Statistical Techniques for Project Control (Systems Innovation Book Series)


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In general, savings and improvements achieved from the VM process will justify the costs involved. The VM process includes pre-study activities, the VM study itself, and post-study activities.

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Pre-study activities, generally undertaken by the VM study leader, include:. The actual VM studies are conducted in the form of workshops. The specific objectives for each study are defined as an initial activity during the workshop. The participants include the study leader, representatives of the client organization, the designers this term is used generically , and representatives of the supply, installation and construction functions.

At the option of the client, personnel not connected with the design team may be present to contribute a view which is entirely independent of the solutions being reviewed. The process undertaken during the workshop requires that the VM study leader operates as a facilitator.

The process, which is both rational and creative, is structured to review operating criteria and assumptions, identify risks and opportunities, and create and analyze alternative solutions. The objective of this chapter is to set out a basis for risk management that will provide sufficient understanding of the process for implementing effective risk management for a specific project. All projects have associated risks. The extent to which risks exists for a particular project component determines how sensitive successful project outcomes are to that component.

Statistical Techniques for Project Control (Industrial Innovation) (Systems Innovation Book Series)

Effective project management requires that, if project outcomes are risk sensitive, relevant risks are properly managed. A detailed review of the analytical techniques necessary to undertake comprehensive quantitative analysis is outside the scope of this chapter. Risk is the effect of uncertainty that prejudices the successful achievement of the project outcome, by adversely impacting on cost, time, or functional objectives.

The benefits from applying risk management accrue to both the project team and to the project sponsor. These benefits include:. The cost of applying risk management to a project varies, as a function of the scope of the project and the depth to which the process is applied. Costs can be very low say, a few hours at one end of the scale, and ranging up to several percent of the total management costs if done in depth and as an ongoing process throughout all project phases.

Risk management is applicable to all projects. While this may be obvious, it is the widespread experience of project managers that it is difficult to convince clients to adopt comprehensive risk management processes. While it is clearly of great relevance on large or complex projects, it will provide benefits on all projects except on recurring projects being undertaken in an unchanging environment - if such a situation ever arises.

Risk management is a continuous process that can be initiated at any point in the project cycle, and continued until the costs of maintaining the process exceed the potential benefits. It has the greatest potential benefits if used early on in the project. The following points within a project should be specifically addressed within the risk management processes:. In order to maintain a record to facilitate ongoing reviews as well as an adequate audit trail, all components of the risk management process must be adequately documented.

This defines the external and internal parameters which need to be taken into account when managing risk, and setting the scope and risk criteria for the risk management policy. This analysis reviews the external environment in which the organization seeks to achieve its objectives The purpose is to identify factors that enhance or impair the ability of the organisation to manage risks.

This includes the financial, operational, competitive, political, legal, social, and cultural aspects of the operating environment.

Project Management for Research: A Guide for Graduate Students

This analysis is directed at the internal environment in which the organization seeks to achieve its objectives. This analysis is directed to the specific project objectives and its component technologies, activities, timing and physical environment. The organization needs to identify sources of risk, areas of impacts, events including changes in circumstances and their causes and their potential consequences. The purpose of this step is to identify all the risks, including those not under the control of the organisation, which may impact on the framework defined above.

A systematic process is essential because a risk not identified during this step is removed from further consideration.

Statistical Techniques for Project Control

The process of risk identification can be complex, and a planned approach is necessary to ensure that all sources of risk are identified. This process may involve:. The objectives of risk analysis are to comprehend the nature of risk and to determine the level of risk. This involves:. Risk is analysed by consideration of the likelihood and consequence of events occurring within the context of existing controls i.

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The analysis can be carried out to various levels of refinement by way of qualitative and quantitative analysis, or a hybrid of the two. It is necessary to avoid subjective biases when analysing the likelihood and consequences of individual risks. Once the risks associated with every area of the project have been identified, the impacts of these risks are assessed qualitatively. Where the impact that the risks within specific areas have on the overall project may be different, the resulting impacts need to be evaluated independently.

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Initial responses for the significant risks should be developed at this stage. If risks requiring immediate response are identified, then the initial response should be implemented. A proposed response to an initial risk may result in consequential risks not initially present. Secondary risks need to be included in the risk assessment process as they may dictate that a proposed response to a primary risk is not acceptable.

The advantage of using software is that it ensures that few questions are left un-asked, and also provides a database with all risks, their assessment and methods of addressing them that can be accessed by the entire project team. Interviews are conducted in general or specific project.

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Figure 4. Once the project-related risks have been identified, their chance of occurring and the related severity of such an occurrence have to be ascertained, together with the method and costs of addressing the issue. A quantitative risk analysis enables the impacts of the risks to be quantified with respect to the three fundamental project success criteria: time, cost and functionality. The techniques outlined below have been developed for analysing the effects of risks on the time and cost outcomes of projects, and are in common use. These techniques are well documented in the literature, and a detailed treatment is outside of the scope of this chapter.

These techniques are often not applicable to analysing risk impacts on functionality objectives. Generally considered to be the simplest analytical technique to apply, this analysis determines the effect on the desired dimension of the project - i. The resulting Sensitivity Diagrams - one for each project dimension modeled - identifies impact each variable has on project outcome, and what those impacts are. By inspection, the critical variables are apparent.

This provides the opportunity to develop a risk management strategy that targets the most critical risks. Probabilistic analysis is an analysis to identify the frequency distribution for a desired project outcome, e. The most common form of this analysis uses sampling techniques, normally referred to as Monte Carlo Simulation. This can only be practically undertaken using an appropriate software application package.

see url A mathematical model of the project is developed, incorporating all relevant variables. A probability distribution is then defined for each variable, and the project model is analyzed taking into account all risks in combination. This analysis is repeated a number of times, typically to passes, and at each pass the value for each variable is randomly calculated within the assigned probability distribution. The results from each analysis provide a distribution frequency of the project outcome. This establishes a mean outcome, and the range of outcomes possible. Probabilistic analysis can be performed on cost as well as project schedules.

One of the better-known software packages in this regard is RISK, although there are various alternatives on the market, some stand-alone and others as add-ons for scheduling packages such MS Project and Primavera. An example of an inexpensive software package for Monte Carlo analysis on project costs is Project Risk Analysis. The following figures show the statistical behavior of project costs for a given project see Figure 4. If the above bell curve distribution is integrated from left to right, it yields a so-called S curve that indicates the possibility that the cost will be less than a given value see Figure 4.

On less complex analyses this technique offers great precision for less computer effort. This method has been in use for a considerable time and provides for decision making based on a relatively crude risk assessment. Decision trees display the set of alternative values for each decision, and chance variable as branches coming out of each node.

This is a relatively new technique, used as an interface with computer based risk models to facilitate development of complex risk models see Figure 4. Decisions, shown as rectangles with sharp corners i. General variables not shown here appear as rectangles with rounded corners, and are deterministic functions of the quantities they depend on.

Arrows denote influence. An influence expresses knowledge about relevance and does not necessarily imply a causal relation, or a flow of material, data, or money. Influence diagrams show the dependencies among the variables more clearly than decision trees would. Although decision trees show more details of possible paths or scenarios as sequences of branches from left to right, all variables have to be shown as discrete alternatives, even if they are actually continuous.

In addition, the number of nodes in a decision tree increases exponentially with the number of decision and chance variables and, as a result, Figure 4. Risk evaluation is used to assist in making decisions based on the outcomes of risk analysis as to which risks need treatment and the priority for such treatment This involves comparing the levels of risks determined from the analysis process against the acceptance criteria previously established.

The assessment process will determine whether risks may be categorised as low or acceptable, or other. Low or acceptable risks may be accepted as they are, or with minimal further treatment, subject only to ongoing monitoring. Risk treatment involves identifying the range of options available for modifying those risks identified as requiring action in the previous stage, evaluating those options in respect of each risk, and developing and implementing risk treatment plans.

Note that some risk response activities may have been undertaken during the qualitative analysis step, if the urgency of developing a response to specific risks warranted it. Risk treatment options include the following. These options may not necessarily be mutually exclusive, or appropriate in all circumstances. There are two additional classifications for risk treatment responses, viz.

Options generated for risk treatment should be evaluated on the basis of the extent of risk reduction versus the costs of doing so, taking into account the risk assessment criteria previously developed. Clearly large reductions in risk where achieved for relatively low cost should be implemented.

Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)
Statistical Techniques for Project Control (Systems Innovation Book Series) Statistical Techniques for Project Control (Systems Innovation Book Series)

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