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Title: Development of a project complexity assessment method for energy megaprojects
Author: Kian Manesh Rad, Ehsan
ISNI:       0000 0004 6061 0564
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2016
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Megaprojects are characterised by their large-scale capital expenditure, long duration and significant levels of technical and process complexity. Empirical data show that megaprojects in the energy sector experience alarming rates of failure, such as cost overruns, delays in completion and production shortfalls. One of the main causes of failure is their high level of complexity and the absence of effective tools to assess and manage it. Project complexity has received increasing attention in recent years, both in academia and the industry. However, there is still a lack of consensus on a clear definition for ‘project complexity’ or a comprehensive list of complexity indicators, specifically for energy megaprojects. Furthermore, there is also a lack of a widely accepted assessment method to measure project complexity in a quantitative manner. This study is carried out in response to these problems. First, it develops a taxonomy of project complexity indicators on the basis of a comprehensive review and synthesis of existing literature. It includes 51 internal and external Project Complexity Indicators (PCIs) in a logical hierarchical structure; these indicators specify the aspects that need to be measured when assessing project complexity. Second, weights for all indicators are established through an integrated Delphi-AHP method, with the participation of 20 international experts. Finally, the study specifies Numerical Scoring Criteria (NSCs) for all indicators based on a synthesis of existing knowledge about megaprojects. The criteria specify the scoring thresholds, on a 1-5 scale, for each indicator. These three components constitute a new Project Complexity Assessment (PCA) method, which is implemented as a spreadsheet PCA tool. The developed tool allows a project team to assess and score their project in each of the PCIs against the defined criteria. It then calculates two separate complexity indices for internal and external factors; the results indicate the complexity level of the project. Complexity profiles are also produced to illustrate the complexity scores of different categories of PCIs. The PCA method is tested using an energy megaproject case study. The results demonstrate not only that the tool can help a project team understand the complexity of their project, but also it can help the team to develop appropriate complexity management strategies by comparing the assessment results of different projects.
Supervisor: Sun, Ming ; Bosche, Frederic Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available