In Great Britain, one of the key clients of large and complex projects is the Ministry of Defense (MoD). Most of the projects discussed (although not all) were performed by a contractor (who controls the resources to carry out the project) for a client or owner (who initiates the project, defines the requirements, secures the funding, and owns the final product). project and who implements the project). Still in the contract realm, you need to be aware of penalty clauses and liquidated damages (costs the contractor must pay if they fail to meet all project requirements, especially the due date).
The hierarchical structure of the WBS facilitates the hierarchical division of responsibility within the project. This is the basis of the US DoD methodology known as C/SCSC (or C/SPEC) (discussed further in Farld and Karshenas 1986). The answer to this question actually lies in the third point - the purpose of the model.
This means that the "language" of the model will need to be as consistent, unambiguous and precise as possible. Behavioral reproducibility: how well the model's behavior matches the behavior that can be observed in the real system. He emphasized the importance of the concept of complexity for the project manager and its role in the strategic management of projects.
The meaning of the words epistemic and aleatoric is by no means agreed upon in the literature. UK MoD (1992) top level guidance defines risk as "the product of the probability of an event occurring and the impact of that event. To calculate the preparation costs, the example stated that "The exact state of the ground.
Using simulation, we can evaluate the duration of the project and the time to important event milestones. On the other hand, it is a much more general definition and can be used to answer some of the questions posed in the previous paragraph. There is little in the literature on the analysis of performance risk (i.e. risk to achieving the total technical objective).
In this case, we are not only interested in the cost of the project over the construction phase, but also the (discounted) cost over the lifetime (or at least the cost over the fixed period).
Motivating the subject
If we consider the existence of mental probability concepts as read, a literature of formal methods for eliciting subjective probabilities has been built up to ensure consistency and uniformity: for a review, see Kahnemanet al. In certain risk analyses, where the number of probability distributions is quite small and the decisions to be made are very important, it is worth putting a lot of effort into getting an expert to specify one probability distribution (getting experts to specify or quantify risks are commonly known as "elicitation"). The best known method of doing this is the SRI coding process and this is described in Merkhofer's paper.
It should be noted that in project risk management it is rarely worthwhile to fully implement this methodology. Generally, we have too many distributions or probabilities to quantify and just enough time to get a good estimate. But the methodology is summarized here because it highlights the main biases that can occur in such a stimulus.
It is these biases that we need to be aware of when quantifying our models.
Structuring the variable
Conditioning the subject
Encoding the judgement
Verifying the result
Resolving expert differences
All of the above comments should, of course, be tempered by the general principles for causing uncertainty discussed in the previous section. We will look at some of the most important ones, taking in turn the three players mentioned above (customers, project managers and the project team). Examples of the far-reaching effects of scope changes on the projects listed in Chapter 2 are multiple, but I will cite just one here.
The effects of the changes brought about by these regulations were varied, but of particular importance were (a) the air conditioning system (HVAC) had to be redesigned and improved, and subsequent changes resulted in the redirection of the HVAC line and the adjustment of related services; and (b) the deck spaces (that is, the available space within the ceilings) had to be reduced. These are some of the main subjective effects that we need to be able to model within our project. So we now have, if not an exhaustive, at least a well-rounded picture of all the effects.
During the course of the project, additional requirements or additions to the scope of work are necessary (i.e. not anticipated or planned); For example, a causal map could be drawn of the effects described in this section, as shown in Figure 8.6. Then we can add the effect of using overtime: increased project complexity; the increased probability of downstream errors, and so on.
Approved” inventory is shown in Figure 9.8, which illustrates the combined effect of the delay variables. Third, in addition to group size itself, we need to consider changes within the group. It should be noted that the projects described in Chapter 2, both the Montreal 1976 Olympics and the wagons, depleted the number of relevant workers in the geographic region.
It is important to model the impact of all these changes on the behavior of the project. It is governed by simple logical rules, depending on the progress of the work compared to the original plan. Thus, the normal logarithmic learning curve, which is well established in industry, is interrupted, as part of the project has to be relearned.
Indeed, Abdel-Hamid and Madnick (1991) devote an entire chapter to the 90% syndrome in their book, citing Baber's (1982) description of the problem. But the management's decisions are absolutely fundamental to the project's behavior.
