Integrated computer modeling and stochastic project management

Student thesis: Master's Thesis

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Author(s)

  • Chi Kai LIANG

Detail(s)

Awarding Institution
Supervisors/Advisors
Award date28 Aug 1997

Abstract

Studies have shown that the traditional techniques formulated around the deterministic project management paradigm are unsatisfactory for the management of projects involving varying degrees of risks and uncertainties. A survey was so conducted with a view to ascertain the current project management practices and also to identify the uncertain factors which may impinge upon the project performance within the context of Hong Kong and the Pearl River Delta area. The results of survey show that a great majority of project activities take place, not in the traditional form of large scale projects, but rather in the form of relatively small to medium sized under taking in a wide range of organizations. Also, most of the projects are managed in multiple and multi-locational mode and often have to share a common pool of resources. So, the management of such projects become more complex and demanding because of the additional uncertainty involved. There is therefore a need for the development of a coherent and pragmatic project management methodology, in order to address such risks and uncertainties inherent in such types of projects. The premise of this research contends that such a methodology would best be achieved within the framework of stochastic project management. Thus, this research proposes a computer-integrated stochastic project management methodology which utilizes a number of probability distributions (namely the beta, the exponential, the gamma, the normal, the triangular, the uniform and the Weibull), as a means of modeling the associated risks and uncertainties within a project environment. The proposed COSPROM (acronym for amputerized Stochastic PROject Management) methodology incorporates the various probability distributions within a Windows-based software package (Microsoft Project for Windows 4.0) in such fashion as to facilitate a more effective planning, scheduling and controlling of a project in a stochastic environment. The design of the architecture of the COSPROM methodology aims to enhance the centralization and distribution of information on various patient project parameters such as activity durations, their associated costs, the resources utilized, etc., so that the project manager is better informed of the progress status and the implications of any variances on decisions/actions taken. The major features of COSRPOM that are not found in the present stochastic project management tools, include the capability to simulate all the project variables (e.g. tasks' duration, costs, resources demand level, etc.) and handle multiple projects simultaneously, the function of steady-state monitoring to reduce the simulation time, and a database project management system for storing and analyzing the past project performance record. Such features of COSPROM improve the effectiveness of project management by enhancing the accuracy simulation outcome, reducing the simulation time and providing a better decision support for user to choose the most appropriate probability distribution and input parameters. In addition, the present version of COSPROM also has various user-friendly features, such as most of the inputs and event selection can be carried out by using the mouse, an on-line help function, etc. The COSPROM methodology has been tested using an actual industrial case study involving a hospital re-location construction project in Hong Kong. The test result strengthens our believe that COSPROM would give the project manager a valuable foresight, in his/her quest to efficiently and effectively generate optimal project schedules (shortest possible project completion times, consistent with minimal project total costs and the most prudent resources utilization profiles), well in advance of any operational/ contractual environments. Such results have the potential to effect considerable cost savings in stochastic project management environment.

    Research areas

  • Project management, Computer simulation