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Abdenour, J I (2021) A cost estimation model for improving the budget estimates of industrial plant construction projects, Unpublished PhD Thesis, , The George Washington University.
Adoko, M T (2016) Developing a cost overrun predictive model for complex systems development projects, Unpublished PhD Thesis, , The George Washington University.
- Type: Thesis
- Keywords: complex systems; cost overrun; predictive model; schedule; systems engineering; technology readiness levels; aerospace engineering; systems science
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/1750077665
- Abstract:
While system complexity is on the rise across many product lines, the resources required to successfully design and implement complex systems remain constrained. Because financiers of complex systems development efforts actively monitor project implementation cost, project performance models are needed to help project managers predict their cost compliance and avoid cost overruns. This dissertation presents a cost overrun predictive model for complex systems development projects. The dissertation is based on a research undertaken to develop the cost overrun predictive model using five known drivers of complex systems development cost: system performance, technology maturity, schedule, risk, and reliability. The dissertation demonstrates how large-scale system development project managers and systems engineers can use the model to support decision making aimed at achieving compliance with the Nunn-McCurdy cost overrun requirements. Sixty-nine aerospace and defense systems development projects were analyzed using logistic regression leading to the development of the predictive model. Model variables include system performance, Technology Readiness Levels (TRL), risk, schedule, and reliability. The final model predictability accuracy was 62.1% for significant cost overrun and 83.3% for no significant cost overrun respectively, within the statistical boundaries of the research. Overall, the model is inconclusive on 10 cases, predicts 29 cases as significant cost overruns and 30 cases as on budget. For the aerospace projects, the model is inconclusive 7.14% of the cases; predicts 35.71% of the cases as significant cost overruns; and predicts 57.14% of the cases as no significant overrun outcomes. For defense projects, the model is inconclusive 19.51% of the cases; predicts 46.34% of the cases as significant cost overruns; and 34.15% of the cases as no significant cost overrun outcomes. Therefore, the model predicts more cost overruns for the defense projects than for the aerospace projects. Specifically the model predicts approximately 36% significant cost overruns for aerospace projects and 46% for defense projects. The model identifies schedule and reliability as the key determinants of whether or not a large complex systems development project will experience cost overrun, within constraints of the data. Projects that achieve both the defined schedule and reliability thresholds will have the lowest level of probability for a cost overrun outcome. That is, projects that fail to meet the requirements of the schedule and reliability criteria are more likely to experience significant cost overruns, within the statistical boundaries of the model. Interestingly, the model demonstrates that the TRL threshold alone is not adequate for preventing a cost overrun. However, the interaction between TRL and system performance parameters decreases the probability of a cost overrun.
Alves, L F (2006) Stochastic approach to risk assessment of project finance structures under public private partnerships, Unpublished PhD Thesis, , The George Washington University.
Boyer, E J (2012) Building capacity for cross-sector collaboration: How transportation agencies develop skills and systems to manage public-private partnerships, Unpublished PhD Thesis, , The George Washington University.
Cho, S (2000) Sequential estimation and decision-making in project management: A Bayesian way and heuristic approaches, Unpublished PhD Thesis, , The George Washington University.
Farmer, C M (2018) Constructing program management offices for major defense acquisition programs: Factors to consider, Unpublished PhD Thesis, , The George Washington University.
Griffin, M G (2008) The lived experience of first line managers during planned organizational change: A phenomenological study of one firm in the residential construction industry, Unpublished PhD Thesis, , The George Washington University.
Innocent, M J F, Jr. (2018) Predicting military construction project time outcomes using data analytics, Unpublished PhD Thesis, , The George Washington University.
Kim, E (2000) A study on the effective implementation of earned value management methodology, Unpublished PhD Thesis, , The George Washington University.
Lounsbury, C R (1983) From craft to industry: The building process in North Carolina in the nineteenth century, Unpublished PhD Thesis, , The George Washington University.
Ngamthampunpol, D (2008) An assessment of safety management in the Thai construction industry, Unpublished PhD Thesis, , The George Washington University.
Park, J (2015) Essays on the delivery of public infrastructure projects: Empirical analyses on transportation projects in Florida, Unpublished PhD Thesis, , The George Washington University.
Schulte, W D, Jr. (1999) The effect of international corporate strategies and information and communication technologies on competitive advantage and firm performance: An exploratory study of the international engineering, procurement and construction (IEPC) industry, Unpublished PhD Thesis, , The George Washington University.
Shamma, E M (1988) A dynamic model for the growth of construction firms, Unpublished PhD Thesis, , The George Washington University.
Taku, A M (2021) Predicting modular efficiency in oil and gas capital projects using multi-criteria decision analysis, Unpublished PhD Thesis, , The George Washington University.
Zhou, G (2021) Machine learning-based cost predictive model for better operating expenditure estimations of U.S. light rail transit projects, Unpublished PhD Thesis, , The George Washington University.