Dynamic decision support for contingency management and allocation for construction projects

With the increasing size and complexity of construction projects, it is highly probable that the design and construction process will encounter problems, varying in different degrees in rework, delays, quality issues; all problems which translate into cost overruns. Cost overruns can be reduced by incorporating an appropriate amount of contingency into a project's budget. Contingency is included to provide the management team with the flexibility required to address risks and various deviations from the original plan. Contingency allocation have traditionally been estimated by adding a predetermined percentage of the budget to the total cost without consideration of the project characteristics, risks involved, or complexity of the project. Other methods include using an expert to estimate and allocate contingency and performing a risk and regression analysis, which are all static approaches that does not consider project dynamics. It is important to recognize the unique dynamics that exist in construction projects and their impact on contingency management. The objectives of this research are to explore the area of cost and contingency management and to present a dynamic decision support system for contingency allocation. The system dynamics methodology emphasizes the importance of viewing the management of contingency from a "systems perspective", so that better decision making about tradeoffs can be executed. System dynamics can produce a robust and holistic representation of the project providing a realistic project view. The dynamic decision support system for contingency allocation shows that certain project characteristics have a higher impact on contingency requirement. The results show that the complexity parameter has the highest impact on the contingency requirement. The desired quality also has a high impact on the contingency required. This research addressed the important issue of the causes of dynamic behavior in contingency allocation and management by building, testing and applying a dynamic simulation system of a multiple phase project. This research has contributed insights concerning the dynamics of projects in the area of contingency management, a tested framework for modeling projects based on demand for contingency, a systematic approach for project contingency analysis and a framework for improving the understanding of contingency allocation and management.