Estimating Effort. 1. Preparing to Estimate. 2. Techniques for Estimating. 2.1 Developing an Estimate

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1 Crispin ( Kik ) Piney, B.Sc., PgMP Estimating Effort kik@project-benefits.com As explained in a companion posting on the need for a Performance Management Knowledge Area in the PMI standards, the estimation of effort is the starting-point for developing a detailed budget and schedule and for managing them effectively throughout the lifetime of the project. This paper explains the main processes directly involved: Preparing to estimate The estimation process for discrete effort Improving the estimate Adding the additional categories of effort Presenting the results 1. Preparing to Estimate Initial steps are required in order to develop the following: Scope of work defined to the level of detail currently understood Definition of categories of effort Agreement on level of accuracy required for the estimate o This may turn out to be incompatible with the previous information Historical information and tools e.g. o Data on previous, similar undertakings o Parametric estimating models o Competitor information including rival bids o External publications and standards List of specific risk events to be estimated separately List of experts to call on Once the rules have been agreed, they can be applied. 2. Techniques for Estimating The first step is to understand how to develop an estimate in general. Once the approach is known, a good rule for estimating is: divide and conquer as well as working by successive approximations. This can be achieved by splitting the types of effort into three categories, estimating within the main category, assessing the accuracy of the estimates, improving as necessary the model, reviewing the quality of the data, and applying risk management techniques. 2.1 Developing an Estimate Estimation obviously depends on a sound technical knowledge of the domain in what the estimate is required. However, there are some general guidelines that need to be applied in order to improve the quality of the estimates: The level of uncertainty needs to be made explicit: this implies that an estimate should provide much or all of the following information o The most likely value (m) o The smallest value expected (a) o The largest value expected (b) Several estimates should be obtained whenever possible. This allows you to evaluate the overall level of credibility of the estimates o If the ranges do not agree, some of the estimators, at least, are insufficiently experienced in that domain In most cases (i.e. where the distribution is not too asymmetrical), a good approximation of the value with a 50% confidence level is the PERT value : ( + +4 ) 6 Estimating effort 2011 Crispin ( Kik ) Piney Estimating Effort.docx

2 This PERT value is a better one to present (with the two extremes a and b) than the most likely, since its confidence level is known. This approach should then be applied to each category of effort as explained below. 2.2 Categories of effort There are three different categories of effort and they need to be evaluated differently. Discrete effort: Effort that can be planned and measured and that yields a specific output. Apportioned effort: Effort allotted proportionately across certain discrete efforts and not divisible into discrete efforts. Level of effort: A type of supporting activity that does not produce definitive end products and is characterized by a uniform rate of work performance over a period of time. In order to estimate the whole project, the effort for each category of resource, for each task needs to be estimated. In some cases, a task may include both discrete and level of effort types of resource. Apportioned effort is derivative from the effort analysis of the overall allotment calculated using the tools for estimating discrete efforts. Since most of the effort-related cost of a project is therefore dependent on discrete effort, this will be described first. 2.3 Estimating the discrete effort The basic approach is divide and conquer : estimate each component of the endeavour and then add them up. If this does not give the level of accuracy required, identify the component that is generating the greatest uncertainty, subdivide it, estimate and then sum the parts. For the very first estimate, the whole endeavour may initially be estimated as a single effort. Component estimation is always based to a considerable extent on experience: Analogy: find the effort required in a similar, situation and update it to allow for the differences Parametric: this similar to analogy, but where the main parameters affecting the estimate have been identified and a formula developed to calculate the effort The knowledge required for these estimates can come from: Experts Proprietary data Competitive analysis Standards Other professional publications A single value for each estimate is insufficient, since all of the stakeholders need to know the accuracy with which the numbers have been estimated. 2.4 Estimating the uncertainly Since estimating is a forecasting technique, it always contains a degree of uncertainty. Unless this margin for error is known, the estimate can be dangerously misleading as it can, for example, invalidate the business case on which the justification for the work is based, or lead to unrealistic time-estimates. The uncertainly depends on four main features: The granularity of the model The quality of the data The degree of understanding and objectivity of the estimators The degree of understanding and control over the risks 2.5 The granularity of the model Because of statistical effects, there is an optimal degree of decomposition of any entity for estimation. This is because the mean of the estimate is the sum of the component means, whereas the measure of Estimating effort 2 of 5

3 uncertainty (i.e. the standard deviation of the estimate) increases more slowly as it is the square root of the sum of the component standard deviations. This means that the ratio of the standard deviation to the mean (the variability ) decreases as a proportion of the total number, as the number of components increases. This is only valid insofar as the variability of the subcomponent estimates do not increase when a component is split which could occur due to other errors (e.g. limits of available precision). The rule of thumb is therefore: do not aim for more accuracy than a) you need and b) is reasonable to expect. This mathematical view is naturally only valid if the data is reliable. 2.6 The quality of the data There are a number of physical and psychological reasons that the data can be incorrect, and they all need to be investigated to ensure that the estimate will be credible. The reasons include Lack of knowledge Inability of measure more accurately Bias Fraud Lack of knowledge One useful way of determining the level of knowledge is to use a three-point estimate, by asking the estimator to provide: The most likely value The value in the most optimistic case The value in the worst possible case their level of confidence in the estimate and the assumptions or justification for the estimate. If you are able to obtain estimates from a number of different people, the three-point estimates can give an indication as to their credibility for example if the estimates differ so much that their ranges do not even overlap, it is clear that at least some of the estimates are less than credible. It is a fact that people are generally too confident in their estimates and propose a range that is much too narrow, even when it does span the correct value. The three-point approach can help to identify bias and fraud, so long as not every estimator is biased or fraudulent in the same way Measurement difficulty Measurement difficulties can arise when either the full scope of the work to be estimated is not very well defined, or when the measurement standard is imperfectly defined. The problem with the measurement standard can arise when the units are imprecise (e.g. man-days: what level of competence; CPU cycles: other factors can affect the processor efficiency) or the measurement tools are ill-adapted to the problem (e.g. measuring an irregular track with a straight ruler) Bias There are many reasons that can cause estimators to apply unconscious modifications to their estimates. These can come from anything from A desire to please or other external pressure Previous suggestions (e.g. it could be 100 man-days what do you think? ) Untypical experience Faulty or selective memory Fraud Fraud does not have to be motivated only by the desire for financial gain. It covers any conscious deviation from providing an estimate that the estimator considers to be the best they can honestly provide, whatever the reason. The reason can, for example, be to minimize a risk, to skew a business case, to overstate the effort in order to raise the price, or to provide padding as an unreasonable safety factor. Once these techniques have been applied, if the range of uncertainty is still too large, the estimate will need to be improved. Estimating effort 3 of 5

4 3. Improving the Estimate 3.1 Ways of improving the estimate There are basically two steps that can be applied to improve the estimate: Increase the granularity of one or more components Identify the reason for the size of the uncertainty range, by identifying the major risks that give rise to this uncertainty The first of these points has already been addressed (2.5 above). The second one is key to more than just good estimating, as it is the basis of good project management! As already implied, a good estimate has the following characteristics: It accurately gives the range of possible values The range of uncertainty is acceptable to the stakeholders Effective risk management adds another key characteristic: the principal factors affecting the value and therefore the possible spread of values are known. This is explained in more detail below. 3.2 Applying risk management to estimating There are two main ways in which uncertainty affects the size of the estimation range: discrete risks and overall risk. Discrete risks have a localized effect on the tasks the affect, whereas the overall risk arises from the interaction of a number of risks within the project. The standard risk management tools need to be applied in order to identify, assess, prioritize and address both types of risk. Once this has been completed, it is the residual risks i.e. those remaining after the definite action to treat the risks pre-emptively have been carried out that determine the amount of remaining uncertainty. Adding response actions tends to have two related effects: it raises the estimated most likely value for the amount of effort, but reduces the maximum limit of the range. Only once all of the tasks including those related to risk management have been completed, can the estimating be finalised as outlined below, by including apportioned effort and level of effort estimates. 4. Including Apportioned Effort and Level of Effort Discrete effort has been described in detail above. One this is known, it can be used in the evaluation of the duration of the corresponding tasks, based on assumptions about the resources that will be assigned. The addition of task dependencies then allows a schedule to be determined. It is interesting to note that, as will be shown below, only the discrete effort directly affects the project schedule, whereas all of the three types of effort affect the resource allocations and costs. Apportioned effort and level of effort therefore need to be included in the final estimate. 4.1 Apportioned effort In order to evaluate the amount apportioned to each discrete task, the apportionment rules need to be defined. These will naturally not affect the total amount of effort apportioned, but can affect the resultant costs, if the unit costs of the effort differ between the apportioned tasks. 4.2 Level of effort The value in this case is directly proportional to the duration of the level-of-effort task (e.g. project management, equipment rental, etc.). This means that the schedule of at least the relevant portion of the project has to be determined with sufficient accuracy as a prerequisite for calculating this value. All of this work than needs to be documented and presented in a way that is understandable and acceptable to the stakeholders. 5. Presenting an Estimate The general rules for a good explanation of an estimate are: Estimating effort 4 of 5

5 Make sure you do not set unrealistic expectations Use numbers intelligently Provide a structured view Use the audience s language 5.1 Setting expectations Make sure the audience understands that estimating includes assumptions and uncertainty, and that you will provide ranges of numbers rather than single, invariable numbers. Explain also that how these numbers should then be used in strategic decision making is outside your level of competency. 5.2 Intelligent numbers The precision of a number (i.e. the number of significant digits) should be compatible with the accuracy of the corresponding estimate, since, otherwise, the number provided contains digits which cannot be supported by the analysis. For example a value that is estimated as to +/- 10% should only give two significant digits as in 550,000 +/-10% and not 553,000 +/-10%). To put it in mathematical terms: if the uncertainty measured as a percentage is +/- U%, then the number of digits precision P is given by P= 3-log 10 (U) 5.3 Structured approach Because of the way in which the estimating process has been structured to arrive at this point, it is possible to present at least two sub-categories within the estimate: Estimates related to definite activities Estimates related to contingent activities or impacts Definite activities The range of uncertainty on these depends only on unknown risks or natural variation in the expected effort for the activity, and has been reduced as much as reasonable Contingent activities These are activities or impacts arising for known risks that may or may not occur. There are two levels of uncertainty in this case: uncertainty about whether or not the event will occur, and the uncertainty incorporated in the range of the estimate. This type of uncertainty can be considered as risk-related rather than related directly to imprecision in the estimating. Specific risk management techniques are required to deal with it, but will not be addressed in this posting. 5.4 The correct language Do not use terms such as discrete effort, unless they are in common usage in your organizations: use ones specific to the project and the context. In general, make sure you understand just what the audience requires as information from the report or presentation, and make sure you provide it in a way that is directly comprehensible and applicable for them. Make sure that both you and the audience understand that you are responsible for the accuracy of the analysis, but you are not responsible for the effect that the data might have on their hopes or expectations: the fact that the results may be unwelcome is not a reason for assuming they is wrong or that you or they are at fault. As always in projects, once you have made sure that you know the facts, the most important skill is to communicate effectively with your stakeholders. Estimating effort 5 of 5