Research On Influence Factors And Measurement Of Mega Engineering Project Complexity

Engineering projects are one of the major vehicles that facilitate a country‘s fixed asset investment, as well as an important channel to adjust economic structures and the way the whole economy grows. Strategic goals of national importance, such as economic and social benefits, sustainable growth, and long-term prosperity, depend on major engineering projects. In recent years, with the segmenting in the construction sector, the dynamic project environment and ever increased fierce commercial competition make project complexity ubiquitous and thus challenge the conventional project management approaches. Therefore, construction management practitioners have to adopt innovative management approaches so as to keep up with the project complexity. In the project implementation process, there are many interweaving factors affecting the outputs. Project elements, such as natural resources, finance, human resource, technology and so on, are in a dynamic environment, which leads to the increased project complexity level and hence high uncertainty. Conventional project risk management theories cannot satisfy the needs of contemporary practices. In order to bridge such gap, a complexity approach can be an alternative solution and therefore how to analyse project complexity from a systems point view and how to influence it via efficiently controlling complexity emerge as a practical question to both academia and practitioners. So far, there are not sufficient researches in the field of project complexity and complexity measurement, making the research still at its beginning stage. Such a gap urges academics to view engineering project management with a complexity view. This thesis aims at researchin project compelxity and hence proposed innovative management approaches of project complexity, via capturing the future directions of the topic and integrating it with the macro environment.This thesis has reviewed previous literatures, researches and studies o n engineering project complexity and has attempted to investigate factors that affect project complexity from both static and dynamic perspectives. Firstly, the thesis explores project complexity from a static perspective. So far as shown in existing literatures, factors that affect project complexity are studied as isolated individuals and thus ignored the connectivity among factors. Based on such a static approach, this thesis uses a connectivity matrix to analyse the distances among complexity factors, including finance, cost, time, environment, and technology. The output holistically illustrates how multiple factors influence project complexity as a whole and how these factors interact with each other. Secondly, this thesis tries to explore project complexity from a dynamic angle. Factors that affect project complexity would have differentiated impact at different time points and therefore a static approach does need complementary measures to give a comprehensive picture of project complexity. In these regards, it is necessary to study and measure project complexity from a dynamic viewpoint. In this thesis, it adopts an evolutionary approach(known as phylogenetics) to understand how complexity factors change over time. As a result, the nature of project complexity can be understood with more accuracy and efficiency.Finally, the thesis analyses project complexity from the whole and sub-project level. For an engineering project, it is necessary to know how individual sub-project influences each other in the framework of a whole project. In the meantime, the whole would be better understood if the individuals were better understood. As shown in existing literatures, previous studies paid little attention to this area. Instead, they focus on the overall complexity of a project and often lead to increased risk and failed management.By establishing connectivity matrix, phylogenetic tree, and Markov Chain entropy model, this thesis proposed an integrated methodology to analyse how static factors influence project complexity, how dyanmic factors influence project complexity, and how to measure projject complexity. The methodology has been verified and validated using real world case studies. TConnectivity matrix outlines a five-factor framework of project complexity from a systems‘ point of view and it is the interactions among these factors that make project complex.The Markov chain entropy model demonstrates the dynamic nature of a complex system and thus measures its complexity level, whilst enabling a compari son of different complex projects. The phylogenetic approach can help one categorise complexity factors, trace the root of complexity, and predicts the possible evolution in the future.In general, the three quantitative approaches adopted in this thesis pe rceive project complexity from different but closely related perspectives. The topic is treated as a whole system and being both complement and supplement, it offers decision support to project managers, as well as a new approach to academics in the field of project management.