Teams models

The quality of front-end work in answering the questions above, when addressed by a broadly comprised project team, is directly related to project success. It is useful to keep in mind that the consequences of decisions (question 4) are often different for the firm, for departments, for research groups, and for individuals on the research team. Models can reduce ambiguity and arbitrary discretion. This is appreciated in successful projects. 

Time and cost requirements for a FTA depend on the complexity of the process being analyzed and the level of resolution. With an experienced team, modelling a single top event involving a simple process could require one day or less. Complex processes or large systems with many potential accident events could require many weeks or months, even with an experienced analysis team. Table 4.26 presents estimates of the time needed to perform a PrHA using the FTA method. 

A round robin was organized wherein several teams modeled the fire spread before they were provided with the experimental results (a priori simulations) [101]. Participants were given basic information regarding the layout of the apartment and the types of combustibles present, but they were not provided with small-scale test data (i.e., Cone Calorimeter, thermogravimetric analysis, etc.) to characterize any of the combustibles present in the apartment. Most teams used FDS4, and two teams used CFAST. 

The SNUSES Teaming Model Has Been Very Effective... 

Kim, S., Choi, J. -H., Saple, A., and Yang, J. (2006) A hybrid gene team model and its application to genome analysis. J. Bioinform. Comput. Biol. 4, 171-196... 

Quality consists of two elements quality of conformance (have we met specifications ) and quality of design (are the specifications correct ). In the case of the behaviour-based approach, such specifications are expressed in terms of specific, observable and measurablebehaviours.Ratherthan being a somewhat abstract truism, quality is, in effect, defined for sub-units of the overall process in terms of specific units of behaviour. The previous use of this approach for safety improvement suggests that it would work equally well for quality of conformance. Quality of design, however, is dependent on increasing levels of inter-departmental communication and the establishment of an internal quality model. Therefore, the approach should facilitate both a team model and an internal customer approach to quality improvement. Before this structure is discussed in detail, it is important to consider the issue of gaining commitment to a quality improvement process. 

Mokrzycki MH, et al An interventional controlled trial comparing 2 management models for the treatment of tunneled cuffed catheter bacteremia a collaborative team model versus usual physician-managed care. Am J Kidney Dis 2006 48 587-595. 

Numerical simulations on the parison formation can minimize machine setup times and tooling costs. Several research teams modeled the parison formation stage to predict the parison dimensions [1-6]. The results showed that the finite-element-based numerical simulation method can predict the parison dimensions with certain precision. Huang et al. [7, 8] utilized the artifieial neural networks (ANN) method to predict the diameter and thickness swell of the parison and showed that the ANN method can predict the parison dimensions with a high degree of precision. However, the parison formation simulations and... 

Establishing the physical and analytical boundaries for a QRA is also a difficult task. Even though you will provide input, the scope definition will largely be made by the QRA project team. Defining the physical boundaries is relatively straightforward, but it does force the QRA team to explicitly identify and account for interfaces that may significantly affect the QRA results. Eor example, analysts often treat a connection to a power supply (e.g., a plug) or a feed source as a physical boundary yet, loss of power or contamination of the feed must be considered in the QRA model. 

The QRA project team can select the appropriate technical approach once you specify the study objectives, and together you can define the scope. A variety of modeling techniques and general data sources (discussed in Section 3.2) can be used to produce the desired results. Many computer programs are now available to aid in calculating risk estimates, and many automatically give more answers than you will need. The QRA team must take care to supply appropriate risk characteristics that satisfy your study objectives—and no more. 

You should consider obtaining internal and external quality assurance reviews of the study (to ferret out errors in modeling, data, etc.). Independent peer reviews of the QRA results can be helpful by presenting alternate viewpoints, and you should include outside experts (either consultants or personnel from another plant) on the QRA review panel. You should also set up a mechanism wherein disputes between QRA team members (e.g., technical arguments about safety issues) can be voiced and reconciled. All of these factors play an essential role in producing a defendable, high-quality QRA. Once the QRA is complete, you must formally document your response to the project team s final report and any recommendations it contains. 

Adequate support from the facility staff is absolutely essential. The facility staff must help the analysis team gather pertinent documents (e.g., PSilDs, procedures, software descriptions, material inventories, meteorological data, population data) and must describe current operating and maintenance practices. The facility staff must then critique the logic model(s) and calculation(s) to ensure that the assumptions are correct and that the results seem reasonable. The facility staff should also be involved in developing any recommendations to reduce risk so they will fully understand the rationale behind all proposed improvements and can help ensure that the proposed improvements are feasible. Table 12 summarizes the types of facility resources and personnel needed for a typical QRA. 

A multitude of analysis techniques and models have been developed to aid in performing these four steps (Figure 7). Many references exist for specific methods, and several recent publications give specific advice and how to details for the various techniques. You will not have to select specific techniques—your QRA team will do that. But you must appreciate the types of results available from each class of techniques. 

Frequency Phase 3 Use Branch Point Estimates to Develop a Ere-quency Estimate for the Accident Scenarios. The analysis team may choose to assign frequency values for initiating events and probability values for the branch points of the event trees without drawing fault tree models. These estimates are based on discussions with operating personnel, review of industrial equipment failure databases, and review of human reliability studies. This allows the team to provide initial estimates of scenario frequency and avoids the effort of the detailed analysis (Frequency Phase 4). In many cases, characterizing a few dominant accident scenarios in a layer of protection analysis will provide adequate frequency information. 

The comparison with experiment can be made at several levels. The first, and most common, is in the comparison of derived quantities that are not directly measurable, for example, a set of average crystal coordinates or a diffusion constant. A comparison at this level is convenient in that the quantities involved describe directly the structure and dynamics of the system. However, the obtainment of these quantities, from experiment and/or simulation, may require approximation and model-dependent data analysis. For example, to obtain experimentally a set of average crystallographic coordinates, a physical model to interpret an electron density map must be imposed. To avoid these problems the comparison can be made at the level of the measured quantities themselves, such as diffraction intensities or dynamic structure factors. A comparison at this level still involves some approximation. For example, background corrections have to made in the experimental data reduction. However, fewer approximations are necessary for the structure and dynamics of the sample itself, and comparison with experiment is normally more direct. This approach requires a little more work on the part of the computer simulation team, because methods for calculating experimental intensities from simulation configurations must be developed. The comparisons made here are of experimentally measurable quantities. 

The model will probably be introduced incrementally, perhaps into one team, and then over time it will spread to larger sections of the company... 

Jin, Y., Levitt, R. E., Christiansen, T. R. and Kunz, I. C. 1995 Modelling Organisational Behaviour of Concurrent Design Teams. Artificial Intelligence for Engineering Design Analysis and Manufacturing, 9(2), 145-158. 

FIAZOP is a formally struetured method of systematieally investigating eaeh element of a system for all ways where important parameters ean deviate from the intended design eonditions to ereate hazards and operability problems. The HAZOP problems are typieally determined by a study of the piping and instrument diagrams (or plant model) by a team of personnel who eritieally analyze eflfeets of potential problems arising in eaeh pipeline and eaeh vessel of the operation. 

Depending on the nature of the work you may require space models, prototypes, process capability studies, or samples of work as evidence of their capability. You may also make a preliminary visit to each potential bidder but would not send out an evaluation team until the qualification stage. 

This ranking exercise can be assigned to one or two team members as a subtask. Consider selecting a teammate with experience in facility operations to compile the necessary data and one with process safety and computer experience to run the models. The resulting report can then be shared with the full team and included in the plan you submit to your management. 

A key advantage of the business process redesign approach is that while it draws on past experience (in the form of the team s collective expertise) it is not bound by it. This helps minimize the risk that inadequate practices may become institutionalized through habit or neglect, and forces the team to take a fresh look at the critical processes under review. At the same time, this approach requires more concentrated effort than either TQM or model programs and may not be necessary in cases where incremental improvement is all that s required to address PSM gaps. 

What Is the Expected Process for Their Work. This requires a clear presentation of the approach (TQM, process redesign, model programs, other), which will to some extent dictate the working process. In addition, specifics about team leadership, the role (if any) of facilitators or advisors. 

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