Improved methods for managing megaprojects
Author(s)Minelli, Paolo,Ph. D.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering.
Michael W. Golay.
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Nuclear power is in danger of fading away as a signicant source of energy supply in some parts of the world unless it becomes more competitive. The cost of nuclear energy largely depends on capital cost because nuclear power plants are very expensive to design and build and relatively cheap to operate. The capital cost of nuclear power plants is literally dominated by factors other than those of physical equipment, such as project preparation, site preparation, engineering, planning, installation, and management. Interestingly, project planning, monitoring, execution and management is where too often the nuclear industry has failed in the past. As of 2014, the average time and cost overrun for nuclear projects worldwide are 64% and 117.3%, respectively. There is evidence showing that there is something that we fundamentally do not understand (or represent) in project planning and management. This work is based on the premise that nuclear projects are megaprojects.As such, they are complex systems that are (1) constantly changing, (2) tightly coupled, (3) governed by feedback, (4) non-linear, (5) history-dependent, (6) self-organizing, (7) adaptive, (8) characterized by tradeoffs, (9) counter intuitive, (10) policy resistant. Hence, traditional project management methods alone are insufficient. An alternative approach to reduce cost and uncertainties of nuclear projects is to turn them into more standard projects, in terms of scope, complexity and capital at risk. For example, the nuclear industry is pursuing the development of micro-reactors, a type of plug- and-play nuclear batteries that would be two orders of magnitude smaller in physical size, wholly manufactured and fueled in a factory, and transported to the site within standard-size freight containers, requiring minimal site excavation and preparation.This work develops an improved framework for managing megaprojects, estimating their value, and making project/design decisions involving numerous stakeholders, multiple competing objectives, and substantial uncertainty. The framework is built on two pillars: a System Dynamics (SD) model and a probabilistic Discounted Cash Flow (DCF) model. The former focuses on design and construction, while the latter focuses on operations. These two models are consistent with each other and they are run sequentially. To demonstrate its feasibility and appreciate its benefits, the SD-DCF approach is applied to a real-world case study, e.g. an ongoing project in North America based on a marine nuclear power plant entirely built in a shipyard and towed to the site upon completion. A multi-objective decision making problem is framed to illustrate the importance of a solid decision management process in megaprojects.270 dierent projects are derived from the combination of six high-level design/project choices: plant's capacity, deployment concept, flexibility, overlap between design and construction, level of efforts spent on FEED, size and availability of management team. The projects are simulated using the SD and DCF models, represented on trade spaces and finally evaluated against success objectives to derive general policy insights. This framework represents a synthesis of management methods that was not practically avail- able before. This work documents the development of the models, it shows why they should be used, it applies them to an actual case study hence providing a real-world application, and it makes the method credible, publicly available and convenient to adopt.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, February, 2020Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 192-202).
DepartmentMassachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology
Nuclear Science and Engineering.