Level: Advanced
TCM Section(s):
7.2. Schedule Planning and Development
10.4. Project Historical Database Management
Venue: 2023 AACE International Conference & Expo
Abstract: Big data and machine learning are increasingly being used to solve some of the world’s greatest problems, one of these being the global need for better project management to increase efficiencies, and reduce costs and negative impacts during construction. This paper presents a unique data-driven approach to enable improved project scheduling and execution by predicting future project risk. This has been made possible over the past few years thanks to the fact that machine learning algorithms are now able to process schedule data at an unprecedented rate. Today the authors have data from over 500,000 project schedules, containing 252M activities and representing over $1.5T of capital deployed (the largest volume of project data in the world). This paper outlines key findings from the research conducted on this dataset, including exciting new developments in project forecasting, turning backwards-looking KPIs into forward-looking ones, as well as insights into how projects around the world have performed and are likely to continue to perform.
TCM Section(s):
7.2. Schedule Planning and Development
10.4. Project Historical Database Management
Venue: 2023 AACE International Conference & Expo
Abstract: Big data and machine learning are increasingly being used to solve some of the world’s greatest problems, one of these being the global need for better project management to increase efficiencies, and reduce costs and negative impacts during construction. This paper presents a unique data-driven approach to enable improved project scheduling and execution by predicting future project risk. This has been made possible over the past few years thanks to the fact that machine learning algorithms are now able to process schedule data at an unprecedented rate. Today the authors have data from over 500,000 project schedules, containing 252M activities and representing over $1.5T of capital deployed (the largest volume of project data in the world). This paper outlines key findings from the research conducted on this dataset, including exciting new developments in project forecasting, turning backwards-looking KPIs into forward-looking ones, as well as insights into how projects around the world have performed and are likely to continue to perform.