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### 163 Cards in this Set

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 RISK . . . . . . Project Risk Management . . The processes concerned with identifying, analyzing, and responding to project risk. (PMBOK) . . or . . The art and science of identifying, assessing and responding to project risk throughout the life of a project and in the best interests of its objectives. . . Uncertainty . . Lack of knowledge of future events . . Goals of PRM . . To identify project risks and develop strategies which either significantly reduce them or take steps to avoid them. . . Opportunity . . The probability those outcomes will be favorable. . . Risk . . The probability those outcomes will be unfavorable. . . Project Risk . . Is the cumulative effect of the chances of uncertain occurrences adversely affecting project objectives. . . Risk Factors . . 1.      Risk Event – Precisely what might happen to the detriment of the project . . 2.      Risk Probability – How likely the event is to occur . . 3.      Amount at Stake – The severity of the consequences . . Probability . . Probability = Frequency of relevant events . . . . Risk Event Status (criterion value or ranking) . . Risk Event status = risk probability x amount at stake . . Processes (11) . . 1.      Risk Identification . . Determining which risks are likely to affect the project and documenting the characteristics of each. Can be classified as: . . a.       Scope – Risk associated with changes of scope or the subsequent need for “fixes” to achieve the required technical deliverables. . . b.      Quality – Failure to complete tasks to the required level of technical or quality performance . . c.       Schedule – Failure to complete tasks within the estimated time limits, or risks associated with dependency network logic . . d.      Cost – Failure to complete tasks within the estimated budget allowances . . 2.      Risk Quantification . . Evaluating risks and risk interactions to assess the range of possible project outcomes. . . 3.      Risk Response Development . . Defining enhancement steps for opportunities and responses to threats. . . 4.      Risk Response Control . . Responding to changes in risk over the course of the project. . . Identification (11.1) . . 1.      Inputs . . a.      Product description . . Risk depends on the nature of the product. Proven technology has less risk than products requiring innovation and invention. . . b.      Other Planning outputs . . Review outputs from the processes for possible risks, e.g., WBS, cost estimates and schedule duration’s, staffing plan, procurement management plan. . . c.       Historical information . . 2.      Tools and Techniques . . a.      Checklists . . b.      Flowcharting . . Helps understand the cause and effects of risks. . . 3.      Outputs . . a.      Sources of Risks . . Includes such items as stakeholder actions, unreliable estimates, team turnover, changes in requirements, insufficiently skilled staff. . . b.      Potential Risk Events . . Precisely what might happen to the detriment of the project, such as natural disasters, requirement for development of new technology, etc. . . c.       Risk symptoms . . Sometimes called triggers, early warning of an impending event, etc. . . d.      Inputs to other processes . . Risks can be inputs to other processes as constraints or assumptions. . . Quantification (11.2) . . 1.      Inputs . . a.      Stakeholder risk tolerances . . Provides a screen for both inputs and outputs to risk quantification. . . b.      Sources of risk . . c.       Potential risk events . . d.      Cost Estimates . . e.       Activity duration estimates . . 2.      Tools and Techniques . . a.       Expected Monetary value . . This is the product of risk event probability of occurring times the risk event value (could be a gain or loss). . . b.      Statistical sums . . Calculates the range of alternative project budgets from the cost estimates for individual work items. . . c.       Simulation . . Most common is Monte Carlo analysis, which is used to analyze the behavior or performance of the system. The results of a schedule simulation may be used to quantify the risk of various schedule alternatives, different project strategies, and different paths through the network or individual activities. . . d.      Decision Tree . . e.       Expert Judgement . . 3.      Outputs . . a.      Opportunities to pursue, threats to respond to . . List opportunities that should be pursued and threats that require attention. . . b.      Opportunities to ignore, threats to accept . . Risk Response Development (11.3) . . Responses to threats fall into the following categories: . . ·        Avoidance . . Eliminating a specific threat, usually by eliminating the cause. The project management team can never eliminate all risk, but specific risk events can often be eliminated. . . ·        Mitigation . . Reducing the expected monetary value of a risk by reducing the probability of occurrence, reducing the risk event value. . . ·        Acceptance . . Accepting the consequences. Acceptance can be active (e.g. by developing a contingency plan to execute should the risk event occur) or passive (e.g., by accepting lower profit if some activities overrun). . . 1.      Inputs . . a.      Opportunities to pursue, threats to respond to . . List opportunities that should be pursued and threats that require attention. . . b.      Opportunities to ignore, threats to accept . . The duration of most activities will be significantly influenced by the resources assigned to them. . . 2.      Tools and Techniques . . a.       Procurement . . Acquiring outside sources for services and equipment is an appropriate tool. . . b.      Contingency Planning . . Making plans to take action if a risk arises. . . c.       Alternative Strategies . . Avoid risk by changing the planned approach. . . d.      Insurance . . 3.      Outputs . . a.      Risk Management Plan . . Documents the procedures to be used to manage risk throughout the project. It should cover who is responsible for managing various areas of risk, how the initial identification and quantification outputs will be maintained, how contingency plans will be implemented and how reserves will be allocated. . . b.      Inputs to other processes . . Contingencies and plans must be feed back to other processes. . . c.       Contingency plans . . d.      Reserves . . Examples management reserve, schedule reserve, and contingency reserve. . . e.       Contractual agreements . . May be entered into for insurance, services, and other items as appropriate in order to avoid or mitigate threats. Contractual terms and conditions will have a significant effect on the degree of risk deduction. . . Risk Response Control (11.4) . . 1.      Inputs . . a.      Risk management plan . . b.      Actual risk events . . When an event occurs, project management must recognize it so that the response plan can be implemented. . . c.       Additional Risk identification . . As performance is reported additional risks may surface and should be identified. . . 2.      Tools and Techniques . . a.      Workarounds . . Unplanned responses to negative risk events. . . b.      Additional risk response development . . If event was not anticipated or effect greater than expected, then it may be necessary to repeat the response development and quantification process. . . 3.      Outputs . . a.      Corrective Action . . Primarily the act of performing the planned risk response. . . b.      Updates to the risk management plan . . Risk Analysis Techniques . . 1.      Brainstorming . . Is used extensively in formative project planning and can also be used to advantage to identify and postulate risk scenarios for a particular project. It is a simple but effective attempt to help people think creatively in a group setting without feeling inhibited or being criticized by others. . . The rules are that each member must try to build on the ideas offered by preceding comments. No criticism or disapproving verbal or nonverbal behaviors are allowed. The intent is to encourage as many ideas as possible, which may in turn, trigger the ideas of others. . . 2.      Sensitivity Analysis . . Sensitivity analysis seeks to place a value on the effect of change of a single variable within a project by analyzing that effect on the project plan. It is the simplest form of risk analysis. Uncertainty and risk are reflected by defining a likely range of variation for each component of the original base case estimate. In practice such an analysis is only done for those variables which have a high impact on cost, time or economic return, and to which the project is most sensitive. . . Some of the advantages of sensitivity analysis include impressing management that there is a range of possible outcomes, decision making is more realistic, though perhaps more complex. And the relative importance of each variable examined is readily apparent. Some weaknesses are that variables are treated individually, limiting the extent to which combinations of variables can be assessed, and a sensitivity diagram gives no indication of anticipated probability of occurrence. . . 3.      Probability Analysis . . Probability analysis overcomes the limitations of sensitivity analysis by specifying a probability distribution for each variable, and then considering situations where any or all of these variables can be changed at the same time. Defining the probability of occurrence of any specific variable may be quite difficult, particularly as political or commercial environments can change quite rapidly. . . As with sensitivity analysis, the range of variation is subjective, but ranges for many time and cost elements of a project estimate should be skewed toward overrun, due to the natural optimism or omission of the estimator. . . 4.      Delphi Method . . The basic concept is to derive a consensus using a panel of experts to arrive at a convergent solution to a specific problem. This is particularly useful in arriving at probability assessments relating to future events where the risk impacts are large and critical. The first and vital step is to select a panel of individuals who have experience in the area at issue. For best results, the panel members should not know each other identity and the process should be conducted with each at separate locations. . . The responses, together with opinions and justifications, are evaluated and statistical feedback is furnished to each panel member in the next iteration. The process is continued until group responses converge to s specific solution. . . 5.      Monte Carlo . . The Monte Carlo method, simulation by means of random numbers, provides a powerful yet simple method of incorporating probabilistic data. Basic steps are: . . a.       Assess the range of the variables being considered and determine the probability distribution most suited to each. . . b.      For each variable within its specific range, select a value randomly chosen, taking account of the probability distribution for the occurrence of the variable. . . c.       Run a deterministic analysis using the combination of values selected for each one of the variables. . . d.      Repeat steps 2 and 3 a number of times to obtain the probability distribution of the result. Typically between 100 and 1000 iterations are required depending on the number of variables and the degree of confidence required. . . 6.      Decision Tree Analysis . . A feature of project work is that a number of options are typically available in the course of reaching the final results. An advantage of decision tree analysis is that it forces consideration of the probability of each outcome. Thus, the likelihood of failure is quantified and some value is place on each decision. This form of risk analysis is usually applied to cost and time considerations, both in choosing between different early investment decisions, and later in considering major changes with uncertain outcomes during project implementation. . . 7.      Utility Theory . . Utility theory endeavors to formalize management’s attitude towards risk, an approach that is appropriate to decision tree analysis for the calculation of expected values, and also for the assessment of results from sensitivity and probability analyses. However, in practical project work Utility Theory tends to be viewed as rather theoretical. . . 8.      Decision Theory . . Is a technique for assisting in reaching decisions under uncertainty and risk. All decisions are based to some extent on uncertain forecasts. Given the criteria selected by the decision-maker, Decision Theory points to the best possible course whether or not the forecasts are accurate. . . The Quality Risk . . This risk can best be expressed by the question: “What if the project fails to perform as expected during its operational life?” This may well be the result of less than satisfactory quality upon project completion, and is especially true if quality is not given due attention during the project life cycle. Since the in-service life of the resulting product is typically much longer than the period required to plan and produce that product, any quality shortcomings and their effects may surface over a prolonged period of time. . . Consequently, of all the project objectives, conformance to quality requirement is the one most remembered long after cost and schedule performance have faded into the past. It follows that quality management can have the most impact on the long-term actual or perceived success of the project. . . Risk Perceptions . . 1.      People do not, in fact, demand zero risk. They take risk every day, both consciously and subconsciously, and they are willing and able to take benefit/risk decisions, as in driving and speeding. . . 2.      Peoples’ judgment of degrees of risk is not, however, coincident with most methodologies for measuring risk statistically. The public may greatly underestimate familiar risks (e.g. driving) while greatly overestimating unfamiliar risks (e.g. buying a home near a nuclear facility). . . 3.      A variety of emotional, not logical, factors control risk perceptions: . . a.       Primary is the sense of personal control and the ability to mange the risk . . b.      Secondary are qualities of familiarity and conversely, dread. The greater the unfamiliarity and potential for connection to gruesome, the more it is likely to be judged as highly risky and therefore unacceptable. . . 4.      Once established, risk perceptions are extremely hard to change. New information may be absorbed by the intellect, but it is not readily absorbed at an emotional level. . . 5.      Risk perceptions reside fundamentally at an emotional level. . .