مدیریت ریسک های پروژه های مهندسی کلان با استفاده از شبکه ی باور بیزی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|722||2009||8 صفحه PDF||24 صفحه WORD|
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پیشنهاد می کنیم کیفیت محتوای سایت خود را با استفاده از منابع علمی، افزایش دهید.
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 3, Part 2, April 2009, Pages 5880–5887
2- بازنگری تالیفات جدول
1. نمونه هایی از پروسه ی مدیریت ریسک های پروژه
2.2- شبکه ی باور بیزی
3- شیوه ی مدیریت ریسک پروژه با استفاده از شبکه ی باور بیزی گام
1. طبقه بندی و شناسایی ریسک ها نمودار
1. شیوه ی مدیریت ریسک های پروژه با استفاده از شبکه ی باور بیزی گام
2. ارزیابی ریسک برای شبکه ی باور بیزی گام3. ایجاد شبکه ی باور بیزی گام
4. کنترل ریسک گام
5. ارزیابی مجدد ریسک جدول
2. ماتریس ریسک برای شبکه ی باور بیزی
جدول3. 26 گونه ی ریسکی مهم در پروژه های کشتی سازی
4- به کار گرفتن شیوه ی مدیریت ریسک های پروژه با استفاده از شبکه ی باور بیزی
4.1- شناسایی ریسک ها و طبقه بندی آنها
4.2- ارزیابی ریسک برای شبکه ی باور بیزی جدول
4.ویژگی های پاسخ دهندگان 4.3- ایجاد شبکه ی باور بیزی شکل
2. شبکه ی باور بیزی برای شرکت های بزرگ کشتی سازی.
4.4- کنترل ریسک جدول
5. گونه های ریسکی مهم برای شرکت های بزرگ کشتی سازی جدول
6. گونه های ریسکی مهم در شرکت های کشتی سازی متوسط جدول
7. خلاصه ی بررسی حساسیت برای شرکت های بزرگ کشتی سازی جدول
8. خلاصه ی بررسی حساسیت برای شرکت های کشتی سازی متوسط
4.5- ارزیابی مجدد ریسک ها جدول
9. نتایج ارزیابی مجدد ریسک های مربوط به اجرای پروژه برای شرکت های بزرگ کشتی سازی جدول
10. نتایج ارزیابی مجدد ریسک های مربوط به اجرای پروژه برای شرکت هایمتوسط کشتی سازی
5- نتیجه گیری و بحث
This paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. Differences of project performance risks between large-scale and medium-sized shipbuilding companies were identified. Exceeding time schedule and specification discontent were more important to large-scale shipbuilding companies, while exceeding budget and exceeding time schedule were more important to medium-sized shipbuilding companies. The change of project performance risks was measured by risk reduction activities of quality management, and strikes at headquarters and subcontractors, in both large-scale and medium-sized shipbuilding companies. The research results should be valuable in enabling industrial participants to manage their large engineering project risks and in extending our understanding of Korean shipbuilding risks.
Project risk management, one of the main subjects of project management (Raz & Michael, 2001), is the planning, organization, monitoring and control of all aspects of a project and it consists of risk identification, risk qualification, risk response development, and risk response control (Saynisch, 2005). Miller and Lessard (2001) pointed out that understanding and managing project risks in large engineering projects are challenging tasks at the early phase. The failure of large engineering projects has highlighted the importance of risk management mainly in the defense, construction and oil industries due to the serious damages that may be incurred (Williams, 1995). Active research has investigated process modeling and the methodologies of project risk management, in order to develop a systematic approach and integrated methodology of project risk management (del Cano and de la Cruz, 2002 and Raz and Michael, 2001). The use of diagrams such as cause and effect diagram and influence diagram is one of the methodologies for project risk management. A diagram is suitable for the modeling of conditional probability relationships among risks, and is useful when handling complex problem. However, it is not easy to construct relationships and it is more complex than intuition-based analysis, so it has not been applied to project risk management as a widely used methodology (Han and Diekmann, 2001, Lyons and Skitmore, 2004, Raz and Michael, 2001 and Simister, 1994). A Bayesian belief network is a graphical model that presents probabilistic relationships among a set of variables by determining the causal relationships among them (Heckerman, 1997). Because a Bayesian belief network constructs a cause and consequence diagram easily, it could be a suitable methodology for project risk management with systematic and integrated processes. Therefore, this study presents a project risk management procedure using a Bayesian belief network, applies this procedure to the Korean shipbuilding industry, and performs a project risk comparison between large-scale and medium-sized shipbuilding companies.
نتیجه گیری انگلیسی
This study has presented a large engineering project risk management procedure using a Bayesian belief network. The procedure was applied to the Korean shipbuilding industry, with the results demonstrating the difference of risks between large-scale and medium-sized shipbuilding companies, and the relationships among the risk items. For this, we deduced 26 risk items from a literature review and expert interviews, and conducted a survey analysis of 252 experts from 11 major Korean shipbuilding companies in April 2007. This study also identified the major risk items that affected project performance and measured the changes of project performance risks through the control activities of those risk items. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. The world shipbuilding industry has attracted increasing international attention since 2003 due to the rapid growth of international trade with China. Orders of ship-construction have increased by 236% during the last five years (Clarkson Research Service, 2007). This rapid growth of orders may increase the high risks of design manpower, design change, and raw material supply, and also exchange rate because the Korean shipbuilding industry is an export industry. In medium-sized shipbuilding companies, labor supply and capital supply were also the important risks because Korean medium-sized shipbuilding companies are extending their factories as they experience increasing orders. Risk reduction efforts are shared with shipbuilding companies and related industries since the major risks are associated with related industries. In the analysis of risk items related with project performance, the exceeding time schedule and specification discontent of large-scale shipbuilding companies were more important due to the relative stability of the capital funding ability of large-scale shipbuilding companies. However, exceeding budget and exceeding time schedule were more important in medium-sized shipbuilding companies. Large-scale shipbuilding companies’ risk items related with project performance were more various than those of medium-sized shipbuilding companies, and medium-sized shipbuilding companies experienced a higher risk for each risk item than large-scale shipbuilding companies did. The change of project performance risks was measured by the risk reduction activities of quality management, and strikes at headquarters and subcontractors in both large-scale and medium-sized shipbuilding companies. The present study results demonstrated the different effects of risk reduction activities between large-scale and medium-sized shipbuilding companies. Because large-scale shipbuilding companies produce more complex products such as liquid natural gas (LNG) carriers and ocean plants, it is difficult to reduce the specification discontent risk. Meanwhile, medium-sized shipbuilding companies experienced difficulties due to exceeding time schedule and exceeding budget risk reduction owing to their lack of management capability. The limitations of this study were the reliance on an expert survey to construct the Bayesian belief network and the consequent requirement for a great effort for data collection.