Renewable Energy
About Project Cost Benchmarking
Benchmarking of project and process equipment costs using the project close out information offers a practical way to establish realistic targets for cost and schedule for your next oil and gas projects.
Our method of collecting and analyzing upstream projects and also equipment families data working together with our client means we hold the most in-depth database of completed project costs, durations and technical parameters in the industry.
We do benchmark upstream project performance on a range of unique cost, schedule, technical and project complexity metrics.
We do benchmark project performance on a range of unique cost, schedule, technical and project complexity metrics.
Data Analytics & Cost Benchmarking Services
Data analytics and Cost Benchmarking are the often complex process of examining big data to uncover information — such as hidden patterns, correlations, market trends and customer preferences — that can help organizations make informed business decisions. On a broad scale, data analytics technologies and techniques give organizations a way to analyze data sets and gather new information. Business intelligence (BI) queries answer basic questions about business operations and performance. Big data analytics is a form of advanced analytics, which involve complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by analytics systems.
Organizations can use data analytics systems and software to make data-driven forecasts that can improve cost items-related outcomes. The benefits may include more accurate assessments, customer personalization and improved operational efficiency. With an effective strategy, these benefits can provide competitive advantages for Cost Estimating organizations.
Project Cost Benchmarking combined with data analytics together represent the most powerful method to make accurate the process of Project Cost Estimation.
Cost estimator Professionals together with data analysts, data scientists, predictive modelers, statisticians and other analytics professionals collect, process, clean and analyze growing volumes of structured transaction data as well as other forms of data.