Proje risklerinin sistem dinamik modellemesi

ABSTRACT Risk, defined as exposure to the possibility of economic or financial loss or gains, physical damage, or injury, or delay as a consequence of the uncertainty associated with pursuing a cause of action, is by nature subjective. The fast changing environmental conditions, information technology, and the complexity of the projects increase risk threat and make project management more susceptible to risks. This sensitiveness is also at its highest level in telecommunication projects, because there are various technologies and scientific techniques used in telecommunication projects increasing the uncertainty and complexity. Project risk dynamics are difficult to understand and control. Therefore managing project risks requires an approach supported by special tools and techniques. System dynamic (SD) modeling is a tool that covers a wide range of project management needs, by addressing the system issues that influence and often dominate the project outcome. With SD modeling approach we will be able to see all things as a whole. SD modeling also will enable us to build formal computer simulation of complex project risk dynamics, so we can identify and analyze probable risks within the project and respond to them. In the thesis project, we attempt to classify all project risks within the framework of PSO (People-System-Organization) concept. Five risk sectors are defined. 1. Social Risk 2. Economic Risk 3. Technical Risk 4. Political Risk 5. Ecological Risk Political and Ecological risk sectors are excluded and the first three sectors are defined as endogenous in the model. In Stock and Flow Map these three sectors are determined as main stocks. Modeling these three risk sectors stock and flow diagrams 46 risk variables are used. There are three main stocks and seven sub-stocks in the model. Moving from causal map to computer simulation we used the approach called NUMBER (Normalized Unit Modeling by Elementary Relationships), which enable us to limit the variable's values between 0 and 1. We needed to use this approach because we did not have the real data. After forming the equations we simulated the model with time bound which is assumed to be 60 months. Simulation results show that all main risk sectors increase in the initial times of the project. Then if all thins go well the risk levels begin to decrease but never zero. Economic Risk shows the highest level about 0.6 at the time 18 in the base model The highest levels of the other two main risk factors are about 0.3 at a determined time. Near the base model we defined an alternative model to which a new variable called Hiring New Staff is added. After simulating the alternative model it was observed that to introduce new personnel to an ongoing project increases risks levels and project cost, decreases productivity, quality and leads to a poor team relationship. The model can be simulated with different policies and designs. It enables us to see various situations of risk factors and other variables affecting risks. In this way, irrelevant risks can be eliminated, preventing unnecessary mitigating efforts. At the end of this study we are able to have a complete picture of telecommunication project risk dynamics with the help of an SD model computer simulation. This may help us in seeing risk behaviors during project time and formulating responses to risks for mitigating their impacts on telecommunication projects. XIV