Decision making in the front end loading (FEL) stage of project management.

Lawani, Ama; Flin, Rhona; Adedokun, Racheal; Benton, Peter

Abstract

Front End Loading (FEL) is a fundamental work process in project management, undertaken before project authorisation or sanction. The main aim of FEL is to provide a complete definition for the capital project. Hence, the goal is to optimise the possibility for a successful execution that meets the project's objectives, including cost, schedule and operability. This stage is where the degree of uncertainty is highest and, if properly conducted with optimal decision making, it maximises the potential for project success. Despite projects falling behind schedule, exceeding estimated budgets, or failing in the upstream sector of the UKCS oil and gas industry, there is a recognition that projects are expected to go beyond traditional constraints of time, cost and quality to deliver value. Therefore, it is significant to understand project decision-making processes in this context. Research reveals that analytical and intuitive methods are used in project management decision-making. However, the focus has been on classical analytical approaches, with less attention on intuitive methods. This presentation will provide an account of a preliminary study undertaken by Aberdeen Business School, funded by the Engineering Construction Industry Training Board.

Citation

LAWANI, A., FLIN, R., ADEDOKUN, R. and BENTON, P. 2022. Decision making in the front end loading (FEL) stage of project management. Presented at the Association for Project Management seminar on decision making in the FEL stage of project management, 31 March 2022, Aberdeen, UK.

• Presentation Conference Type: Presentation / Talk
• Conference Name: Association for Project Management seminar on decision making in the FEL stage of project management
• Start Date: Mar 31, 2022
• Deposit Date: Apr 6, 2022
• Publicly Available Date: Mar 6, 2025
• Peer Reviewed: Peer Reviewed
• Keywords: Project management; Decision making; Oil and gas industry
• Public URL: https://rgu-repository.worktribe.com/output/1609112