Systemic risk assessment: a case study

Project Risk Registers have been used extensively for many years. However, they do not account for the interaction between risks, for example, the occurrence of one risk exacerbating other risks or portfolios of risks being more significant than the sum of the individual risks. This leads to the need to consider ‘risk systemicity’ as a part of risk analysis. This paper reports on a specific case for a large multinational project based organization, one that the authors had been involved with in the analysis of a number of projects that had massive cost overruns. Following these analyses the organization was persuaded of the importance of risk systemicity. The organization therefore engaged the authors to develop a ‘Risk Filter’. This filter is a tool for identifying areas of risk exposure on future projects and creating a framework for their investigation. The ‘Risk Filter’ is now used on all projects ever since its introduction; by the end of May 2003 it had been used by nine divisions, on over 60 major projects, and completed by 450 respondents. It is also used at several stages during the life of a project to aid in the risk assessment and management of each project, and contributes to a project database.     

Using a risk-based approach to project scheduling: A case illustration from semiconductor manufacturing

This paper introduces a risk-based optimization method to schedule projects. The method uses risk mitigation and optimal control techniques to minimize variables such as the project duration or the cost estimate at completion. Mitigation actions reduce the risk impacts that may affect the system. A model predictive control approach is used to determine the set of mitigation actions to be executed and the time in which they are taken. A real-life project in the field of semiconductor manufacturing has been taken as an example to show the benefits of the method in a deterministic case and a Monte Carlo simulation has also been carried out.     

A comparative analysis of learning curves: Implications for new technology implementation management

New technology implementation projects are notoriously over time and budget resulting in significant financial and strategic organizational consequences. Some argue that inadequate planning and management, misspecification of requirements, team capabilities and learning contribute to cost and schedule over runs. In this paper we examine how learning curve theory could inform better management of new technology implementation projects. Our research makes four important contributions: (1) It presents a comparative analysis of learning curves and proposes how they can be used to help ERP implementation planning and management. (2) Based on empirical data from four ERP implementation projects, it provides illustrations of how managers can apply the curves in different project situations. (3) It provides a theoretical basis for empirical studies of learning and ERP (and other IT) implementations in different organizational settings. (4) It provides empirical justification for the development of learning curve theory in IT implementation.     

基于可信性理论的工程投标模糊风险评估

工程项目投资决策需要考虑承包商自身、业主、项目、竞争对手等方面的情况,需要对各种风险因素进行分析.结合我国当前建筑市场信息化程度不高,难以获取充分历史数据的特点以及国内外相关文献,提出了一种基于可信性理论的工程投标模糊风险评估方法,并给出了详细计算步骤.最后结合算例验证了方法的可行性和实用性.     

Managing Cost Overrun Risk in Project Funding Allocation

This paper discusses decision making of project funding allocation under uncertain project costs. Because project costs are uncertain and funding allocations may not necessarily match the costs required, each project is inherently subject to a cost overrun risk (COR). In this paper, a model is proposed in which project cost is treated as a factor with a probability density function. The decision maker then allocates the total funding to the projects while minimizing a weighted sum of mean and variance of the COR of the project portfolio. Some properties of project COR are derived and interpreted. Optimal funding allocation, in relationship to factors such as various project sizes and riskiness, project interdependency, and the decision maker’s risk preference, is analyzed. The proposed funding allocation model can be integrated with project selection decision-making and provides a basis for more effective project control.     

基于SEIRS的建设工程质量风险传递模型及仿真研究

工程建设过程、从业人员专业水平和信息存在较为突出的碎片化现象,导致了工程质量风险的传递。为揭示平行承发包模式下建设工程质量风险的传递效应和放大效应,利用传染病模型中的动力学原理,综合考虑工程质量风险传递的影响因素,构建了SEIRS的建设工程质量风险传递模型。仿真研究结果表明,建设工程在质量风险传递过程中存在阈值;质量风险传递阈值h1时,在质量风险传递阻碍因素的作用下,风险逐步衰减直至消失;质量风险传递阈值h<1时,在质量风险传递促进因素的作用下,风险会稳定存在于建设工程中,甚至危害工程的整体质量。研究结论为政府实现由直接监管向间接监管、由事前监管向事中事后监管的转变等提供理论支撑。     

基于风险网络的大型工程项目风险度量方法研究

风险度量是风险管理的基础,提出适合大型工程项目风险的风险度量方法.针对大型工程项目风险因素、风险信息、风险损失之间的复杂联系,构建大型工程项目风险网络,分别采用贝叶斯网络推理和网络层次分析法获得风险发生概率和风险量的估计,从而提出基于风险网络的大型工程项目风险度量方法.方法将风险损失量和风险损失发生概率进行了明确合理的结合,既可用于度量客观风险,也可用于度量主观风险.最后以槽菁头隧道施工风险管理为例说明该方法的具体应用步骤和效果.     

合作竞争条件下R&D项目投资决策分析

R&D项目的成功会为高新技术企业带来巨大收益,但是其研发过程中存在着技术风险、商业风险和突发风险等所带来的多种不确定性。为了应对这些不确定性,一些高新技术企业往往结成追求联盟整体利益最大化的成本共享联盟来进行R&D项目投资,然而由于不进行技术共享并争夺研发成功后的收益,联盟中的企业又会在合作的同时进行竞争,这就需要企业对R&D项目的估值非常的精确,以便在合作竞争条件下做出收益最大的最优投资决策。市场中两个合作竞争的高新技术企业各自拥有四种投资决策:成为市场先行者或跟随者,立刻与另一企业共同投资,与另一企业共同选择先等待时机再进行投资。本文以这两个高新技术企业为例,利用欧式期权理论量化了多种不确定性,建立相应的R&D项目投资决策数学模型,对R&D项目的投资时机和决策收益进行评估。当成本共享联盟整体收益最大时,得到的两个高新技术企业做出的投资决策即为在合作竞争条件下R&D项目投资中各自的最优投资决策,最后通过Shapley值的计算可对联盟中企业各自的收益进行合理分配。     

基于扰动模糊综合评价法的企业BI项目实施风险评估研究

商业智能(BI)系统能够帮助企业经营者进行智能化的决策,但在一个日益复杂的企业运营环境中,BI系统的实施如同ERP系统一样,面临着巨大的问题、压力和高风险.针对以上特性,提出了一种BI项目实施风险扰动模糊综合评价模型和方法,以上海新媒体A公司的BI项目为实例对企业BI项目实施中的各相关风险因素进行了分析,最优判别,得出了导致风险的主要因素,同时为BI实施风险规避策略提供科学依据.     

A bi-objective hierarchical program scheduling problem and its solution based on NSGA-III

In enterprise project management systems, a program at the tactical level coordinates and manages multiple projects at the operational level. There are close relationships between multiple projects in a program, which are typically manifested as shared resources and precedence relationships. Most research efforts have concentrated on the resource sharing by projects, while the precedence relationships between projects have yet to be comprehensively investigated. In this paper, a bi-objective hierarchical resource-constrained program scheduling problem proposed, where both resource sharing and precedence relationships between projects are considered in a distributed environment. The problem contains two different sub-problems at the operational level and the tactical level, and they are modeled in the same way as two bi-objective multi-mode scheduling problems. Shared resources are allocated from the tactical level to the operational level, and once they are allocated to a project, they can only be re-allocated to other projects once the current project is finished. Subsequently, a two-phase algorithm based on NSGA-III is developed. The algorithm runs at the operational level and the tactical level in turn. According to the Pareto fronts of projects that are submitted from the operational level, the bi-objective program planning at the tactical level is conducted under the constraints of precedence relationships and shared resources. The results of computational simulations demonstrate the satisfactory performance of the improved algorithm. By coordinating the local optimization of projects and the global optimization of the program in a hierarchical framework, the method proposed in this paper provides an effective integrated scheduling method for decision-makers at various levels of a program.

     

Decision-making in Government

The economic assessment of a research and development project is an important factor in planning and carrying out a project but its value depends on the forecasts on which it is based. The reasons why probabilistic methods are considered inappropriate when forecasting for novel projects are discussed and an alternative technique using credibility forecasts is described. Credibility forecasts for stages of a project are transformed into pairs of standardized “focus” forecasts from which are obtained two focus cash flow curves for the complete project. Two derived focus values of any suitable economic criterion are compared with a neutral value of the criterion to evaluate the attractiveness of the project and the degree of risk involved. The use of this technique for the initial assessment of a project and for reassessment at later stages is discussed and the procedure is illustrated by an example.     

Multistage decision-making fuzzy methodology for optimal investments based on experts’ evaluations

A new methodology of making a decision on an optimal investment in several projects is proposed. The methodology is based on experts’ evaluations and consists of three stages. In the first stage, Kaufmann’s expertons method is used to reduce a possibly large number of applicants for credit. Using the combined expert data, the credit risk level is determined for each project. Only the projects with low risks are selected.     

基于区间灰色区间直觉不确定语言集的工程项目风险辨识和评价

针对具有信息灰性、模糊性及语言描述性的工程项目风险问题,定义区间灰色区间直觉不确定语言集用于表达这些特征,结合多属性群决策理论和C-OWA算子,构建工程项目风险辨识和风险评价模型,以南京市纬三路过江隧道为例验证所建模型的可行性。结果表明,该模型可以利用区间灰度、区间隶属度和区间非隶属度以及不确定语言变量,更全面、更真实地表达工程项目的实际信息,得到更符合工程实际的风险辨识和评价结果,帮助项目管理者更准确地预知主要风险因素和风险状态。     

Scenario-based portfolio selection of investment projects with incomplete probability and utility information

In the selection of investment projects, it is important to account for exogenous uncertainties (such as macroeconomic developments) which may impact the performance of projects. These uncertainties can be addressed by examining how the projects perform across several scenarios; but it may be difficult to assign well-founded probabilities to such scenarios, or to characterize the decision makers’ risk preferences through a uniquely defined utility function. Motivated by these considerations, we develop a portfolio selection framework which (i) uses set inclusion to capture incomplete information about scenario probabilities and utility functions, (ii) identifies all the non-dominated project portfolios in view of this information, and (iii) offers decision support for rejection and selection of projects. The proposed framework enables interactive decision support processes where the implications of additional probability and utility information or further risk constraints are shown in terms of corresponding decision recommendations.     

Risk measurement and risk identification for BOT projects: A multi-attribute utility approach

This paper aims to identify and assess the potential risks faced by private sectors in holding BOT projects through the risk assessment model developed herein. The multi-attribute utility function and aggregation utility are established using the multi-attribute utility theory to evaluate the risk state of each uncertainty, and in turn to determine whether such an uncertainty is a risk factor or not from the negotiator group’s viewpoint. This model shows that the uncertainty is regarded as a risk factor only when the aggregation utility value is less than the average aggregation utility value when the outcome, attribute, and states of a factor as well as its occurrence probability are all independent. A numerical example is also utilized to demonstrate the application of the developed risk assessment model. Results of the numerical example reveal that the concession period of a BOT project is the primary risk factor whereas the foreign exchange ratio is the secondary risk factor. Accordingly, the concession period dominates the negotiation results of BOT projects.     

基于熵权物元的大型水利工程项目风险评价研究

大型水利工程项目是典型的复杂系统工程,为了准确预测大型水利工程风险等级,降低事故风险,论文从管理、技术、经济、政策、环境五个方面选取影响水利工程项目风险的14个指标,建立了水利工程项目风险评价指标体系.借助熵权法确定各项指标的权重,分析影响项目风险的主要因素,之后利用物元分析理论定量评价项目的风险状况.通过分析可知:跨国项目中,政策风险占比重最大;偏远地区,经济风险占比最高;管理风险与技术风险的权重在各个项目中接近且稳定.     

An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.     

An integrated decision support approach for project investors in risky and uncertain environments

In project investment decisions, it is often assumed that estimated values of project parameters are certain and they would not deviate by the time. However, project parameters normally change during a life cycle of the project. Therefore, an existence of a deviation or gap between forecasted values and actual values is inevitable. Because of the uncertainty of the future, forecasting the true and exact values of project parameters is almost impossible. In this study, an integrated decision support approach based on simulation and fuzzy set theory is proposed for project investors in risky and uncertain environments. This approach determines the risk levels of the projects and helps investors to make investment decisions. In the scope of the study, a flowchart is presented to guide to decision maker in different situations of information uncertainty that belongs to project parameter values. Via this flowchart, the values of project parameters can be chosen depending on how they are determined (deterministic, stochastic or fuzzy) by project analyst. Besides, calculating and analyzing the project risk in all possible situations would be easier. Illustrative examples are given to demonstrate the application of this approach.     

企业自主研发与合作创新的风险测度及组合优化

企业在整合内部创新要素进行自主研发的同时,也会寻求外部创新资源进行合作创新,当前同时从事多个R&D项目已成为常见的企业经营活动,如何在不确定条件下分析多个R&D项目投资的策略选择及风险优化,对于企业的长期发展具有重要意义。根据企业是否采取合作创新策略,可将其R&D项目分为自主研发与合作创新两类,以项目的研发成功率和投资收益率代表技术风险和市场风险,分别测度自主研发与合作创新项目的风险特性,并在此基础上构建企业R&D项目投资组合优化模型,以在自主研发与合作创新项目之间进行权衡取舍。结果表明,企业对于自主研发与合作创新项目投资组合的最优投资权重,主要取决于这两类组合的期望收益率、收益率方差、期望成功率以及两组合之间的协方差。企业可基于关键参数制定出最优的R&D项目投资组合选择策略,合理分配资金以达到风险最小化的投资目标。     

基于MIV-BP型网络实验的房地产项目风险识别研究

为了更准确更客观地识别房地产项目中的风险,为房地产项目投资决策提供科学依据和参考,有效地规避风险,本研究在BP神经网络 (Back-Propagation Neural Network)建模的基础上,采取MIV(Mean Impact Value)算法对BP神经网络模型进行变量筛选的网络优化和改良,从而形成新的优化后的MIV-BP(Mean Impact Value Back-Propagation Neural Network)神经网络,并以此用于评价房地产项目中的风险度以及各因素在风险度中的影响作用大小;同时选取目前相关的房地产项目数据进行仿真实证分析和验证。验证实验结果表明,MIV-BP型神经网络对于房地产项目风险度识别具有良好的适应性和准确性,实验结果客观,达到专家评价的要求,并在风险因素作用度分析上具有良好的应用价值。