It Industry Measure Of Economic And Management Indicators And Forecasting Model Empirical Study

There usually exists typical difficulties for domestic IT project management work, such as time slippage, cost overrun, low-grade quality and so on. Based on careful examination, the thesis derives the conclusion that one of the root causes for the failed IT project is lack of quantitative method to establish objectives for the IT projects. Hence, the main goal of the thesis is to establish an IT project management indicator framework and explore the quantitative dependant relationship among the project quality and management indicators.The thesis combines the qualitative and quantitative method to study the IT project quality and management indicator framework, the qualitative method is mainly used to establish the management indicator framework. According to the results of careful examination of the literatures in the field of project management and author's consulting practices in the field of IT quantitative project management, thesis explores three dominant IT project management models-PMBOK, ICB and CMMI. However, there is no definite guiding rule in these management models for establishing IT project management indicator framework. The thesis also analyses the industry indicator frameworks, such as ISO 15939, SEI indicator, ISBSG data questionnaire, SPR indicator and Bangalore SPIN indicator. Due to incompletency or lack of practice, these indicator frameworks are still not satisfying to describe the complete and practical management objectives for the IT project management. Therefore, the thesis designs a new framework for the IT project management indicator. Combining three factors, management concept of the indicator, data availability of the indicator and the important relation to the key indicator, the thesis suggests a 4-catogory 16-indicator framework. The suggested indicator framework is also based on project management triangle model, it encompasses 4 key indicators and other 12 affiliated indicators.The thesis uses quantitative method to study the dependency relationship among the indicators. The thesis assumes that there exist two types of dependant relationship, linear relationship and non-linear relationship. The linear model simplifies the indicator relationship and presents a simple and linear model in order to achieving better operation performance. However, the IT project management indicator relationship in the real world should be more complex and the relationship is non-linear. Therefore, the non-linear relationship among the indicator framework is the main content for the quantitative study method. Considering different features of the study objects, the thesis adopts two types of analysis model. Because there is usually lacking the IT system quality related data, the Bayesian Belief Network(BBN) model is selected for it can combine the pre-tested subjective information with post-tested objective information. The first step of modeling a BBN IT quality model is building a Basic BBN model, it can forecast the potential defect number. The second step of BBN modeling is to extend the basic BBN IT quality model to form the Extended BBN model, it introduces review node, testing node, usage frequency node to predict the potential defect number which will be found during the end user operation period. The BBN predicting model can also use the real defect number information and feedback it to the BBN model to adjust the corresponding probability of the pre-test condition. Therefore, the BBN model can optimize the model for predicating IT system defect number in a continuous manner.The thesis uses the Support Vector Machine (SVM) to study the IT project schedule indicator and effort indicator. The IT project schedule and effort related historical data are firstly normalized to meet the needs for SVM processing, then the data is divided up to 3 data files, including training sub-data, testing sub-data and predicting sub-data. Then the thesis adopts Radial Basis Function SVM to regress and predict the IT project schedule indicator and effort indicator。The thesis also analyses the sensitive degree among the indicators. Among the indicators which affect the IT project effort, the project size is the most important factor, the project team size is the second important. However, the contribution from project type, application type and the development language type are comparatively slight, even can be omitted. Project size contributes less to IT project schedule comparing with contribution to IT project effort, although it is still the most important one. The project team size and project type contribute similarly to the project schedule, the contributions from the application type and the language type are still very slight.Although the thesis applies different research methods, the research results should be linked together in order to provide information for IT project management decision work. Finally, the thesis also introduces author's consulting practices in the field of IT quantitative project management, points out that IT project management indicator framework study and application is indispensable to continuous development for IT industry.