A Study On Evaluation Model And Mitigation System For IT Outsourcing Risk Of Enterprise

IT outsourcing of enterprise has been developing fast during last decade with great risk involved. It inherits natureally risks of IT and possesses unique risks, on which attracts many research interests and achievements. However, there still exists space for improvement in the analysis of risk mechanism, the approach of evaluation and the strategy of control. Aiming at the problems mentioned above, this paper, after reviewing on the research development on IT outsourcing risk of enterprise, studies risk mechanism, and provides group decision making model of evaluation and the strategy of control. The study, based on Variable Precision Fuzzy Rough Set(VPFRS), combining Transaction Cost Theory(TCT) and Fuzzy Group Decision-making(FGDM), provides a comprehensive and flexible methodology for study on IT outsourcing risk in enterprise.Aiming at the complexity of forming mechanism and mutual relationship between IT outsourcing risk of enterprise, the thesis initializes to study risk mechanism. Based on TCT, the thesis proposes a two layer transferring mechanism of risk sources (the set of risk drivers) and risk, analyzes risk drivers such as Asset Specificity and five main risks such Lock-in, and discusses the principle of function and relationship between the two components above. The analysis offers more convenience and accuracy for the analysis, evaluation and control of risk, namely, a firm foundation for subsequential work.In order to solve the complexity, uncertainty, amguity and subjective bias in IT outsourcing risk evaluation, a new integrated model is proposed to evaluate IT outsourcing risk of enterprise, which is called Fuzzy Rough Group Decision-Making (FRGDM). On the basis of recent work, the model decreases the dimension of condition feature and derives the weight of features directly from fuzzy decision table, then transforms the semantic evaluation given by evaluators into triangle fuzzy numbers, finally rates and ranks alternatives' aggregative risk with FGDM. The model integrates the advantage of FGDM and FRST, improves the reliability and efficiency, which is verified by the case of the choice of outsourcing projects in a nuclear power station.For the purpose of further improving the robustness, reliability of the evaluation of IT outsourcing risk of enterprise, under variable admissible inclusion error is discussed and a new integrated model, Variable Precision Fuzzy Rough Group Decision-making (VPFRGDM) is proposed. First of all, under a given u, the model rates and ranks aggregative risk of alternatives with fuzzy Technique for Order Performance by Similarity to Ideal Solution(TOPSIS) in FGDM. Finally, then evaluates the risk on the whole admissible inclusion error interval to optimize decision-making. The model enhances the reasonableness of FGDM, further improvs the robustness and reliability of the evaluation, and improves the efficiency with better flexibility and comprehensiveness to choose the best precision level in IT offshore outsourcing risk decision-making.In order to improve the efficiency and effectiveness of countermeasures to alleviate IT outsourcing risk in enterprise, risk mitigation system is also studied. First of all, the thesis summarizes and refines literal developments in IT outsourcing risk of enterprise mitigation system, further categorizes the mitigation system into two subsystems, namely, main and complementary. Then, for the first time to our best knowledge, the thesis studies how the mitigation system functions on risk drivers instead of risks, and finally instructs the process of implementing the dynamic mitigation system. The system improves the pertinency and effectiveness, fit for providing a foundation for establishing the research methodology on the mitigation system for IT outsourcing risk of enterprise.Appropriate for the complex and dynamic nature of IT outsourcing risk, aiming at the less emphasis on knowledge, an integrated spiral monitoring model based on VPFRS is proposed. The model take advantage of Knowledge Discovery in Database(KDD)method based on VPFRS, which digs rules from historical data accumulated through two approaches, namely, rules digging approach under given u and the best precision interval. Since the KDD method is capable of finding rules easily understood from historical data, better dealing with errors and robustness, the monitoring model could help decision-makers judge risk condition more accurately and efficiently during the IT outsourcing process. The value of the model is also verified by the case.