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1、河北科技师范学院2013届本科毕业论文（设计）外文翻译Project Risk AnalysisChapter 1 Introduction1.1 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 schedu

2、le and a cost model 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 g

3、et 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 DefinitionsAleatory uncertainty Variation of quantities in a population. We sometimes use the wor

4、d 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 quan

5、tity 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

6、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 probability1616statement about an observable quantity, X, is that a parameter is a construct where the value of the parameter is the limit

7、ing 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

8、 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

9、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.Sto

10、chastic 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

11、the performance of a system, and observable quantities in particular.Chapter 2Risk 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 compr

12、ise (IEC 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 t

13、he 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 “combinati

14、on of the frequency, or probability, of occurrence and the consequence of a specified hazardous events”. Most definitions comprise the elements of probabilities and consequences. However, some as Klinke and Renn suggest a very wide definition, stating: “Risk refers to the possibility that human acti

15、ons 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

16、 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 ha

17、s 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 acc

18、ept 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 crit

19、eria 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.

20、 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 projec

21、t. 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 ar

22、e three types of numeric values we could introducein 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

23、 (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 re

24、alistic 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 th

25、at 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 identificationA scenario is a description of a imagi

26、ned 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

27、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

28、 in connection with security problems, e.g. sabotage, terrorism, and vandalism.2.3 Structuring and modeling of riskIn 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 dur

29、ation.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 ar

30、e 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 stochasticVariables, which we denote Ti for activity in a flow network we might also have uncertain activities which will be carri

31、ed 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 t

32、hreats 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 quanti

33、ties 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

34、 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 easil

35、y 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 th

36、e 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

37、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 an

38、d 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 dela

39、yed 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 model

40、ing 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,

41、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

42、, 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

43、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

44、(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.

45、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 projec

46、t 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

47、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 cost

48、s, 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 Sect

49、ion 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 Registerand R