El fenotipo asociado a la expresión de TIA1 y TIAR es reversible

Parametric Estimating

Parametric estimating is also called model estimating. Parametric estimating depends on cost history and an estimate of similarity between that project history available to the model and the project being

estimated. Parametric estimating is employed widely in many industries, and industry-specific models are well published and supported by the experiences of many practitioners. ^[12] The software industry is a case in point with several models in wide use. So also do the general industry that builds hardware, as well as the construction industry, environmental industry, pharmaceuticals, and many others have many good models in place. The general characteristics of some of these models are given in Table 3-7.

Standard deviation, σ = $486 Confidence calculations:

Total standard deviation of WBS = √1, 653,124 = $1,286 50% confidence: WBS total ≤ $32,591 ^[*]

68% confidence: WBS total ≤ $32,591 + $1,286 = $33,877

[*]Assumes approximately Normal distribution of WBS summation with mean = $32,594 and σ =

$1,286. Construction PACES 2001 Covers new construction, renovation,

and alteration

Covers buildings, site work, area work Regression model based on cost history in military construction

Input parameters (abridged list): size, building type, foundation type, exterior closure type, roofing type, number of floors, functional and utility space

Environmental RACER Handles natural attenuation, free product removal, passive water

Ability to use either system costs or user-defined costs

Professional labor template that creates task percentage template

Hardware Price H® Key parameters: weight, size, and manufacturing complexity

Six knowledge bases support the WBS elements: application, platform,

optional description, acquisition category, standards, class Cost estimates are produced for development and production cost activities (18) and labor categories (14), as well as "other" categories (4)

Production cost Labor rate inputs, overhead, and G&A costs

Software COCOMO 81 Development environment: detached, Effort and duration in

Most parametric models are "regression models." We will discuss regression analysis in Chapter 8.

Regression models require data sets from past performance in order that a regression formula can be derived. The regression formula is used to predict or forecast future performance. Thus, to employ parametric models they first must be calibrated with cost history. Calibration requires some

standardization of the definition of deliverable items and item attributes. A checklist specific to the model or to the technology or process being modeled is a good device for obtaining consistent and complete history records. For instance, to use a software model, the definition of a line of code is needed, and the attributes of complexity or difficulty require definitions. In publications, the page size and composition require definition, as well as the type of original material that is to be received and published. Typically, more than ten projects are needed to obtain good calibration, but the requirements of cost history are model specific.

Once a calibrated model is in hand, to obtain estimates of deliverable costs the model is fed with parameter data of the project being estimated. Model parameters are also set or adjusted to account for similarity or dissimilarity between the project being estimated and the project history. Parameter data

(waterfall source lines of code, product attributes, computer attributes, personnel of code, function points, COCOMO 81 parameters (with some modification), productivity rating (Stage 1)

Effort and duration in staff hours or months Other parametric reports

Price S Nine categories for attributes: project magnitude, program application,

SEER-SEM Three categories for attributes: size, knowledge base, input

Input is further subdivided into 15 parameter types very similar to the other models discussed

Effort and duration in staff hours or months Other parametric reports

could be the estimated number of lines of software code to be written and their appropriate attributes, such as degree of difficulty or complexity. Usually, a methodology is incorporated into the model. That is to say, if the methodology for developing software involves requirements development, prototyping, code and unit test, and system tests, then the model takes this methodology into account. Some models also allow for specification of risk factors as well as the severity of those risks.

Outcomes of the model are applied directly to the deliverables on the WBS. At this point, outcomes are no different than bottom-up estimates. Ordinarily, these outcomes are expected values since the model will have taken into account the risk factors and methodology to arrive at a statistically useful result. The model may or may not provide other statistical information, such as the variance, standard deviation, or information about any distributions employed. If only the expected value is provided, then the project manager must decide whether to use some independent evaluation to develop statistics that can be used to develop confidence intervals. The model outcome may also specify or identify dependencies

accounted for in the result; as we saw in the discussion of covariance, dependencies change the risk factors.

Table 3-8 provides a numerical example of parametric estimating practices in the WBS.

Table 3-8: Parametric Estimating

WBS

Element Deliverable Units Quantity

Parametric

Average deliverable from model = $1,465,000/7 = $209,286 Variance, σ² = 1,822,250,000/7 = 260,321,429

Standard deviation, σ = √260.321 ,429 = $16,134 Confidence calculations:

[12]A current listing of some of the prominent sources of information about parametric estimating can be found in "Appendix E, Listing of WEB Sites for Professional Societies, Educational Institutions, and Supplementary Information," of the Joint Industry/Government "Parametric Estimating Handbook,"

Second Edition, 1999, sponsored by the Department of Defense. Among the listings found in Appendix E are those for the American Society of Professional Estimators, International Society of Parametric Analysis, and the Society of Cost Estimating and Analysis.

Estimating "Completion" versus "Level of Effort"

In almost every project there are some WBS elements for which the tasks and activities of a deliverable cannot be scoped with certainty. Estimates for cost accounts of this type are called "level of effort."

Level of effort describes a concept of indefinite scope and application of "best effort" by the provider.

How then to contain the risk of the estimate? We call on three-point estimates, a sober look at the most pessimistic possibilities, and the use of statistical estimates to get a handle on the range of outcomes.

Completion estimates are definitive in scope, though perhaps uncertain in total cost or schedule. After all, even with a specific scope there are risks. Nevertheless, completion estimates have a specific inclusion of tasks, a most likely estimate of resource requirement, and an expectation of a specific and measurable deliverable at the end. In the examples presented in this chapter, the underlying concept is completion.

Let us consider a couple of examples where level of effort is appropriate. Project management itself is usually assigned to a WBS cost account just for management tasks. Although there are tangible outcomes of project management, like plans, schedules, and budgets, the fact is that the only real evidence of successful completion of the cost account is successful completion of the project. Work packages and cost accounts of this type are usually estimated from parametric models and "similar-to"

estimates, but the total scope is indefinite.

Research and development efforts for "new to the world" discoveries are an obvious case for level of effort, particularly where the root problem is vague or unknown or where the final outcome is itself an

"ah-hah!" and not specifically known in advance. In projects of this type, it is appropriate to base

funding on an allocation of total resources available to the business, set somewhat short-term milestones for intermediate results, and base the WBS on level of effort tasks. If prior experience can be used to establish parameters to guide the estimating, then that is all to the advantage of the project sponsor and the project manager.

Standard deviation of total expected value = √(1 ,822,250,000) = $42,687 50% confidence: WBS total ≤ $1 ,465,000 ^[*]

68% confidence: WBS total ≤ $1,465,000 + $42,687 = $1,507,687

[*]Assumes approximately Normal distribution of WBS summation with mean = $1 ,465,000 and σ =

$42,687.

Summary of Important Points

Table 3-9 provides the highlights of this chapter.

Table 3-9: Summary of Important Points Point of Discussion Summary of Ideas Presented

Work definition and scoping

l Organizing and defining the project scope of work is a required prerequisite to all analytical estimates concerning projects.

l The WBS serves two purposes: (1) organizing the work and (2) providing data on the project deliverables.

l The WBS is not a project organization chart or a project schedule.

l The WBS may support more than one view of the project: sponsor's view, developer's view, operations and maintenance view.

l Most often, the WBS is a hierarchical structure of the project deliverables.

The OBS and RAM ^l The people who work on projects may be assigned exclusively to the project or participate in the project and a "home" organization. The

"home" organization breakdown structure is called the OBS.

l The OBS maps to the WBS by means of the RAM.

Work packages and cost accounts

l The work package is the lowest level of work identified with cost on the WBS.

l The cost account is a summation of subordinate work packages.

Chart of accounts l The chart of accounts is a WBS of the business organized by financial accounts like expense accounts, capital accounts, and equity accounts.

l The WBS is typically an extension of the chart of accounts structure, touching expense and capital accounts most often.

WBS dictionary l The WBS dictionary of a set of definitions of the work packages and cost accounts on the WBS.

WBS baseline l The WBS baseline is the scope and cost decided on at the outset of the contract. The baseline is managed as a fixed set of numbers until changed through a change management process.

Project estimates l The objectives of performing an estimate are twofold: to arrive at an expected value for the item being estimated and to be able to convey a figure of merit for that estimate.

Top-down estimates l Top-down value judgments from the business side of the project balance sheet conveyed to the project team.

References

1. Editor, MIL-HDBK-881, OUSD(A&T)API/PM, U.S. Department of Defense, Washington, D.C., 1998, paragraph 1.4.2.

2. Editor, MIL-HDBK-881, OUSD(A&T)API/PM, U.S. Department of Defense, Washington, D.C., 1998, paragraph 1.6.3.

3. Haugan, Gregory T., Effective Work Breakdown Structures, Management Concepts, Vienna, VA, 2001, chap. 1, pp. 7–13.

4. A Guide to the Project Management Body of Knowledge (PMBOK® Guide) — 2000 Edition, Project Management Institute, Newtown Square, PA, chap. 5.

5. A Guide to the Project Management Body of Knowledge (PMBOK® Guide) — 2000 Edition, Project Management Institute, Newtown Square, PA, chap. 2.

Similar-to estimates l Similar-to judgments from either side of the project balance sheet, but most often from the other business side.

Bottom-up estimates l Bottom-up facts-driven estimates of actual work effort from the project side of the project balance sheet conveyed to the project sponsor.

Parametric estimates ^l Parametric calculations from a cost-history model, developed by the project team, and conveyed to the project sponsor.

Confidence intervals in estimates

l Every estimate should include some assessment of the risk of completing the project for the estimated figures.

l The Normal distribution is invoked by means of the Central Limit Theorem to describe the project outcome distribution probabilities.

l Cumulative probabilities from statistical distributions provide the data to assess the confidence of meeting the project outcome.

l Confidence intervals can be estimated for any WBS regardless of the estimating methodology.

Completion and level of effort estimates

l Level of effort is appropriate where the scope is indefinite.

l Completion should be used whenever it is possible to make the scope definitive.