Project decisions

Historically, once a new enzyme was identified, it was often a laborious effort to identify the correct conditions for small scale fermentation in order to produce enough material for laboratory scale testing. This meant a major investment in resources and equipment simply to determine if the enzyme was suitable for further analysis. Pilot scale quantities required an even greater expenditure of effort. With a recombinant approach, it is possible to rather quickly clone and express an enzyme at high enough levels to enable applications scientists to test its utility. By moving this analysis earlier into the project, decisions with regard to which can dates should be pursued can be made more effectively in terms of resources spent. 

One of the main concerns of any environmental project is the collection of relevant and valid data. These are the data of the type, quantity, and quality that are appropriate and sufficient for the project decisions. The standards for data relevancy and validity stem from the intended use of the data since different uses require different type, quantity, and quality of data. For example, the data requirements for a risk assessment project are drastically different from those of a waste disposal project the requirements for site investigation data are different from these for site closure. 

Data relevancy and validity are two different concepts, a fact that is not always recognized by all project participants. Data can be perfectly valid, and yet irrelevant for their intended use. Conversely, the quality of data may be flawed in some way, as is usually the case in the real world, nevertheless they can be used for project decisions. 

Quality Assessment. Data validity is established through the application of data evaluation procedures their relevancy for making project decisions is determined in the course of the data quality assessment (DQA) process. 

Total error can undoubtedly affect the outcome of all four questions. Only after we have established that the collected data are appropriate for the intended use, are representative of the sampled matrix, conform to appropriate standards, and are technically and legally defensible, the data can be described as data of known quality. These are the data can be used for project decisions. 

To facilitate systematic planning, EPA developed the DQO process, a seven-step planning approach for data collection designs, which enables us to collect relevant and valid data for project decision-making. 

The decision rule assumes that perfect information has been obtained from an unlimited number of samples and that the sample mean concentration (x) is equal to the true mean concentration (p). (The definitions of sample mean and true mean concentrations can be found in Appendix 1). The reality is that we never have perfect information and unlimited data, and that is why this decision rule is only a theoretical one. In fact, environmental project decisions are made on data that are obtained from... 

A practical way to calculate completeness is on an individual method basis. Individual method completeness is the number of valid measurements as a percentage of the total number of measurements in one analytical method. A single parameter analysis may be invalid for all samples, but it will not have much effect on the completeness calculation if merged with a large total analyte number. This one method may, however, be of critical importance for the project decisions. Calculating method analytical completeness enables us to determine whether any of the performed analytical methods fail to provide a sufficient quantity of valid data and, consequently, whether resampling and reanalysis may be needed. 

The collection of field QA/QC samples prescribed by the EPA and DOD guidance documents is part of every sampling event. Typically, QA/QC samples are collected to satisfy the protocol requirements and the obtained data are rarely used for project decisions. And yet, field QA/QC samples, like all other samples collected for the project, must have a well-defined need, use, and purpose and be relevant to the project objectives. 

An accepted standard for the frequency of MS/MSD analysis is 5 percent or one MS/MSD pair for every 20 field samples. This frequency may be insufficient for some projects and excessive for others. Similar to other field QC samples, MS/MSDs should be collected and analyzed only if their data will be used for project decisions. 

Plan on project-specific MS/MSD analysis only if the obtained data will be used for project decisions. 

Procedures for the calibration of field instruments and inspection of field supplies to determine whether they meet the project needs are important factors in the implementation of field activities. Element B9, cryptically named by the EPA Non-Direct Measurements is applicable when previously collected data are to be used for project decisions. Data management, which consists of field and laboratory data recording, transmittal, tracking, storage, and retrieval, is described in element BIO. 

At the top of the data collection pyramid shown in Figure 5.1 is assessment. By the time the data collection process enters assessment, its third and final phase, the provisions made in the planning phase have been already implemented in the field and at the laboratory as the requirements for sampling, analysis, and QA/QC activities. In the assessment phase, by conducting Task 6—Data Evaluation and Task 7—Data Quality Assessment, we will establish whether the collected data are valid, relevant, and may be used for project decisions. 

The answer to the first question will establish the appropriateness of the collected data type and quantity or data relevancy, whereas the remaining three answers will establish the data quality or data validity. If the answers to all four questions are positive, the data may be confidently used for project decisions. A negative answer to any of them will reduce the level of confidence with which the data may be used or even make a data set unusable. 

Data qualified as estimated values are used in the same manner as valid, unqualified values for making project decisions, in completeness calculations, and in statistical calculations. After all, all environmental data are only the approximations of true contaminant concentrations. 

Unqualified data and estimated values are considered valid and can be used for project decisions, whereas the data points, which were rejected due to serious deficiencies in representativeness, accuracy, or precision, cannot. In an attempt to obtain data of better quality, the chemist may ask the laboratory to reanalyze some of the samples or extracts, if they are still available and have not exceeded the holding time. Depending on the number of the rejected data points and their importance for project decision, the chemist may recommend resampling and reanalysis. 

A complete knowledge of the data quality that arises only from Level 4 validation enables the data user to make project decisions with the highest level of confidence in the data quality. That is why Level 4 validation is usually conducted for the data collected to support decisions related to human health. Level 4 validation allows the reconstruction of the entire laboratory data acquisition process. It exposes errors that cannot be detected during Level 3 validation, the most critical of which are data interpretation errors and data management errors, such as incorrect computer algorithms. 

The sample data are never qualified based on field duplicate precision, and the information gained from the evaluation of soil duplicates usually does not have any effect on project decision-making. The only situation, when these data may... 

The reconciliation of data with the DQOs is necessary because data validity does not assure data relevancy and usability. Regardless of how excellent the quality of a data set may be, if the data are not relevant, they cannot be used for project decisions. 

Making a distinction between the method target analytes and the project contaminants of concern will enable the team to determine the completeness of relevant data. Only the data for the contaminants of concern are important for project decisions. For some projects, the contaminants of concern may not be known, and the method target analytes then become the contaminants of concern. For others, a subset of target analytes may represent the contaminants of concern, and only these are relevant for the project decisions. A data set may have inadequate completeness, with many target analytes rejected, but are they the contaminants of concern If not, this is of no consequence for the project, as the contaminant of concern data or the relevant data are valid and complete. 

Sometimes even valid and relevant data cannot be used for project decisions, particularly for projects that depend on a comparison of sample results to action levels. This may happen when samples are diluted during analysis. Although laboratory PQLs may be below the action levels, the RLs for the contaminants of concern in samples may be elevated due to dilutions and exceed the action levels. Example 5.11 demonstrates how the data may become unusable due to sample dilutions. 

Conversely, on occasion, invalid data may still be used for project decisions. In DQA Step 4, the chemist made decisions related to the effects of trip and equipment blank contamination on sample data by applying the logic demonstrated in Example 5.8. In DQA Step 6, this decision logic will be extended further to compare the contaminants of concern concentrations in the samples to the action levels as demonstrated in Example 5.12 on the next page. 

The results for first two compounds cannot be used for project decisions. Due to sample dilution, the RLs for these compounds exceed the action levels. These data cannot be meaningfully compared to the action level. The third compound illustrates how the same incident may be of no consequences, as the RL for methylene chloride is orders of magnitude below the action level. 

If the concentrations of contaminants of concern in the trip and equipment blanks and the samples are significantly lower than the action levels (at least by a factor of 10), then the sample data, although technically invalid, may still be used for project decisions. 

This example illustrates the profound effect the selection of the confidence level has on project decisions. 

Cohen DJ. 2001. The Project Manager s MBA How to Translate Project Decisions into Business Success, 1st ed. San Francisco Jossey-Bass. 

Remarks Other information related to the review (project decisions, related data, pending studies, etc.). 

This is a place to point out two mistakes that are easily made in project decisions. Suppose the project has run according to your model through the fourth quarter (Figure 16-13). You have then made the expenses in the first four columns. When people see these figures you can expect two kinds of reaction, both irrational ... 

Major decisions can and must be anticipated and prepared by both the decision makers and by those who s further work depends on the decision. The major decisions for a development project, as proposed in Table 4, should be integrated in the project plan along with the necessary tasks for their preparation. Management should provide guidance on the information needed for such decisions. Most larger companies have established routine schemes for the major project decisions and for the necessary information and prerequisites. If applied pragmatically, such schemes can shorten decision procedures considerably. 

Project decisions tend to take a long time, and even projects which eventually finish up successfully are sometimes subject to several critical decisions during their life-span. In these times the individuals in the project group may frequently ask themselves, whether their efforts are worth it. The rule should be that as long as there is no official decision to discontinue a project, everyone should continue unharmed by the ongoing discussion. 

Process design is the first action step in the execution of a project. Process design packages are normally prepared to serve as the starting point for more advanced design stages as well as the basis for the various types of cost estimates required to make progressive project decisions. For example ... 

Ascertain that all important project decisions are formally documented through minutes of meetings, memos and/or formal change notices. 

An agreed-upon condition on the completion date is an important project decision that should be made prior to award of construction contracts and should be made a written part of them. In the past, this was called mechanical completion, which was generally meant that all equipment was tested and ran in a mechanically approved manner and proper tests had been conducted to confirm tightness and pressure rating of system. Only necessary material for testing the system would be introduced. Today the requirements for cleanliness and proofing of tests as part of validation require that the definition of condition for turnover to start-up must be developed in much more detail. The questions to be asked when developing the completion plan are ... 

In Phase I of this project, decision models for selecting a feedstock, a process, and alternatives were developed, and a 7 kg/hr fluidized-bed catalytic steam reformer system was designed and constructed. In Phase 2, the emphasis is on integrating and testing the pilot-scale pyrolyzer at... 

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