The research is conducted in five phases, the first phase of the research explores O&C project and their performance management practices. Based on the gathered knowledge via literature study and available information, the main research question is formulated as “How can future problems and performance of a current O&C project be predicted at early stages using knowledge and experience from past projects in an EPC environment?” Thereafter, a series of sub questions were formulated aimed to answer the above-mentioned research question. The later part of the first phase developed a structured research approach and research methods. In the second part of the research, efforts were directed to find the so-called “early warnings” of problems. To identify the early warnings, two main sources were explored, literature and experts from O&C project industry. Each investigation into respective sources resulted into number of early warnings. Each identified early warning was evaluated on selection criteria with three selection parameters. After the careful evaluation, the following ten early warnings were selected. ID Early warning indicator LES Lack of understanding of project execution strategy among project team PTE Project team lacks experience required for the project COC Conflicts between owner and E&C contractor NCO Numbers of change orders CCO Cost impact of changes FED Percentage of missing information in FEED package PH Growth in process man-hours PS Delay in process engineering CE Change in concurrency level between process and piping engineering DPO Delay in issuance of purchase orders The selected early warnings were carried to the third phase of the research, in which four detailed case studies were performed to have observatory evidence. The case studies in this phase consisted of four project with different performance levels. The difference in performance levels of case projects set the contrast in which the predictive capability of early warnings could be observed. The case study investigation found that there is a relationship between early warnings, project problems and project performance. After obtaining the observatory evidence, the fourth phase of the research adopted a purely quantitative approach and studied the behavior of early warnings in a relatively larger set of past projects. Subsequently correlation analysis was performed to find correlations between early warnings and final project outcomes (which collectively asses the project performance). The quantitative analysis did present interesting and encouraging results. The main results are mentioned as follows: I. Early warnings do behave differently in case of poor and good performance projects, few in terms of their absolute value and few in their incremental changes. II. Correlations do exist between EWI and project outcomes, however not all the EWI found to be correlated with all project outcomes. III. The EWI indicators does show a dynamic quantitative relationship with project outcomes over engineering duration of the project Using the results from quantitative analysis, an attempt is made in the last phase of this research for the development of prediction model, which can predict the future performance of projects. The results of pilot prediction model were analyzed and compared with forecasts made via traditional forecasting methods. The comparison of forecasts found that prediction model does make prediction that is more accurate. However, there are errors with-in prediction models. In addition, the external validation of model suggested limited reliability and accuracy of pilot model. The dataset used for quantitative analysis and building of prediction model is relatively small and limit the generalization of findings. Therefore, to have a more accurate prediction in good projects, a dataset is required which contains a balance of Successful and less than successful performance projects. Despite the smaller dataset, the findings and approaches presented in this research can be used to build a useful model and subsequently applied in O&C project industry. A set of insights and recommendations (short term and long term) has been made for Fluor to implement the findings of this research to develop an operational performance prediction system. The research possibly has following main contributions to scientific and industry. Contribution to scientific community I. A shift from reactive project management to proactive project management II. A new and constructive role of past projects Contribution to O&C project industry I. An approach, which facilitate the early detection of future potential problems II. An approach to capitalize on past projects to improve project performance management