Abstract
The main subject of this research is to develop a forecast and mitigation model of schedule and cost performance during a detailed engineering stage of offshore engineering, procurement and construction (EPC) projects. The weight factors of major elements in detailed engineering completion rating index system (DECRIS) were measured using a fuzzy inference system (FIS) and an analytic hierarchy process (AHP). At five key engineering milestones, from an EPC contract being awarded to the start of construction, detailed engineering maturities were assessed in fourteen historical offshore EPC projects using the DECRIS model. DECRIS cutoff scores for successful project execution were defined at the key engineering milestones. A schedule and cost performance was forecasted and validated through comparison of DECRIS and other models using statistical confidence of a fuzzy set qualitative comparative analysis (fsQCA) and a regression analysis. As a mitigation method for engineering risks to EPC contractors, engineering resource enhancement is recommended for trade-off optimization of cost overrun using a Monte Carlo simulation. The main contribution of this research is that EPC contractors could continuously forecast construction costs and schedule performance utilizing the DECRIS model, and could review the adequacy of engineering resources, assessing the trade-off between said resources and cost/schedule risk mitigation.
Highlights
International oil prices are, as of 2014, experiencing a downward trend which was caused by a diversification of oil mining and a reduction in oil demand due to the global economic recession.The low point was experienced in 2016, and is currently rebounding in correlation with growth in the global economy, though it has yet to fully recover
The last step consists of verification of the statistical significance using regression analysis and
The results suggest that the prediction performance of the detailed engineering completion rating index system (DECRIS) model using analytic hierarchy process (AHP) and fuzzy inference system (FIS) is stronger than the existing simple statistics model
Summary
The low point was experienced in 2016, and is currently rebounding in correlation with growth in the global economy, though it has yet to fully recover. These figures depict an industry that is currently.
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