Intelligent decision making and knowledge management in engineering project management procedures

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Most industries have standard values that are applied to cost estimates to reflect uncertainty early in the life of a project. Much research has focused on our failure to properly reflect real project conditions and to accurately estimate costs, schedules, and risks, especially for infrastructure and industrial megaprojects. A study of 63 international mining and smelting projects suggested that one out of thirteen projects had cost overruns 100% or higher. Public infrastructure projects typically fair worse. This project aims to develop a cognitive framework in which data and expert knowledge are integrated to provide support during the process of estimating capital expenditure and contingency costs early in the project phase with the goal of improving cost performance. Cognitive computing refers to computer systems built to simulate human thinking and reasoning using artificial intelligence (AI). Improving the estimates for expenditures and contingencies could have a major positive impact on the well-being and profitability of megaprojects. The systematic and accurate estimation of capital expenditures and contingencies could identify and help mitigate risk factors that drive up costs, enhance financing of the project for the client, and create higher reliability and competency in the marketplace.****