本科毕业论文（设计）外文翻译
项目管理分析
院（系、部）名称：商务管理系
专业名称：财务会计教育（师范类）
学生姓名：
学生学号：
指导教师：
2013 年01月05日
河北科技师范学院教务处制
Project Risk Analysis
Chapter 1 Introduction
1.1 About this compendium
This course compendium is to be used in the course “Risikostyring is projector”. The focus will be on the following topics:
? Risk identification
? Risk structuring
? Risk modeling in the light of a time schedule and a cost mode l
? Risk follows up
We will also discuss elements related to decision analysis where risk is involved, and use of life cycle cost and life cycle profit models. The course compendium comprises a large number of exercises, and it is recommended to do most of the exercises in order to get a good understanding of the topics and methods described. A separate MS Excel program, pRisk.xls has been developed in order to assist numerical calculations and to conduct Monte Carlo simulation.
1.2 Definitions
Aleatory uncertainty
Variation of quantities in a population. We sometimes use the word variability rather than aleatory uncertainty.
Epistemic uncertainty
Lack of knowledge about the “world”, and observable quantities in particular. Dependency
The relation between the sequences of the activities in a project.
Observable quantity
A quantity expressing a state of the “world”, i.e. a quantity of the physical reality or nature, that is unknown at the time of the analysis but will, if the system being analyzed is actually implemented, take some value in the future, and possibly become known. Parameter
We use the term parameter in two ways in this report. The main use of a parameter is that it is a quantity that is a part of the risk analysis models, and for which we assign numerical values. The more academic definition of a parameter used in a probability
statement about an observable quantity, X, is that a parameter is a construct where the value of the parameter is the limiting value where we are not able to saturate our understanding about the observable quantity X whatsoever new information we could get hold of. Parameter estimate
The numeric value we assess to a parameter.
Probability
A measure of uncertainty of an event.
Risk
Risk is defined as the answer to the three questions [14]: i) what can go wrong? ii) How likely is it? And if it goes wrong, iii) what are the consequences? To describe the risk is a scenario
Risk acceptance
A decision to accept a risk.
Risk acceptance criterion
A reference by which risk is assessed to be acceptable or unacceptable.
Schedule
A plan which specifies the start and finalization point of times for the activities in a project.
Stochastic dependency
Two or more stochastic variables are (stochastically) dependent if the expectation of one stochastic variable depends on the value of one or more of the other stochastic variables. Stochastic variable
A stochastic variable, or random quantity, is a quantity for which we do not know the value it will take. However, we could state statistical properties of the variable or make probability statement about the value of the quantity.
1.3 DEFINITIONS
Uncertainty
Lack of knowledge about the performance of a system, and observable quantities in particular.
Chapter 2
Risk Management
Generally, risk management is defined (IEC 60300-3-9) as a “systematic application of
management policies, procedures and practices to the tasks of analyzing, evaluating and controlling risk”. It will comprise (I EC definitions in parentheses):
? Risk assessment, i.e.
–Risk analysis (“Systematic use of available information to identify hazards and to estimate the risk to individuals or populations, property or the environment”)
–Risk evaluation (“Process in which judgments are made on the tolerability of the risk on the basis of risk analysis and taking into account factors such as socio-economic and environmental aspects”)
? Risk reduction/control (Decision making, implementation and risk monitoring).There exists no common definition of risk, but for instance IEC 60300-3-9 defines risk as a “combination of the frequency, or probability, of occurrence and the consequence of a specified hazardous events”. Most definitions comprise the elements of probabilities and c onsequences. However, some as Klinke and Renn suggest a very wide definition, stating: “Risk refers to the possibility that human actions or events lead to consequences that affect aspects of what humans value”. So the total risk comprises the possibility of number (“all”)
unwanted/hazardous events. It is part of the risk analysis to delimit which hazards to include. Further, risk usually refers to threats in the future, involving a (high) degree of uncertainty. In the following we will present the basic elements of risk management as it is proposed to be an integral part of project management.
2.1 Project objectives and criteria
In classical risk analysis of industrial systems the use of so-called risk acceptance criteria has played a central role in the last two or tree decades. Basically use of risk acceptance criteria means that some severe consequences are defined, e.g. accident with fatalities. Then we try to set an upper limit for the probability of these consequences that could be accepted, i.e. we could not accept higher probabilities in any situations. Further these probabilities could only be accepted if risk reduction is not possible, or the cost of risk reduction is very high.
In recent years it has been a discussion in the risk analysis society whether it is fruitful or not to use risk acceptance criteria according to the principles above. It is argued that very often risk acceptance criteria are set arbitrary, and these do not necessarily support the overall best solutions. Therefore, it could be more fruitful to use some kind of risk evaluation criteria, rather than strict acceptance criteria. In project risk management we could establish acceptance criteria related to two types of events:
? Events with severe consequences related to health, environment and safety.
? Events with severe consequences related to project costs, project quality, project duration, or
even termination of the project. In this course we will have main focus on the project costs and the duration of the project. Note that both project cost and project duration are stochastic variables and not events. Thus it is not possible to establish acceptance criteria to project cost or duration directly. Basically, there are three types of numeric values we could introduce
in relation to such stochastic variables describing the project:
1. Target. The target expresses our ambitions in the project. The target shall be something we are striving at, and it should be possible to reach the target. It is possible to introduce (internal) bonuses, or other rewards in order to reach the targets in a project.
2. Expectation. The expectations are the value the stochastic variables will achieve in the long run, or our expectation about the outcome. The expectation is less ambitious than the target. The expectation will in a realistic way account for hazards, and threats and conditions which often contribute to the fact that the targets are not met.
3. Commitment. The commitments are values related to the stochastic variables which are regulated in agreements and contracts. For example it could be stated in the contract that a new bridge shall be completed within a given date. If we are not able to fulfill the commitments, this will usually result in economical consequences, for example penalties for defaults, or in the worst case canceling of the contract.
2.2 Risk identification
A scenario is a description of a imagined sequence or chain of events, e.g. we have a water leakage, and we are not able to stop this leakage with ordinary tightening medium due to the possible environmental aspects which is not clarified at the moment. Further the green movement is also likely to enter the scene in this case. A hazard is typically related to energies, poisonous media etc, and if they are released this will result in an accident or a severe event. A threat is a wider term than hazard, and we include also aspects as “wrong” method applied, “lack of competence and experience”. The term threat is also very often used in connection with security problems, e.g. sabotage, terrorism, and vandalism.
2.3 Structuring and modeling of risk
In Section 2.2 we have identified methods to identify events and threats. We now want to relate these events and threats to the explicit models we have for project costs and project duration.
2.3.1 Model for project execution time/schedule modeling
When analyzing the execution time for a project we will have a project plan and typically
a Gantt diagram as a starting point. The Gantt diagram is transformed into a so-called flow network where the connections between the activities are explicitly described. Such a flow network also comprises description of duration of the activities in terms of probability statements. The duration of each activity is stochastic
Variables, which we denote Ti for activity in a flow network we might also have uncertain activities which will be carried out only under special conditions. These conditions could be described in terms of events, and we need to describe the probability of occurrence of such events. Thus, there is a set of quantities, i.e. time variables and events in the model. The objective is now to link the undesired events and threats discussed in Section 2.2 to these time variables and events. Time variables are described by a probability distribution function. Such a distribution function comprises parameters that characterize the time variable. Often a parametric probability distribution is described by the three quantities L (low), M (most likely) and H high. If an undesired event occur, it is likely that the values of L, M and H will be higher than in case this event does not occur. A way to include the result from the risk identification process is then to express the different values of L, M and H depending on whether the critical event occurs or not. If we in addition are able to assess the probability of occurrence of the critical event, the knowledge about this critical event has been completely included into the risk model. Based on such an explicit modeling of the critical event, we could also easily update the model in case of new information about the critical event is obtained, for example new information could be available at a later stage in the process and changes of the plan could still be possible in light of the new information.
2.3.2 Cost modeling
The cost model is usually based on the cost breakdown structure, and the cost elements will again be functions of labor cost, overtime cost, purchase price, hour cost of renting equipment, material cost, amount of material etc. The probabilistic modeling of cost is usually easier than for modeling project execution time. The principle is just to add a lot of cost terms, where each cost term is the product of the unit price and the number of units. We introduce price and volume as stochastic variables to describe the unit price and the number of units. The price and volume variables should also be linked to the undesired events and threats we have identified in Section 2.2. Often it is necessary to link the cost model to the schedule model. For example in case of delays it might be necessary to put more effort into the project to catch up with the problems, and these efforts could be very costly. Also, if the project is delayed we may need to pay extra cost to sub-contractors that have to postpone their support into the project.
2.3.3 Uncertainty in schedule and cost modeling
As indicated above we will establish probabilistic models to describe the duration and cost of a project. The result of such a probabilistic modeling is that we treat the duration and cost as stochastic variables. Since duration and costs are stochastic variables, this means that there is uncertainty regarding the values they will take in the real project we are evaluating. Sometimes we split this uncertainty into three different categories, i) Aleatory uncertainty (variability due to e.g. weather conditions, labor conflicts, breakdown of machines etc.), ii) parameter or epistemic uncertainty due to lack of knowledge about “true” parameter values, and iii) model uncertainty due to lack of detailed, or wrong modeling. Under such thinking, the aleatory uncertainty could not be reduced; it is believed to be the result of the variability in the world which we cannot control. Uncertainty in the parameters is, however, believed to be reducible by collecting more information. Also uncertainty in the models is believed to be reducible by more detailed modeling, and decomposition of the various elements that go into the model. It is appealing to have a mental model where the uncertainty could be split into one part which we might not reduce (variability), and one part which we might reduce by thorough analysis and more investigation (increased knowledge). If we are able to demonstrate that the part of the uncertainty related to lack of knowledge and understanding has been reduced to a sufficient degree, we could then claim high confidence in the analysis. In some situation the owner or the authorities put forward requirements. Which could be interpreted as confidence regarding the quality of the analysis? It is though not always clear what is meant by such a confidence level. As an example, let E(C) be the expected cost of a project. A confidence statement could now be formulated as “The probability that the actual project cost is within an interval E(C) ± 10% should at least be 70%”. It is, however, not straight forward to document such a confidence level in a real analysis. The “Successive process (trinnvisprosessen)” [4] is an attempt to demonstrate how to reduce the “uncertainty” in the result to a certain level of confidence.
We also mention that Even [12] has recently questioned such an approach where there exist model uncertainty and parameter uncertainty, and emphasizes that we in the analysis should focus on the observable quantities which will become evident for us if the project is executed, e.g. the costs, and that uncertainty in these quantities represent the lack of knowledge about which values they will take in the future. This discussion is not pursuit any more in this presentation.
2.4 Risk elements for follow up: Risk and opportunity register
As risk elements and threats are identified in Section 2.2 these have to be controlled as far as possible. It is not sufficient to identify these conditions and model them in the schedule and cost models, we also have to mitigate the risk elements and threats. In order to ensure a systematic follow up of risk elements and threats it is recommended to establish a so-called threat log. The terms ?Risk Register…and?Ris k & Opportunity Register…(R&OR) is sometimes used rather than the term ?threat log.… A R&OR is best managed by a database solution, for example an MS-Access Database. Each row in the database represents one risk element or threat. The fields in such a database could vary, but the following fields seems reasonable: ? ID. An identifier is required in order to keep track of the threat in relation to the quantitative risk models, to follow up actions ET.
? Description. A description of the threat is necessary in order to understand the content of the problem. It could be necessary to state the immediate consequences (e.g. occupational accident), but also consequences in terms of the main objectives of the project, e.g. time and costs.
? Likelihood or probability. A judgment regarding how probable it is that the threat or the risk condition will be released in terms of e.g. undesired or critical events.
? Impact. If possible, give a direct impact on cost and schedule if the event occurs, either by an expected impact, or by L, M and H values.
? References to cost and schedule. In order to update the schedule and cost models it is convenient to give an explicit reference from the R&OR into the schedule and cost models. ? Manageability. Here it is descried how the threat could be influenced, either by implementing measures to eliminate the threat prior to it reveals it self, or measures in order
to reduce the consequences in case of the threat will materialize.
? Alert information. It is important to be aware of information that could indicate the development of the threat before it eventually will materialize. If such information is available we could implement relevant measures if necessary. For example it could be possible to take ground samples at a certain cost, but utilizing the information from such samples could enable us to choose appropriate methods for tunnel penetration.
? Measures. List of measures that could be implemented to reduce the risk.
? Deadline and responsible. Identification of who is responsible for implementing and follow up of the measure or threat, and any deadlines.
? Status. Both with respect to the threat and any measure it is valuable to specify the development, i.e. did the treat reveal it self into undesired events with unwanted consequences, did the measure play any positive effect etc.
2.5 Correction and control
As the project develops the R&OR is the primary control tool for risk follow up. By following the status of the various threats, risk elements and measures we could monitor the risk in the project. This information should of course be linked to the time and cost plans. If a given threat does not reveal in terms of undesired events, the time and cost estimates could be lowered and this gain could be utilized in other part of the project, or in other projects. In the opposite situation it is necessary to increase the time and cost estimates, and we need to consider new measures, and maybe spend some of the reserves to catch up in case of an expected delay. During the life cycle of a project it will occur new threats and risk elements which we did not foresee in the initial risk identification process. Such threats must continuously be entered into the R&OR, and measures need to be considered.
一、介绍
（一）关于本纲要
本课程纲要过程中研究的是“风险也是一种项目”。重点将是就以下议题：
?风险识别
?风险结构
?风险模型中光的时间表和成本模型
?风险跟进
我们也将讨论相关的决策分析，风险涉及的元素，并使用生命周期成本和生命周期的盈利模式来介绍风险管理的具体内容。
（二）定义
1.偶然的不确定性。我们有时会使用这个词表示数量的变化。
2.认知的不确定性，缺乏知识的“世界”，特别是观察到的数量。
3.依据，在一个项目中的活动的序列之间的关系。
4.观察到的数量，一定量的需要表达的状态，即对物理现实的数量或性质，是未知的时间分析，在未来，如果被分析的系统实施，可能成为众所周知的。
5.参数，我们使用的术语以两种方式在本报告的参数。参数的主要用途是，它是一个量是风险分析模型的一部分并且是我们指定数值。更多学术有关可观察到的数量。
6.参数估计，我们评估的一个参数的数值。
7.可能性，一种衡量事件的不确定性。
8.风险，风险是指三个问题：I）什么可以去做了吗？ II）项目的可能性有多大呢？，III）如果它出了问题，后果是什么？
9.风险接受，决定接受的风险。
10.风险接受准则，风险被评估为可接受或不可接受的参考。
11.时间表，该计划规定的活动时间在一个项目的开始和完成点。
12.随机依赖，两个或更多个随机变量（随机）依赖的期望的一个随机变量，如果依赖于其他的一个或多个随机变量的值。
二、风险管理
一般来说，风险管理（IEC 60300-3-9）定义为“系统应用程序的管理政策，程序和做法的分析，评估和控制风险”的任务。这将包括（IEC定义在括号中）：——风险分析，系统利用现有的信息，以识别危险，并估计个体或群体，财产或环
境的风险。
——风险评估，评估过程中，判断风险的耐受性是在风险分析的基础上，考虑的因素，如社会，经济和环境方面的。
——减少风险/控制（决策，执行和风险监控）。存在的风险，但并无统一的定义，例如IEC 60300-3-9风险定义为“相结合的频率或概率，发生的后果某一特定危险事件。大多数定义包括概率和后果的元素。雷恩提出一个很广泛的定义，他表示：“风险是指可能人的行动或事件导致的后果，影响人类重视的方面因此，总的风险包括（“全部”）不想要的/危险事件的可能性。这是界定哪些危害进行风险分析的一部分。此外，风险通常是指在未来的威胁，涉及的不确定性。在下面的建议是项目管理的一个组成部分，我们将介绍风险管理的基本要素。
（一）项目的目标和标准
在古典风险分析的工业系统使用所谓的风险接受准则发挥了核心的作用，在过去的两年或几十年。基本上使用的风险接受准则定义了一些严重的后果，例如死亡事故。然后，我们尝试设置一个上限可以接受这些后果的概率，即较高的概率在任何情况下，我们不能接受。此外，这些可能性只能接受降低风险是不可能的，或降低风险的成本是非常高的。
在最近几年中，它一直是讨论在风险分析的方面是富有成果的，或不根据上述原则，使用风险接受准则。有人认为，很多时候风险接受准则任意设定，而这些不一定是支持风险接受的最佳整体解决方案。因此，它可能是使用起来更加丰硕的成果。一些风险评估标准，而不是严格的验收标准。在项目风险管理中，我们可以建立两种类型的事件相关的验收标准：
?关系到健康，环境和安全的严重后果的事件。
?事件相关的项目成本，项目质量，工期，甚至终止项目的严重后果。在这个过程中，我们将主要集中在项目成本和项目的持续时间。需要注意的是这两个项目的成本和项目时间是随机变量，而不是事件。因此，它是无法确定验收标准的项目直接成本或持续时间。基本上，有三种类型的数值，我们可以引入在关系到项目的随机变量来描述：1．目标，在该项目的目标应是我们正在努力，应该是可以达到的目标。引入奖金或其他奖励以达到项目的目标是可能的。
2．期望，期望是随机变量，将实现从长远来看，我们期望的结果。预期是做出这样那样雄心勃勃的目标。预期将是克服危害的现实途径，威胁和经常作出贡献的事实，各项指标均满足的条件。
3．承诺，承诺有关的协议和合同监管的随机变量，这些变量的值。例如，它可以在合同中注明之内完成一个给定的日期，一种新的联系。如果我们不能够履行的承诺，这通常会导致经济后果，例如违约处罚，或在最坏的情况下取消该合同。
（二）风险识别
不期望的事件是可能发生的，例如大的水泄漏在隧道里。情景是一个想象的事件的序列或链的描述，如我们有一个漏水的，我们是不能够阻止这与普通紧缩可能对环境方面的不明确的时刻，由于介质泄漏。此外，绿色运动在这种情况下，也有可能进入现场。通常是一个危险的能量，有毒介质等，如果他们被释放，这将导致事故或严重的事件。威胁是一个更广泛的长期危险，我们还包括“错误”的方法应用的方面，“缺乏能力和经验”。长期威胁也很常使用的安全问题，例如：破坏，恐怖主义和破坏行为。（三）构建与风险建模
在（二）节中，我们已经确定的方法来识别事件和威胁。现在我们要与这些事件和威胁，我们有明确的模式，项目成本和项目工期。
1.模型项目执行时间/日程建模
当分析一个项目的执行时间，我们将有一个典型的项目计划和甘特图作为出发点。被变换成一个所谓的流动之间的活动连接的网络中明确描述的甘特图。这样的流网络还包括持续时间在概率语句方面的活动的描述。每一项活动的持续时间是随机的变量，我们表示在流动网络，我们可能也有不确定的活动，将只有在特殊情况下进行的。这些条件可以是描述的事件中，我们需要描述这样的事件的发生的概率。因此，有一组的数量，即模型中的时间变量和事件。现在，我们的目标是连接的非预期的事件和威胁，第二节中讨论的这些时间变量和事件。时间变量描述的概率分布函数。这样的分布函数，包括特征的时间变量的参数。通常情况下，一个参数的概率分布的三个量L（低），M（最有可能），H（高）。如果发生不期望的事件，这是有可能的L，M和H的值将高于该事件不发生的情况下的。然后甲的方法，包括从风险识别过程的结果是不同的L，M和H的值，取决于是否关键事件的发生或没有表达。如果我们除了能够评估的关键事件发生的概率，这一关键事件有关的知识已完全纳入风险模型。基于这样的直觉式建模的关键事件，我们也可以轻松地更新模型，在新的信息有关的严重事件的情况下获得的，例如新的信息可以在后一阶段的过程中的计划在新的信息仍然是可能的。
2.成本估算模型
成本模型通常是基于成本分解结构，成本要素将再次职能的劳动力成本，如加班成本，采购价格，租用设备的小时成本，材料成本，大量的材料等成本的概率模型通常更容易的为建模项目执行时间。其原理是，只是增添了不少的成本，其中各成本项是产品的单位价格和单位的数量。我们引入价格和交易量作为随机变量来描述单价和单位的数量。价格和交易量的变量应该还可以链接到非预期的事件和威胁，我们已经确定了。往往是必要的时间表模型连接的成本模型。它可能会对需要投入更多的努力来赶上进度的问题，这些努力是非常昂贵的项目。此外，如果该项目被延迟，我们可能需要付出额外
的费用，分包商到项目不得不推迟他们的支持。
3.进度和成本建模的不确定性
如上文所述，我们将建立概率模型来描述一个项目的时间和成本。这样的概率模型的结果是，我们把随机变量的时间和成本。由于时间和成本是随机变量，这意味着有不确定性的价值，他们将在真实的项目，我们正在评估。有时候，我们这种不确定性分割成三个不同的类，I），偶然的不明朗因素的变化，如天气条件下，劳资矛盾，故障的机器等），II）由于缺乏知识“true”参数值的参数或认知的不确定性，及III）模型的不确定性，由于缺乏详细的还是错的建模。在这样的思想，不能减少投机的不确定性，它被认为是在变化的世界，我们无法控制的结果。参数的不确定性，但是，相信通过收集更多的信息还原。在模型中的不确定性也被认为是由更详细的建模和分解到模型中去的各种要素。呼吁有一个心理模型的不确定性可以划分为一个部分，我们可能不会减少（变异），另一部分通过深入的分析和更多的调查增加知。如果我们能够证明，缺乏的认识和了解相关的不确定性的部分已经降低到足够的程度，我们就可以要求高可信度的分析。在某些情况下，所有者或当局提出要求这可被解释为有关的质量分析的信心。它虽然并不总是很清楚这样的信心水平是什么意思。我们也提到，即使最近提出质疑这种做法存在模型不确定性和参数的不确定性并强调，我们的分析应着眼于可观察到的数量，这将成为显而易见的，我们如果执行项目，例如成本，而且在这些数量的不确定性知识的缺乏，他们将在未来的值。
4.跟进的风险因素：风险和机会
由于风险要素和威胁，确定在二节中，这些必须要尽可能的控制。他们的以确定这些条件和模型来跟踪进度和成本模型，这是不够的，我们也有减轻的风险因素和威胁。为了确保系统跟进的风险要素和威胁，建议建立一个所谓的威胁日志。“风险登记册”和“风险与机遇注册“（R＆OR）有时被使用，而不是”威胁日志“。R＆OR是最好管理的数据库解决方案，例如MS-访问数据库。在数据库中的每一行代表一个风险因素或威胁。这样的数据库中的字段可能会有所不同，但以下领域似乎是合理的：?ID。需要一个标识符，以保持轨道的威胁有关的定量风险模型，后续行动等。
?描述。描述的威胁是必要的，以便理解的内容的问题。声明的直接后果（如职业事故），这可能是必要的但在该项目的主要目标的后果，例如：时间和成本。
?可能性或概率。一个判断，它是如何可能的威胁或风险状况将被释放的，例如不需要的或重要事件。
?影响。如果可能的话，得到直接的影响，比如该事件发生时的成本和进度，无论是由预期的影响或由L，M和H值。
?成本和进度的参考。为了更新的进度和成本模型，从R＆OR的进度和成本模型中，可以很方便地给一个明确的参考。
?可管理性。在这里，它是对如何描述威胁的，可以通过采取措施消除威胁，它揭示了它的自我，或采取措施，以减少所造成的后果威胁的情况下实现。
?报警信息。重要的是要知道的信息可能表明发展的威胁之前，它最终会怎样实现。如果这些信息是我们可以实现相应的措施，如果必要的话。例如，它可以是地面样品在一定的成本，但是从这些样品可以利用的信息，使我们能够选择适当的方法，隧道渗透。
?措施。名单可实施的措施，以减少风险。投标截止时间和负责。确定谁是负责实施和跟进的措施或威胁，任何最后期限。
?状态。两个方面的威胁的任何措施，它是有价值的指定开发，即“将揭示它自成非预期的事件，不必要的后果，根本的措施起到任何积极的作用等。
原文出处：
Join Vatn. Project Risk Analysis, Norwegian University of Science and Technology,2008.